Patent Application
20220410360
The invention relates to a method for operating a handheld power tool, and to a handheld power tool designed to execute the method. In particular, the present invention relates to a method for screwing in or unscrewing a threaded means using a handheld power tool.
Rotary impact drivers for tightening screw elements, for example threaded nuts and screws, are known from the prior art; see for example EP 3 202 537 A1. A rotary impact driver of this type comprises for example a structure in which an impact force is transmitted to a screw element in a direction of rotation by a rotary impact force of a hammer. The rotary impact driver which has this structure comprises a motor, a hammer to be driven by the motor, an anvil, which is struck by the hammer, and a tool. The rotary impact driver furthermore comprises a position sensor, which senses a position of the motor, and a controller, which is coupled to the position sensor. The controller detects an impact of the impact mechanism, calculates a drive angle of the anvil, which is caused by the impact, on the basis of the output from the position sensor, and controls the brushless DC motor on the basis of the drive angle.
U.S. Pat. No. 9,744,658 also discloses an electrically driven tool having an impact mechanism, wherein the hammer is driven by the motor. The rotary impact driver furthermore comprises a method for recording and reproducing a motor parameter.
When using rotary impact drivers, a user needs to pay close attention to the work status in order to react appropriately to a change in particular machine characteristics, for example the starting or stopping of the impact mechanism, for instance to stop the electric motor and/or to change the speed via a manual switch. Since the user often cannot react quickly enough or appropriately to a work status, it is possible, when using rotary impact drivers for screwing-in operations, for screws to be overtightened, for example, and, for unscrewing operations, for screws to drop down if they are unscrewed too fast.
It is therefore generally desired for operation to be automated further and for the customer to be assisted to achieve a fully completed work status more easily and for reliably reproducible, high-quality screwing-in and unscrewing operations to be ensured. The user should furthermore be supported by reactions or routines, appropriate to the work status and initiated by the machine, of the device. Examples of such reactions or routines initiated by the machine comprise for instance switching off the motor, changing the motor speed, or sending a notification to the user.
Such smart tool functions can be provided, inter alia, by identification of the current operating state. This is identified in the prior art, independently of the determination of a work status or the status of an application, for example by monitoring the operating variables of the electric motor, for instance the speed and electric motor current. Here, the operating variables are investigated to determine whether particular limit values and/or threshold values have been reached. Corresponding evaluation methods work with absolute threshold values and/or signal gradients.
A drawback here is that a fixed limit value and/or threshold value can be perfectly set in practice only for one application. As soon as the application changes, the associated current and speed values and the temporal progressions thereof change, and impact ascertainment on the basis of the set limit value and/or threshold value and the temporal progressions thereof no longer functions.
Thus, it is possible for, for example, an automatic switch-off, based on the ascertainment of impact operation, to switch off reliably in different speed ranges in some applications when self-tapping screws are used, but for no switch-off to occur in other applications when self-tapping screws are used.
In other methods for determining operating modes in rotary impact drivers, additional sensors, for instance acceleration sensors, are used in order to infer the current operating mode from vibrational states of the tool.
Drawbacks of these methods are additional costs for the sensors and losses in the robustness of the handheld power tool since the number of installed components and electrical connections increases compared with handheld power tools without these sensors.
Furthermore, simply having information as to whether the impact mechanism is working or not is often insufficient in order for it to be possible to draw accurate conclusions about the work status. Thus, for example, when screwing in particular wood screws, the rotary impact mechanism already starts very early, while the screw has not yet been fully screwed into the material, but the demanded torque is already exceeding what is known as the disengagement torque of the rotary impact mechanism. A reaction purely on the basis of the operating state (impact operation and no impact operation) of the rotary impact mechanism is therefore insufficient for a correct automatic system function of the tool, for example switching off.
In principle, the problem exists of largely automating operation in other handheld power tools, too, for instance impact drills, and so the invention is not limited to rotary impact drivers.
The object of the invention is to specify an improved method, compared with the prior art, for operating a handheld power tool, said method at least partially eliminating the abovementioned drawbacks, or at least to specify an alternative to the prior art. A further object is to specify a corresponding handheld power tool.
These objects are achieved by means of the respective subjects of the independent claims. Advantageous configurations of the invention are the subject of respective dependent claims.
According to the invention, a method for operating a handheld power tool is disclosed, wherein the handheld power tool has an electric motor. Here, the method comprises the steps of:
The determining of the signal of the operating variable in this case also comprises possible signal processing of a measured signal, for example in the sense of a classification or clustering of a measured signal.
By way of the method according to the invention, a user of the handheld power tool is assisted effectively in achieving reproducible high-quality application results, wherein the method is characterized by the determining of the application class by way of a high degree of automation, which does away with the preselection, conventional in previous methods, of a particular application. As a result, user errors, occurring everywhere, in the selection of particular machine programs are also avoided.
The concept of application class will be substantiated briefly using an example. In the assembly of furniture, for instance kitchen furniture, a large number of screws of different types need to be screwed into different support material. For instance, the hinges need to be fastened with the aid of small wood screws, and in addition, the cabinets themselves need to be fastened to the wall with wall plugs and larger screws. According to the present invention, for the screwing cases that occur, one of the application classes “small wood screws” and “large wall plug screws” would be thus determined from the signals, associated therewith, of the operating variables. In the context of the method according to the invention, on this basis, a work status is subsequently ascertained, which can in turn serve as a trigger for the automated execution of particular routines or reactions of the handheld power tool.
By way of the invention, it is thus possible to provide the user with assistance with which consistent work quality is possible with as little effort as possible.
A person skilled in the art will recognize that the feature of the model signal shape includes a signal shape of continuous progress of a work operation. In one embodiment, the model signal shape is a state-typical model signal shape, which is state-typical for a particular work status of the handheld power tool. Examples of such work statuses comprise the contact of a screw head with a fastening substrate, the free rotation of a loosened screw, the starting or stopping of a rotary impact mechanism of the handheld power tool, the reaching of a determined screw-in depth of a fastener to be screwed in with the handheld power tool and/or an impact of the rotary impact mechanism without onward rotation of the struck element or of the tool receptacle.
The approach for ascertaining the work status via operating variables in the tool-internal measurement variables, for example the speed of the electric motor, proves to be particularly advantageous since, with this method, the work status takes place particularly reliably and largely independently of the general operating state of the tool or the application thereof.
In this case, the use of, in particular additional, sensor units for capturing the tool-internal measurement variables, for example an acceleration sensor unit, is substantially dispensed with, and so essentially only the method according to the invention serves for ascertaining the work status.
