The present invention relates to a facility status monitoring method and facility status monitoring device that sense in an early stage a malfunction of a facility or a sign of the malfunction, which occurs during an ever-changing activation or suspension sequence, on the basis of multidimensional time-sequential data items outputted from a plant or facility, or restore a continual change, which cannot be obtained because of a sampling interval that is made longer for the purpose of reducing a cost, and monitor statistical-probability properties of the change.
Electric power companies supply warm water for district heating by utilizing waste heat of a gas turbine or the like, or supply high-pressure steam or low-pressure steam to factories. Petrochemical companies run the gas turbine or the like as a power supply facility. At various plants or facilities employing the gas turbine or the like, preventive maintenance for sensing a malfunction of a facility or a sign of the malfunction has quite significant meanings even from the viewpoint of minimizing damage to a society. In particular, a failure is liable to occur frequently during an ever-changing sequence such as activation or suspension. Therefore, it is important to sense in an early stage an abnormality occurring during the period.
Not only a gas turbine or steam turbine but also a water wheel at a hydroelectric power plant, a reactor at a nuclear power plant, a windmill at a wind power plant, an engine of an aircraft or heavy machinery, a railroad vehicle or track, an escalator, an elevator, and machining equipment for cutting or boring are requested to immediately sense an abnormality in the performance for the purpose of preventing occurrence of a fault in case such an abnormality is found.
Accordingly, plural sensors are attached to a facility or plant concerned in order to automatically decide based on a monitoring criterion set for each of the sensors whether the facility or plant is normal or abnormal. An example proved effective in sensing an abnormality during normal running of such an object as a facility, manufacturing equipment, or measuring equipment has been disclosed in Patent Literature 1 (Japanese Patent Application Laid-Open No. 2011-070635). In the disclosed example of Patent Literature 1, multidimensional data items of a facility are mapped into a feature space, and a normal model is created in the feature space. A projection distance of newly inputted sensor data to the normal model is regarded as an abnormality measure. An abnormality is sensed based on whether the abnormality measure exceeds a predetermined threshold.
As a typical technique of sensing an abnormality while calculating parameters that represent statistical-probability properties and enable simultaneous monitoring of the statistical-probability properties of time-sequential sensor data items, a method is disclosed in Non-Patent Literature 1 and Non-Patent Literature 2. According to the method, statistical-probability parameters calculated directly from a sensor wave at respective times are used to produce a normal model. An abnormality is sensed using a degree of separation from the model.
The technology disclosed in Patent Literature 1 has difficulty in presaging or sensing an abnormality occurring during an ever-changing activation or suspension sequence or in machining equipment whose load varies greatly.
In contrast, in the case of an ever-changing sequence, for example, an activation sequence in (b), a change in a data value of acquired normal sensor data items is so large that a normal local space in the feature space created from the normal sensor data items is spread more widely than that obtained at the time of steady running. If an abnormality occurs during the sequence period, abnormal data is found in the normal local space in the feature space, and is hard to be sensed as an abnormality.
According to the method disclosed in the Non-Patent literature 1 and Non-Patent Literature 2, a degree of abnormality is calculated at each time. Therefore, when a sequence changes continually, an abnormality occurring during the sequence can be sensed. However, at a facility such as a plant, if sensor data items are merely acquired in units of a long sampling interval because of a reduction in cost, the continual change cannot be fully grasped. If sampling times of a sensor are not synchronous with the initiation of the sequence, a time lag arises between sensor data items acquired at different times during the same sequence. If data items cannot be acquired with multidimensional sensors synchronized with each other, the time lag arises between sensor data items of the sensors. Therefore, the technology disclosed in Non-Patent Literature 1 and Non-Patent Literature 2 cannot calculate a statistical-probability parameter at each time, and cannot therefore sense an abnormality.
The present invention solves the foregoing problems of the related arts, and provides a facility status monitoring method and facility status monitoring device employing an abnormality sensing method capable of sensing an abnormality while monitoring a continual change in an ever-changing activation or suspension sequence and statistical-probability properties of the change.
In order to solve the aforesaid problems, the present invention provides a method of sensing an abnormality of a plant or facility in which: a sensor signal intermittently outputted from a sensor attached to a plant or facility, and event signals associated with the initiation and termination respectively of an activation sequence or suspension sequence of the plant or facility during the same period as a period during which the sensor signal is acquired are inputted; a sensor signal associated with a section between the event signal of the initiation of the activation sequence or suspension sequence and the event signal of the termination thereof is cut from the inputted sensor signal; signal values at certain times of the cut sensor signal and probability distributions thereof are estimated; a feature quantity is extracted based on the estimated probability distributions; and an abnormality of the plant or facility is sensed based on the extracted feature quantity.