In some embodiments of the invention, the method furthermore comprises the method step of:
wherein the determining of the application classes in step S2 and the provision of the model signal shape and of the threshold value of the match in step S3 takes place at least partially on the basis of application classes generated in the machine learning phase and of threshold values of the match and/or model signal shapes associated with the application classes.
The concept of the machine learning phase comprises, in particular embodiments of the invention, that the impact driver, for example with the aid of an impact quality assessment, which will be explained further below, saves different curves of operating variables for applications carried out by the user or exemplary applications. Furthermore, the impact driver automatically saves the threshold value of the match at which the user reacts in the individual curves, for example by reducing the speed or switching off the machine. With a sufficiently large data basis, the impact driver can now use data analysis methods to establish a correlation between application curves of the same type and the threshold values of the match. The impact driver is thus automatically capable of classifying application curves and assigning a specific threshold value of the match to the classes.
In the above example of the construction of kitchen furniture, the impact driver according to the invention that is used by the user therefore learns over time and with a sufficiently large amount of data whether the “wood screwing” or “wall plug screwing” case of screwing exists, and when a respectively defined work status has been reached.
In other embodiments, the reading of at least one exemplary application into a memory connected to the handheld power tool or integrated into the handheld power tool comprises. In this connection, reading should be understood as meaning the reading of one or more screwing profiles, i.e. an example of a signal of an operating variable of the electric motor. The screwing profiles can be read for example with the aid of a connection to the Internet.
Furthermore, the method step SM can also saving and classification of signals, associated with the exemplary applications, of the operating variable in at least one or more application classes. In this case, “classification” should also be understood as meaning the association of the examples of signals of the operating variable with at least one or more application classes.
In some embodiments of the invention, the method step SM also comprises the method step of:
In some embodiments of the invention, the method step SM also comprises the method step of:
In some embodiments of the invention, the method step SM also comprises the method step of:
In some embodiments of the invention, the method also comprises the method step of:
The handheld power tool can thus react according to the invention to different applications. The reactions could be for example immediately reducing the speed, immediately stopping the motor, reducing the speed with a delay and/or stopping the motor with a delay. Furthermore, a combination of the different reactions is also possible.
In the above example of the construction of kitchen furniture, the impact driver according to the invention ascertains in this embodiment, i.e. during screwing in, that a small wood screw is currently being screwed in, and executes the routine or reaction learned from the user, for example a reduction in the speed, automatically at the correct time. If wall plug screwing is subsequently carried out, the device likewise ascertains this other case of screwing automatically and reacts automatically at the right time, for example by reducing the speed.
This means that it is possible according to the invention to ensure for example that all screws of the same screw type are reproducibly screwed to the same depth, this representing both a simplification of work and an increase in work quality.
In alternative embodiments, it is provided that in the case of unknown applications, the first routine is estimated with the aid of known applications, with similar characteristics or a similar application class.
In some embodiments of the invention, the method also comprises the method step of:
The method according to the invention can also comprise the execution of the method steps SMa, SMb and SMc in a control unit of the handheld power tool and/or on a central computer, in particular by transmitting the signals, determined in step S1 and associated with the exemplary applications, of the operating variable via an Internet connection.
In further embodiments of the invention, the machine learning phase includes the execution or reading of at least two exemplary applications, preferably a multiplicity of exemplary applications, and also the determining of an average of the threshold value of the match from the two or more threshold values, associated with the exemplary applications, of the match.
In this way, irregularities in the screwing operation that are encountered everywhere, for instance variations in the support material, the screwing-in angle, the force exerted by the user, etc. are statistically averaged and thus their effect in the learning process is reduced.
In some embodiments of the invention, the exemplary applications are executed by a user of the handheld power tool and/or read from a database. In this case, both external and internal databases can be used. The use of an external database can comprise for example the reading of a screwing profile via the Internet, while the use of an internal database may be a provision of a database on the handheld power tool at the factory.
As a result of different routines, it is possible to provide the user with one or more system functionalities with which they can complete applications more quickly and/or easily. As a result of the machine learning phase provided according to the invention, the method is extremely adaptive and therefore highly suited to the needs of the user.
In one embodiment, the first routine comprises stopping the electric motor taking into consideration at least one defined and/or presettable parameter, in particular a parameter that is presettable by a user of the handheld power tool. Examples of such a parameter include a period of time, a number of revolutions of the electric motor, a number of revolutions of the tool receptacle, a rotational angle of the electric motor, and a number of impacts of the impact mechanism of the handheld power tool.
In a further embodiment, the first routine comprises changing, in particular reducing and/or increasing, a speed of the electric motor. Such a change in the speed of the electric motor may be achieved for example by means of a change in the motor current, the motor voltage, the battery current, or the battery voltage, or by a combination of these measures.
Preferably, an amplitude of the change in the speed of the electric motor is definable by a user of the handheld power tool. Alternatively or additionally, the change in the speed of the electric motor may also be specified by a target value. The term “amplitude” should in this connection also be understood generally as meaning a level of the change and not be associated only with cyclical processes.
In one embodiment, the change in the speed of the electric motor takes place multiply and/or dynamically, in particular successively in time and/or along a characteristic curve of the change in speed and/or on the basis of the work status of the handheld power tool, wherein the change in the speed is determined at least partially by means of the learning operation on the basis of the exemplary applications.
In principle, suitable operating variables which are captured via a suitable measuring transducer may be different operating variables. In this case, it is advantageous that, according to the invention, an additional sensor is not necessary in this regard since various sensors, for example for monitoring the speed, preferably Hall sensors, are already installed in electric motors.
Advantageously, the operating variable is a speed of the electric motor or an operating variable that correlates with the speed. The fixed transmission ratio of electric motor to impact mechanism results for example in direct dependence of the motor speed on the impact frequency. A further conceivable operating variable that correlates with the speed is the motor current. Also conceivable as operating variables of the electric motor are a motor voltage, a Hall signal of the motor, a battery current or a battery voltage, wherein an acceleration of the electric motor, an acceleration of a tool receptacle or a sound signal of an impact mechanism of the handheld power tool is also conceivable as the operating variable.
In some embodiments, the signal of the operating variable is captured in method step S1 as a time series of measured values of the operating variable, or as measured values of the operating variable as a variable of the electric motor that correlates with the time series, for example an acceleration, a jerk, in particular a higher order jerk, an output, an energy, a rotational angle of the electric motor, a rotational angle of the tool receptacle or a frequency.
In the last-mentioned embodiment, it is possible to ensure that a constant periodicity of the signal to be investigated is achieved regardless of the motor speed.
If the signal of the operating variable is captured in method step S1 as a time series of measured values of the operating variable, then, in a method step S1a following the method step S1, on the basis of a fixed transmission ratio of the transmission, the time series of the measured values of the operating variable is transformed into a series of the measured values of the operating variable as a variable of the electric motor that correlates with the time series. This again results in the same advantages as when the signal of the operating variable is captured directly over time.
Preferably, a work status of the first routine is output to a user of the handheld power tool using an output device of the handheld power tool. Output by means of the output device can be understood as meaning in particular the display or documentation of the work status. Here, documentation can also be the evaluation and/or saving of work statuses. This comprises for example the saving of multiple screwdriving operations also in a memory.
In one embodiment, the first routine and/or characteristic parameters of the first routine are settable and/or presentable by a user via an application program (“app”) or a user interface (“Human-Machine Interface”, “HMI”).
Furthermore, in one embodiment, the HMI may be arranged on the machine itself, while in other embodiments, the HMI may be arranged on external devices, for example a smartphone, a tablet or a computer.
In one embodiment of the invention, the first routine comprises visual, audible and/or haptic feedback to a user.
Preferably, the model signal shape is a waveform, for instance a waveform about a mean value, in particular a substantially trigonometric waveform. In this case, the model signal shape may represent for example ideal impact operation of the hammer on the anvil of the rotary impact mechanism, wherein the ideal impact operation is preferably an impact without onward rotation of the tool spindle of the handheld power tool.
In one embodiment of the invention, in method step S4, the signal of the operating variable is compared by means of a comparison method to determine whether at least one predefined threshold value of the match has been fulfilled.
Preferably, the comparison method comprises at least a frequency-based comparison method and/or a comparative comparison method.
In this case, the decision can be taken, at least partially by means of the frequency-based comparative method, in particular bandpass filtering and/or a frequency analysis, as to whether a work status to be ascertained has been identified in the signal of the operating variable.
In one embodiment, the frequency-based comparative method comprises at least the bandpass filtering and/or the frequency analysis, wherein the predefined threshold value amounts to at least 90%, in particular 95%, very particularly 98%, of a predefined limit value.
In the bandpass filtering, for example the picked up signal of the operating variable is filtered via a bandpass, the pass band of which matches the model signal shape. A corresponding amplitude in the resulting signal should be expected when the relevant work status to be ascertained is present. The predefined threshold value of the bandpass filtering can therefore be at least 90%, in particular 95%, very particularly 98%, of the corresponding amplitude in the work status to be ascertained. The predefined limit value can in this case be the corresponding amplitude in the resulting signal of an ideal work status to be ascertained.
As a result of the known frequency-based comparative method of the frequency analysis, the previously defined model signal shape, for example a frequency spectrum of the work status to be ascertained, can be looked for in the picked up signals of the operating variable. A corresponding amplitude of the work status to be ascertained should be expected in the picked up signals of the operating variable. The predefined threshold value of the frequency analysis can be at least 90%, in particular 95%, very particularly 98%, of the corresponding amplitude in the work status to be ascertained. The predefined limit value can in this case be the corresponding amplitude in the picked up signals of an ideal work status to be ascertained. In this case, appropriate segmentation of the picked up signal of the operating variable may be necessary.
In one embodiment, the comparative comparison method comprises at least one parameter estimate and/or a cross-correlation, wherein the predefined threshold value amounts to at least 40% of a match of the signal of the operating variable with the model signal shape.
The measured signal of the operating variable can be compared with the model signal shape by means of the comparative comparison method. The measured signal of the operating variable is determined in such a way that it has substantially the same finite signal length as that of the model signal shape. The comparison of the model signal shape with the measured signal of the operating variable can in this case be output as an, in particular discrete or continuous, signal of finite length. Depending on a degree of matching or a deviation of the comparison, a result can be output as to whether the work status to be ascertained exists. If the measured signal of the operating variable matches the model signal shape at least to an extent of 40%, the work status to be ascertained may exist. In addition, it is conceivable for the comparative method, by means of the comparison of the measured signal of the operating variable with the model signal shape, to be able to output a degree of a comparison with one another as the result of the comparison. In this case, the comparison of at least 60% to one another can be a criterion for the existence of the work status to be ascertained. Here, it should be assumed that the lower limit for the match lies at 40% and the upper limit for the match lies at 90%. Accordingly, the upper limit for the deviation lies at 60% and the lower limit for the deviation lies at 10%.
In the parameter estimation, a comparison between the previously defined model signal shape and the signal of the operating variable can easily take place. To this end, estimated parameters of the model signal shape can be identified in order to adapt the model signal shape to the measured signal of the operating variables. By means of a comparison between the estimated parameters of the previously defined model signal shape and a limit value, a result relating to the existence of the work status to be ascertained can be determined. Subsequently, a further evaluation of the result of the comparison can take place as to whether the predefined threshold value has been reached. This evaluation can be either a quality assessment of the estimated parameters or the match between the defined model signal shape and the captured signal of the operating variable.
In a further embodiment, method step S4 contains a step S4a of assessing the quality of the identification of the model signal shape in the signal of the operating variable, wherein, in method step S5, the work status is ascertained at least partially on the basis of the quality assessment. An adaptation quality of the estimated parameters can be determined as a measure of the quality assessment.
In method step S5, a decision can be taken, at least partially by means of the quality assessment, in particular the measure of the quality, as to whether the work status to be ascertained has been identified in the signal of the operating variable.
In addition or as an alternative to the quality assessment, method step S4a can comprise a comparative assessment of the identification of the model signal shape and the signal of the operating variable. The comparison of the estimated parameters of the model signal shape with the measured signal of the operating variable can amount to for example 70%, in particular 60%, very particularly 50%. In method step S5, the decision is taken as to whether the work status to be ascertained exists, at least partially on the basis of the comparative assessment. The decision on the existence of the work status to be ascertained can take place at the predefined threshold value of at least 40% matching of the measured signal of the operating variable and the model signal shape.
In the case of a cross-correlation, a comparison between the previously defined model signal shape and the measured signal of the operating variable can take place. In the cross-correlation, the previously defined model signal shape can be correlated with the measured signal of the operating variable. In the case of a correlation of the model signal shape with the measured signal of the operating variable, a measure of the match between the two signals can be determined. The measure of the match can amount to for example 40%, in particular 50%, very particularly 60%.
In method step S5 of the method according to the invention, the ascertainment of the work status can take place at least partially on the basis of the cross-correlation of the model signal shape with the measured signal of the operating variable. The ascertainment can in this case take place at least partially on the basis of the predefined threshold value of at least 40% matching of the measured signal of the operating variable and the model signal shape.
In one embodiment, the threshold value of the match is settable by a user of the handheld power tool and/or predefined at the factory.
In a further embodiment, the handheld power tool is an impact driver, in particular a rotary impact driver, and the work status is starting or stopping of impact operation, in particular rotary impact operation.
In one embodiment, one or more of the above-described steps SM, SMa, SMb, SMc are stored as operating modes, selectable by the user, of the handheld power tool in a controller thereof. The steps SMa, SMb, . . . include, inter alia, the determination, storage and classification of the exemplary applications. In that case, in principle, further reactions, for example, a speed regulation by way of reducing, increasing or switching off, are also able to be selected by the user. In parallel, the handheld power tool can have operating modes in which the threshold value of the match is selectable by a user on the basis of a preselection, predefined at the factory, of applications of the handheld power tool. This can take place for example via a user interface, for instance an HMI (Human-Machine Interface), for instance a mobile device, in particular a smartphone and/or a tablet.
In particular, in method step S3a, the model signal shape may be set to be variable, in particular by a user. Here, the model signal shape is associated with the work status to be ascertained, such that the user can specify the work status to be ascertained.
Advantageously, the model signal shape is predefined in method step S3a, in particular set at the factory. In principle, it is conceivable for the model signal shape to be stored or saved inside the device, alternatively and/or additionally provided to the handheld power tool, in particular provided by an external data device.
A person skilled in the art will recognize that the method according to the invention allows the work status to be ascertained independently of at least one setpoint speed of the electric motor, at least of a start-up characteristic of the electric motor and/or at least of a state of charge of the energy supply, in particular of a rechargeable battery, of the handheld power tool.
The signal of the operating variable should be understood here as being a temporal sequence of measured values. Alternatively and/or additionally, the signal of the operating variable can also be a frequency spectrum. Alternatively and/or additionally, the signal of the operating variable can also be post-processed, for example smoothed, filtered, fitted and the like.
In a further embodiment, the signal of the operating variable is stored as a series of measured values in a memory, preferably a ring memory, in particular of the handheld power tool.
In one method step, the work status to be ascertained is identified on the basis of fewer than ten impacts of an impact mechanism of the handheld power tool, in particular fewer than ten impact vibration periods of the electric motor, preferably fewer than six impacts of an impact mechanism of the handheld power tool, in particular fewer than six impact vibration periods of the electric motor, most preferably fewer than four impacts of an impact mechanism, in particular fewer than four impact vibration periods of the electric motor. Here, an impact of the impact mechanism should be understood as being an axial, radial, tangential and/or circumferentially directed impact of an impact mechanism striker, in particular of a hammer, on an impact mechanism body, in particular an anvil. The impact vibration period of the electric motor is correlated with the operating variable of the electric motor. An impact vibration period of the electric motor can be determined from operating variable fluctuations in the signal of the operating variable.
A further subject of the invention is a handheld power tool having an electric motor, a measured-value pickup for capturing an operating variable of the electric motor, and a control unit, wherein advantageously the handheld power tool is an impact driver, in particular a rotary impact driver, and the handheld power tool is designed to execute the above-described method.
The electric motor of the handheld power tool sets an input spindle in rotation, and an output spindle is connected to the tool receptacle. An anvil is connected to the output spindle for conjoint rotation and a hammer is connected to the input spindle such that, as a result of the rotary movement of the input spindle, it executes an intermittent movement in the axial direction of the input spindle and an intermittent rotational movement about the input spindle, wherein the hammer in this way intermittently strikes the anvil and thus emits an impact pulse and angular momentum to the anvil and thus to the output spindle. A first sensor transmits a first signal, for example for determining a motor rotational angle, to the control unit. Furthermore, a second sensor can transmit a second signal for determining a motor speed to the control unit.
Advantageously, the handheld power tool has a memory unit, in which various values can be stored.
In a further embodiment, the handheld power tool is a battery-powered handheld power tool, in particular a battery-powered rotary impact driver. This ensures flexible use, independent of the grid, of the handheld power tool.
Advantageously, the handheld power tool is an impact driver, in particular a rotary impact driver, and the work status to be ascertained is the contact of a screw head with a fastening substrate, the free rotation of a loosened screw, the starting or stopping of a rotary impact mechanism of the handheld power tool, and/or an impact of the rotary impact mechanism without onward rotation of the struck element or of the tool receptacle.
The identification of the impacts of the impact mechanism of the handheld power tool, in particular the impact vibration periods of the electric motor, can be achieved for example in that a fast fitting algorithm is used, by means of which an evaluation of the impact ascertainment within less than 100 ms, in particular less than 60 ms, very particularly less than 40 ms, can be allowed. Here, the abovementioned method according to the invention allows a work status to be ascertained substantially for all of the abovementioned applications and allows loose and fixed fastening elements to be screwed into the fastening carrier.
By way of the present invention, it is possible to largely dispense with more complicated methods of signal processing, for example filters, signal loopbacks, system models (static and adaptive) and signal tracking.
Furthermore, these methods allow even quicker identification of the impact operation and of the work status, with the result that an even quicker reaction of the tool can be brought about. This applies in particular for the number of past impacts after the starting of the impact mechanism up to the identification and also in particular operating situations, for example the start-up phase of the drive motor. In this case, it is also not necessary for restrictions of the functionality of the tool, for example reducing the maximum drive speed, to be applied. Furthermore, the functioning of the algorithm is also independent of other influencing variables, for example the setpoint speed and battery state of charge.
In principle, no further sensor systems (for example an acceleration sensor) are required, but these evaluation methods can nevertheless also be applied to signals of further sensor systems. Furthermore, in other motor concepts, which manage for example without capturing the speed, this method can also be used for other signals.
In a preferred embodiment, the handheld power tool is a battery screwdriver, a drill, an impact drill or a hammer drill, wherein a drill bit, a core bit or various bit attachments can be used as the tool. The handheld power tool according to the invention is in particular in the form of an impact driver, wherein, as a result of the pulsed release of the motor energy, a higher peak torque for screwing in or unscrewing a screw or a nut is generated. Transmission of electrical energy should be understood in this context as meaning in particular that the handheld power tool passes energy on to the body via a rechargeable battery and/or a power cable connection.
Moreover, depending on the chosen embodiment, the screwdriver may be designed to be flexible in terms of its direction of rotation. In this way, the proposed method can be used both for screwing in and for unscrewing a screw or a nut.
In the context of the present invention, “determine” is intended to include in particular measure or capture, wherein “capture” should be understood as meaning measure and store, and in addition “determine” is also intended to include possible signal processing of a measured signal. Determination by a classification or clustering of a signal, for example.
Furthermore, “decide” should also be understood as meaning ascertain or detect, wherein a clear association is intended to be achieved. “Identify” should be understood as meaning ascertaining a partial match with a pattern, which can be allowed for example by fitting a signal to the pattern, a Fourier analysis or the like. The “partial match” should be understood as meaning that the fitting exhibits an error that is less than a predefined threshold, in particular less than 30%, very particularly less than 20%.
Further features, possible applications and advantages of the invention will become apparent from the following description of the exemplary embodiment of the invention, which is illustrated in the drawing. It should be noted here that the features described or illustrated in the figures, individually or in any desired combination, have only a descriptive character for the subject matter of the invention, regardless of how they are summarized in the claims or the back-references therein, and regardless of how they are formulated and illustrated in the description and in the drawing, respectively, and are not intended to limit the invention in any form.
The invention is explained in more detail in the following text on the basis of preferred exemplary embodiments. In the schematic drawings:
Arranged in the housing 105 are an electric motor 180, supplied with power by the battery pack 190, and a transmission 170. The electric motor 180 is connected to an input spindle via the transmission 170. Furthermore, a control unit 370 is arranged within the housing 105 in the region of the battery pack 190, said control unit 370, for the open-loop and/or closed-loop control of the electric motor 180 and the transmission 170, acting thereon for example by means of a set motor speed n, a selected angular momentum, a desired gear x or the like.
The electric motor 180 is actuable, i.e. able to be switched on and off, for example via a manual switch 195, and may be any desired type of motor, for example an electronically commutated motor or a DC motor. In principle, the electric motor 180 is able to be subjected to electronic open-loop and/or closed-loop control such that both reversing operation and specifications with regard to the desired motor speed n and the desired angular momentum are realizable. The manner of operation and the structure of a suitable electric motor are sufficiently well known from the prior art and so will not be described in detail here in order to keep the description concise.
Via an input spindle and an output spindle, a tool receptacle 140 is mounted rotatably in the housing 105. The tool receptacle 140 serves to receive a tool and can be integrally formed directly on the output spindle or connected thereto in the form of an attachment.
The control unit 370 is connected to a power source and is configured such that it can subject the electric motor 180 to electronic open-loop and/or closed-loop control by means of various current signals. The various current signals provide for different angular momentums of the electric motor 180, wherein the current signals are passed to the electric motor 180 via a control line. The power source may be in the form for example of a battery or, as in the illustrated exemplary embodiment, in the form of a battery pack 190 or of a connection to the grid.
Furthermore, control elements (not illustrated in detail) may be provided in order to set different operating modes and/or the direction of rotation of the electric motor 180.
According to one aspect of the invention, a method for operating a handheld power tool 100 is provided, by means of which a work status for example of the handheld power tool 100 illustrated in
As a consequence of the establishment of the work status, in embodiments of the invention, corresponding reactions or routines are initiated by the machine. As a result, reliably reproducible, high-quality screwing-in and unscrewing operations can be achieved. Aspects of the method are based, inter alia, on an investigation of signal shapes and a determination of a degree of matching of these signal shapes, which may correspond for example to an evaluation of onward rotation of an element, for instance a screw, driven by the handheld power tool 100.
Time is plotted as reference variable on the abscissa x in the present example in
The motor speed and motor current are operating variables that are usually captured without additional effort by a control unit 370 in handheld power tools 100. The provision of a signal of an operating variable 200 of the electric motor 180 is referred to as method step S1 in the context of the present disclosure. “Providing” is understood in this context as making available the corresponding feature in an internal or external memory of the handheld power tool 100.
In preferred embodiments of the invention, a user of the handheld power tool 100 can select the operating variable on the basis of which the method according to the invention is intended to be carried out.
In the first region 310, the screw 900 encounters relatively little resistance in the fastening carrier 902, and the torque required for screwing it in lies beneath the disengagement torque of the rotary impact mechanism. The curve of the motor speed in the first region 310 thus corresponds to the operating state of screwdriving without impact.
As is apparent from
If the head of the screw 900 subsequently reaches the substrate 902, an even higher torque and thus more impact energy is required for further screwing in. Since, however, the handheld power tool 100 does not supply any more impact energy, the screw 900 no longer rotates onward or rotates onward only through a significantly smaller rotational angle.
The rotary impact operation executed in the second 322 and third region 324 is characterized by an oscillating curve of the signal of the operating variable 200, wherein the shape of the oscillation can be for example trigonometric or other oscillation. In the present case, the oscillation has a curve that can be referred to as a modified trigonometric function. This characteristic shape of the signal of the operating variable 200 in impact screwdriving operation arises on account of the priming and releasing of the impact mechanism striker and the system chain, inter alia of the transmission 170, located between the impact mechanism and electric motor 180.
Using the circumstance that different cases of screwing each have characteristic shapes of the signals of the operating variables, in a step S2 of the method according to the invention, an application class is determined on the basis of the signal of the operating variable 200. In the case of a screwing process, the concept of the application class can in this case comprise, inter alia, one or more aspects such as screw size, screw type, screwing direction (screwing in or unscrewing), screw resistance, screwing speed, material of the screw substrate, and/or operating mode of the handheld power tool for the application executed by the user.
As can be gathered from the above, the individual work statuses, for example signal shapes associated with the starting of impact operation, are furthermore also characterized in principle by particular characteristic features, which are preset at least partially by the inherent properties of the rotary impact driver. In the method according to the invention, starting from this finding, in a step S3, comparison information is provided at least partially on the basis of the application class determined in step S2, wherein, in a step S3a, at least one model signal shape 240 is provided. The model signal shape 240 is in this case able to be associated with a work status, for example the achievement of the head of the screw 900 resting on the fastening carrier 902, and, in conjunction with some embodiments of the invention, the model signal shape 240 is also referred to as a state-typical model signal shape. In other words, the model signal shape 240 contains typical features for the work status, such as the existence of a waveform, vibration frequencies or amplitudes, or individual signal sequences in a continuous, quasi-continuous or discrete form.
In other applications, the work status to be detected can be characterized by other signal shapes than by vibrations, for instance by discontinuities or growth rates in the function f(x). In such cases, the state-typical model signal shape is characterized by these very parameters rather than by vibrations.
In a method step S3b, a further item of comparison information is provided, namely a threshold value of the match, which will be described in more detail below.
In a preferred configuration of the method according to the invention, in method step S3, the state-typical model signal shape 240 can be set by a user. The state-typical model signal shape 240 can likewise be stored or saved inside the device or provided by an external data device.
In a method step S4 of the method according to the invention, the signal of the operating variable 200 of the electric motor 180 is compared with the state-typical model signal shape 240. The feature “compare” should be understood to have a broad meaning in the context of the present invention and to be interpreted within the scope of signal analysis, such that a result of the comparison may in particular also be a partial or gradual match of the signal of the operating variable 200 of the electric motor 180 with the model signal shape 240, wherein the degree of matching of the two signals can be determined by different mathematical methods which will be described later.
In step S4, a match rating of the signal of the operating variable 200 of the electric motor 180 with the state-typical model signal shape 240 is moreover determined from the comparison and thus a statement can be made about the matching of the two signals. The match rating takes place in this case at least partially on the basis of the abovementioned threshold value of the match, which can thus also be understood as being the lower limit of the match of the signal of the operating variable 200 with the model signal shape 240 and is explained in more detail in the following text.
In the present example of the screwing in of the screw 900, this rating is used to determine the amount of onward rotation upon an impact. The model signal shape 240 provided in step S3a corresponds in the example to an ideal impact without onward rotation, meaning the state in which the head of the screw 900 is in contact with the surface of the fastening carrier 902, as shown in the region 324 in
As is apparent from the example in
In a method step S5 of the method according to the invention, the work status is now ascertained at least partially on the basis of the match rating 201 determined in method step S4. It should be noted here that the function is not only limited to screwing-in applications but also includes a use in unscrewing applications.
The provision of the comparison information in step S3 can take place according to the invention at least partially on the basis of a machine learning phase. The machine learning phase includes, in embodiments of the invention, the execution or reading of at least two or more exemplary applications of the handheld power tool 100, wherein the at least one exemplary application comprises reaching a defined work status of the handheld power tool 100, for example reaching of the state in which the head of the screw 900 rests on the surface of the fastening carrier 902, as shown in region 324 of
The method according to the invention accordingly comprises, in this embodiment, a step SM of executing a machine learning phase on the basis of at least two or more exemplary applications, wherein the exemplary applications comprise reaching the determined work status. In this embodiment, the determination of the application classes in step S2 and the provision of the model signal shape 240 and/or the threshold value of the match in step S3 takes place at least partially on the basis of application classes generated in the machine learning phase and of threshold values of the match and/or model signal shapes 240′ associated with the application classes.
The handheld power tool thus learns automatically or partially automatically the time at which, in different applications, a reaction to the curve of the match evaluation is desired, without the user needing to give a corresponding instruction.
In particular embodiments of the invention, the method step SM in this case advantageously comprises saving and classification of signals, associated with the exemplary applications, of the operating variable 200′ in at least one or more application classes.
Specific configurations of the method according to the invention can, for this purpose, contain one or more of the following method steps.
SMa determining, saving and classifying model signal shapes 240′, associated with the exemplary applications, at least partially on the basis of the respective signal of the operating variable 200′ at the time the defined work status is reached.
SMb determining, saving and classifying threshold values, associated with the exemplary applications, of the match, at least partially on the basis of the respective signal of the operating variable 200′ at the time the defined work status is reached.
SMc determining and saving threshold values, associated with the application classes, of the match, on the basis of the saved threshold values of the match and model signal shapes 240′ associated with the exemplary applications.
The steps SMa, SMb and SMc comprise different data analysis methods that are known per se, for example averaging or more advanced operation of explorative statistics, which generally provide a more accurate result the larger the set of exemplary applications. In this connection, it should be noted that the method steps SMa, SMb and SMc can take place selectively in a control unit of the handheld power tool 100 and/or on a central computer, in particular by transmitting the signals, determined in step S1 and associated with the exemplary applications, of the operating variable 200′ via an Internet connection. In this case, it is possible for particular steps of the method according to the invention, for example the above-mentioned data analysis for identifying the defined work status or the threshold values of the match not to be executed by the handheld power tool 100 but by a central computer node, and for the underlying set of exemplary applications to be maximized by combining exemplary applications received from different users and saved.
Conversely, such a procedure makes it possible for the exemplary applications to not necessarily need to be executed by a user of the handheld power tool 100. Rather, they can also be read directly from a database. In this case, the database can be both an external database connected for example via the Internet or an internal database, for example in the form of a database provided on the handheld power tool itself at the factory. Exemplary applications or data characterizing these exemplary applications and read from an external database are also referred to as “screw profiles” in connection with the present invention.
The basis of these embodiments is thus accumulation of the “wealth of experience” of the handheld power tool 100 by exemplary applications which are either executed by the handheld power tool 100 itself or are transmitted to the handheld power tool 100 in the form of datasets.
Put simply, in the first variant, the user carries out for example a multiplicity of screwing-in processes, wherein the handheld power tool 100 automatically and cumulatively executes data analyses to classify and categorize the applications, and to determine the model signal shapes 200′ and the threshold values of the match when the user carries out a repetitive routine, for example stops the handheld power tool 100 or reduces the speed. It should be noted here that this repetitive routine can itself likewise be used to classify and categorize the application. In subsequent applications which are likewise evaluated using this methodology, the handheld power tool 100 then automatically determines the application class, provides the comparison information associated with this application class, i.e. at least the model signal shape 240 and threshold value of the match, and, given a corresponding match evaluation of the currently existing signal of the operating variable 200 and of the model signal 240, automatically executes the routine carried out by the user in the particular application class, for example a reduction in the speed of the electric motor 180, as will be described in more detail below.
According to the invention, it is thus possible, by distinguishing and comparing signal shapes, to evaluate a work status of an element driven by a rotary impact driver and to initiate a routing following the work status, wherein particular signal shapes used here, namely the model signal shape 240, and a part of the evaluation criterion for matching the compared signal shapes, namely the threshold value of the match, are made available at least partially by a machine learning phase.
In one embodiment of the invention, it is provided that the machine learning phase in step SM comprises learning of the handheld power tool by way of images as part of “deep learning”. In this case, a suitable image capturing device and/or existing images of application sequences are used. In this case, the image capturing device can comprise an image sensor or a camera, with which the exemplary applications are optically captured, analyzed using the known image processing tools, and subsequently categorized. In a similar way, it is also possible, in step S2, to determine the application class with the aid of optical capturing by way of the image capturing device and subsequent image analysis. Conceivable image capturing devices may be internal image sensors or cameras, i.e. images sensors or cameras integrated in the handheld power tool, or external image sensors or cameras, for example the camera of a smartphone.
Advantageously, the establishment of the work status learned according to the above statements is supplemented by a further method step S6, in which a first routine of the handheld power tool 100 is executed at least partially on the basis of the work status ascertained in method step S5, as set out below. In this case, it is assumed in each case that the work status to be ascertained, as a result of which the handheld power tool executes the abovementioned first routine in method step S6, was defined by a machine learning phase as described above by way of the parameters of the model signal shape 240 and/or threshold value of the match. However, in alternative embodiments, it is likewise provided that the first routine is estimated, in unknown applications, with the aid of known applications with similar characteristics.
In spite of the resultant reduction in the speed on changing the operating state to impact operation, in the case for example of small wood screws or self-tapping screws, it is possible only with great difficulty to prevent the screw head from penetrating into the material. This is due to the fact that the impacts of the impact mechanism result in a high spindle speed, even with increasing torque.
This behavior is illustrated in
In the “no impact” operating state, which is indicated by the reference sign 310 in the figure, the screw rotates at a high speed f and low torque g. In the “impact” operating state, indicated by the reference sign 320, the torque g increases rapidly, while the speed f decreases only slightly, as already noted above. The region 310′ in
In order for example to prevent a screw head of the screw 900 from penetrating the fastening carrier 902, according to the invention, in the method step S6, an application-related, appropriate routine or reaction of the tool is executed at least partially on the basis of the work status ascertained in method step S5, for instance switching off of the machine, a change in the speed of the electric motor 180, and/or visual, audible and/or haptic feedback to the user of the handheld power tool 100.
In one embodiment of the invention, the first routine comprises the stopping of the electric motor 180 taking into consideration at least one defined and/or presettable parameter, in a particular a parameter that is presettable by a user of the handheld power tool.
As an example of this, stopping of the device immediately after the impact ascertainment 310′ is schematically shown in
An example of a defined and/or presettable parameter, in particular a parameter that is settable by a user of the handheld power tool 100, a time, defined by the user, after which the device stops, this being illustrated in
Alternatively or in addition, in one embodiment of the invention, the first routine comprises a change, in particular a reduction and/or an increase, in a speed, in particular a setpoint speed, of the electric motor 180 and therefore also of the spindle speed after impact ascertainment. The embodiment in which a reduction in the speed is executed is illustrated in
The amplitude or the level of the change in speed of the electric motor 180, characterized by ΔD for the branch f″ of the graph f in
In one embodiment of the invention, the amplitude ΔD of the change in speed of the electric motor 180 and/or a target value of the speed of the electric motor 180 is definable by a user of the handheld power tool 100, this increasing the flexibility of this routine further for the purposes of applicability for different applications.
The change in speed of the electric motor 180 takes place multiply and/or dynamically in embodiments of the invention. In particular provision may be made for the change in speed of the electric motor 180 to take place successively in time and/or along a characteristic curve of the change in speed, and/or depending on the work status of the handheld power tool 100.
Examples of this comprise, inter alia, combinations of a reduction in speed and an increase in speed. Moreover, different routines or combinations thereof can be executed in a time-offset manner for impact ascertainment. Furthermore, the invention also comprises embodiments in which there is a temporal offset between two or more routines. If, for example, the motor speed is reduced directly after impact ascertainment, the motor speed can also be increased again after a particular time value. Furthermore, embodiments are provided in which not only different routines themselves but also the time offset between the routines is preset by a characteristic curve.
As mentioned at the beginning, the invention comprises embodiments in which the work status is characterized by a change from an “impact” operating state in a region 320 to the “no impact” operating state in a region 310, this being illustrated in
Such a transition of the operating states of the handheld power tool 100 is given for example in a work status in which a screw 900 is released from a fastening carrier 902, i.e. during an unscrewing operation, this being schematically illustrated in the lower region of
As already explained in connection with other embodiments of the invention, the operating state of the handheld power tool, in the present case the operating state of the impact mechanism, is also ascertained here with the aid of the discovery of characteristic signal shapes.
In the “impact” operating state, i.e. in the region 320 in
The method according to the invention can be applied in order to prevent a threaded means, which may be a screw 900 or a nut, from being unscrewed so rapidly after being released from the fastening carrier 902 that it drops down. In this regard, reference is made to
In one embodiment, the routine in step S6 comprises the stopping of the handheld power tool 100 immediately after it has been established that the handheld power tool 100 has ascertained the work status to be ascertained, in the example the “no impact” operating mode, this being illustrated in
Given a suitable selection of the time period TStopp, it is possible for the motor speed to drop to “zero” precisely when the screw 900 or the nut is still located in the thread. In this case, the user can remove the screw 900 or the nut by way of a few thread revolutions or alternatively leave it in the thread in order, for example, to open a clamp.
A further embodiment of the invention is described in the following text with reference to
As a result of the reduction in the motor speed and thus also in the spindle speed, the user has more time to react when the head of the screw 900 is released from the screw contact surface. As soon as the user is of the opinion that the screw head or the nut has been screwed far enough, they can use the switch to stop the handheld power tool 100.
Compared with the embodiments described in connection with
It should be mentioned that, in some embodiments of the invention, it is provided that the parameters, used in method step S6, of the first routine as described above, for example curve and amplitude of a reduction or increase in speed, can also be defined by a machine learning phase on the basis of exemplary applications and/or screw profiles.
Furthermore, by way of a further method step S7, in which a quality evaluation of the user of the handheld power tool 100 relating to the first routine executed in step S6 is collected, the routine can be optimized at least partially on the basis of the evaluation.
In some embodiments of the invention, a work status is output to a user of the handheld power tool by means of an output device of the handheld power tool.
A number of technical relationships and embodiments relating to the execution of method steps S1-S5 are explained in the following text.
In practical applications, provision may be made for one or more of the method steps S1 to S4 to be executed repetitively during operation of the handheld power tool 100, in order to monitor the work status of the executed application. For this purpose, in method step S1, the determined signal of the operating variable 200 may be segmented such that method steps S2 and S4 are executed on signal segments, preferably always of an identical, fixed length.
For this purpose, the signal of the operating variable 200 can be stored as a sequence of measured values in a memory, preferably a ring memory. In this embodiment, the handheld power tool 100 comprises the memory, preferably the ring memory.
As already mentioned in connection with
In one embodiment, the signal of the operating variable 200 is captured in method step S1 as a time series of measured values of the operating variable, and in a method step S1a following the method step S1, the time series of the measured values of the operating variable is transformed into a series of the measured values of the operating variable as a variable of the electric motor 180 that correlates with the time series, for example a rotational angle of the tool receptacle 140, the motor rotational angle, an acceleration, a jerk, in particular a higher order jerk, an output, or an energy.
The advantages of this embodiment are described in the following text with reference to
The depiction contains two signal curves of the operating variable 200, which can each be associated with a work status, thus for example the rotary impact screwdriving mode in the case of a rotary impact driver. In both cases, the signal comprises a wavelength of a waveform assumed to be sinusoidal under ideal conditions, wherein the signal with a shorter wavelength, T1 has a curve with a higher impact frequency, and the signal with a longer wavelength, T2 has a curve with a lower impact frequency.
Both signals can be generated with the same handheld power tool 100 at different motor speeds and are dependent, inter alia, on the speed of rotation that the user requests via the operating switch of the handheld power tool 100.
If, for example, the parameter “wavelength” is now used for the definition of the state-typical model signal shape 240, at least two different wavelengths T1 and T2 would have to be stored, in the present case, as possible parts of the state-typical model signal shape, in order that the comparison of the signal of the operating variable 200 with the state-typical model signal shape 240 results in both cases in the result of a “match”. Since the motor speed can change generally and significantly over time, this means that the desired wavelength also varies and as a result the methods for ascertaining this impact frequency would accordingly have to be set adaptively.
Given a large number of possible wavelengths, the complexity of the method and of the programming would accordingly increase rapidly.
Therefore, in the preferred embodiment, the time values of the abscissa are transformed into values that correlate with the time values, for example acceleration values, higher order jerk values, output values, energy values, frequency values, rotational angle values of the tool receptacle 140 or rotational angle values of the electric motor 180. This is possible because the fixed transmission ratio of the electric motor 180 to the impact mechanism and to the tool receptacle 140 results in a direct, known dependence of the motor speed with respect to the impact frequency. As a result of this standardization, a vibration signal, independent of the motor speed, of constant periodicity is achieved, this being illustrated in
Accordingly, in this embodiment of the invention, the state-typical model signal shape 240 can be defined, valid for all speeds, by way of a single parameter of the wavelength over the variable that correlates with time, for example the rotational angle of the tool receptacle 140, the motor rotational angle, an acceleration, a jerk, in particular a higher order jerk, an output, or an energy.
In a preferred embodiment, the comparison of the signal of the operating variable 200 in method step S4 takes place using a comparison method, wherein the comparison method comprises at least a frequency-based comparison method and/or a comparative comparison method. The comparison method compares the signal of the operating variable 200 with the state-typical model signal shape 240 to determine whether at least the threshold value of the match has been fulfilled. The comparison method compares the measured signal of the operating variable 200 with the threshold value of the match. The frequency-based comparison method comprises at least the bandpass filtering and/or the frequency analysis. The comparative comparison method comprises at least the parameter estimation and/or the cross-correlation. The frequency-based comparison method and the comparative comparison method are described in more detail in the following text.
In embodiments with bandpass filtering, the input signal transformed, optionally as described, into a variable that correlates with time is filtered via one or more bandpasses, the pass bands of which match one or more state-typical model signal shapes. The pass band results from the state-typical model signal shape 240. It is also conceivable for the pass band to match a frequency stored in connection with the state-typical model signal shape 240. In the event that amplitudes of this frequency exceed a previously set limit value, as is the case upon reaching the work status to be ascertained, the comparison in method step S4 then leads to the result that the signal of the operating variable 200 is equal to the state-typical model signal shape 240 and that therefore the work status to be ascertained has been reached. The setting of an amplitude limit value can, in this embodiment, be understood as being the determination of the match rating of the state-typical model signal shape 240 with the signal of the operating variable 200, on the basis of which a decision is taken in method step S5 as to whether the work status to be ascertained exists or not.
With reference to
The frequency analysis in this form is sufficiently well known as a mathematical tool of signal analysis from many fields in the art and is used, inter alia, to approximate measured signals as series expansions of weighted periodic, harmonic functions of different wavelengths. In
With regard to the method according to the invention, it is thus possible, with the aid of the frequency analysis, to determine whether and with what amplitude the frequency associated with the state-typical model signal shape 240 exists in the signal of the operating variable 200. Furthermore, however, it is also possible for frequencies to be defined, the non-existence of which is a measure of the presence of the work status to be ascertained. As mentioned in connection with the bandpass filtering, a limit value of the amplitude can be set, which is a measure of the degree of matching of the signal of the operating variable 200 with the state-typical model signal shape 240.
In the example in
In alternative embodiments of the invention, only one of these criteria is used, or combinations of one of the criteria or of both criteria with other criteria, for example the reaching of a setpoint speed of the electric motor 180.
In embodiments in which the comparative comparison method is used, the signal of the operating variable 200 is compared with the state-typical model signal shape 240 in order to find out whether the measured signal of the operating variable 200 has an at least 50% match with the state-typical model signal shape 240 and thus the predefined threshold value has been reached. It is also conceivable for the signal of the operating variable 200 to be compared with the state-typical model signal shape 240 in order to determine a match of the two signals with one another.
In embodiments of the method according to the invention in which the parameter estimation is used as the comparative comparison method, the measured signal of the operating variables 200 is compared with the state-typical model signal shape 240, wherein parameters estimated for the state-typical model signal shape 240 are identified. With the aid of the estimated parameters, a measure of the matching of the measure signal of the operating variables 200 with the state-typical model signal shape 240 can be determined, to find out whether the work status to be ascertained has been reached. The parameter estimation is based in this case on curve fitting, which is a mathematical optimization method known to a person skilled in the art. The mathematical optimization method makes it possible, with the aid of the estimated parameters, to adapt the state-typical model signal shape 240 to a series of measurement data from the signal of the operating variable 200. Depending on the degree of matching of the state-typical model signal shape 240 parameterized by means of the estimated parameters and a limit value, the decision as to whether the work status to be ascertained has been reached can be taken.
With the aid of the curve fitting of the comparative method of parameter estimation, it is also possible to determine a degree of matching of the estimated parameters of the state-typical model signal shape 240 with respect to the measured signal of the operating variable 200.
In one embodiment of the method according to the invention, the cross-correlation method is used as comparative comparison method in method step S4. Like the mathematical methods described above, the cross-correlation method is known per se to a person skilled in the art. In the cross-correlation method, the state-signal model signal shape 240 is correlated with the measured signal of the operating variable 200.
Compared with the method, set out above, of parameter estimation, this result of the cross-correlation is again a signal sequence with a signal length added up from a length of the signal of the operating variable 200 and the state-typical model signal shape 240, which represents the similarity of the time-shifted input signals. In this case, the maximum of this output sequence represents the time of the greatest match of the two signals, i.e. of the signal of the operating variable 200 and the state-typical model signal shape 240, and is therefore also a measure for the correlation itself, which is used, in this embodiment, in method step S5, as a decision criterion for the reaching of the work status to be ascertained. In the implementation in the method according to the invention, a significant difference from the parameter estimation is that any desired state-typical model signal shapes can be used for the cross-correlation, while, in the parameter estimation, the state-typical model signal shape 240 has to be able to be represented by parameterizable mathematical functions.
The invention is not limited to the exemplary embodiment described and illustrated. Rather, it encompasses all developments that a person skilled in the art might make in the scope of the invention defined by the claims.
In addition to the embodiments described and depicted, further embodiments are conceivable, which may encompass further modifications and combinations of features.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10 2019 215 417.4 | Oct 2019 | DE | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2020/076496 | 9/23/2020 | WO |