In order to solve the aforesaid problems, the present invention provides a device that senses an abnormality of a plant or facility, and that includes: a data preprocessing unit that inputs a sensor signal, which is intermittently outputted from a sensor attached to the plant or facility, and event signals associated with the initiation and termination respectively of an activation sequence or suspension sequence of the plant or facility during the same period as a period during which the sensor signal is outputted, cuts a sensor signal, which is associated with a section between the event signal of the initiation of the activation sequence or suspension sequence and the event signal of the termination thereof, from the inputted sensor signal, and synchronizes the cut sensor signal with times that are obtained with the event signal of the initiation of the activation sequence or suspension sequence as an origin; a probability distribution estimation unit that estimates signal values at certain times of the sensor signal, which is processed by the data preprocessing unit, and probability distributions thereof; a feature quantity extraction unit that extracts a feature quantity on the basis of the probability distributions estimated by the probability distribution estimation unit; an abnormality detector that detects an abnormality of the plant or facility on the basis of the feature quantity extracted by the feature quantity extraction unit; and an input/output unit that has a screen on which information to be inputted or outputted is displayed, and displays on the screen information concerning the abnormality of the plant or facility detected by the abnormality detector.
According to the present invention, sensor data items that cannot be acquired due to a restriction, which is imposed on equipment, in an ever-changing scene are densely estimated in order to grasp an abnormality occurring in the scene. Therefore, an abnormality occurring during an ever-changing sequence can be sensed.
According to the present invention, sensor data items that cannot be acquired are estimated. Therefore, a time lag between sensor data items acquired at different times during the same sequence, which occurs because sampling times of a sensor are not synchronous with the initiation of the sequence, can be resolved. In addition, a time lag between sensor data items of different sensors which occurs because the data items are not acquired with multidimensional sensors synchronized with each other can be resolved. Accordingly, a statistical-probability property of a sensor wave at an arbitrary time during the sequence period can be monitored.
Accordingly, a system capable of both highly sensitively sensing and easily explaining an abnormality of any of various facilities and components, which include not only a facility such as a gas turbine or steam turbine but also a water wheel at a hydroelectric power plant, a reactor at a nuclear power plant, a windmill at a wind power plant, an engine of an aircraft or heavy equipment, a railroad vehicle or track, an escalator, and an elevator, and deterioration or a service life of a battery incorporated in equipment or a component can be realized.
The present invention relates to a facility status monitoring method and facility status monitoring device that sense a malfunction of a facility or a sign of the malfunction occurring when a sequence for an ever-changing activation or suspension is implemented at the facility such as a plant. Herein, times are adjusted with respect to the initiation time of the sequence, estimation times for sensor data items to be intermittently outputted are determined, and sensor data items to be observed at the times are estimated. Thus, an abnormality is sensed based on probability distributions obtained at the respective times in consideration of a time-sequential transition.
An example of the present invention will be described below in conjunction with the drawings.
The system includes an abnormality sensing system 10 that senses an abnormality on receipt of sampling sensor data items 1002 and event data items 1001, which are outputted from a facility 101 or database 111, and a user instruction 1003 entered by a user, a storage medium 11 in which a halfway outcome or an outcome of abnormal sensing is stored, and a display device 12 on which the halfway outcome or the outcome of abnormal sensing is displayed.
The abnormal sensing system 10 includes a data preprocessing unit 102 that processes data, an estimation time determination unit 112 that determines sensor data estimation times after the data preprocessing unit 102 processes sensor data items 1002 and event data items 1001 fed from the database 111, a sensor data estimation unit 103 that estimates sensor data items to be observed at the times determined by the sensor data estimation time determination unit 112 after the data preprocessing unit 102 processes sensor data items 1002 and event data items 1001 fed from the facility 101, a statistical probability distribution estimation unit 104 that estimates statistical probability distributions to be obtained at the times, a feature quantity extraction unit 105 that extracts a feature quantity using the statistical probability distributions, a learning unit 113 that performs learning using the feature quantity extracted by the feature quantity extraction unit 105, and an abnormality sensing unit 106 that senses an abnormality using a normal space or decision boundary 1004 outputted from the learning unit 113 after completion of learning.
Further, the data preprocessing unit 102 includes an event data analysis block 1021 that retrieves an initiation time of a user-specified sequence from among event data items 1001, a sensor data cutting block 1022 that calculates initiation and termination times, which are used to cut sensor sampling data items from among sensor data items 1002 received using information on the initiation time of the specified sequence, and cuts sensor data items 1002, and a sensor data time adjustment block 1023 that adjusts the times of the cut sensor data items.
The learning unit 113, decision boundary 1004, and abnormality sensing unit 106 constitute a discriminator 107 (107′).
Actions of the present system fall into three phases, that is, an estimation time determination phase in which sensor data estimation times are determined using data items accumulated in the database 111, a learning phase in which the normal space or decision boundary 1004 to be employed in abnormality sensing is determined using the accumulated data items, and an abnormality sensing phase in which abnormality sensing is actually performed based on the normal space or decision boundary using the sensor data items inputted after being corrected at estimation times. Fundamentally, the two phases of the estimation time determination phase and learning phase are pieces of offline processing, while the third phase of the abnormality sensing phase is online processing. However, abnormality sensing may be performed as offline processing. Hereinafter, these phases may be distinguished from one another by mentioning merely estimation time determination, learning, and abnormality sensing respectively.
A solid-line arrow 100 in
The facility 101 that is an object of state monitoring is a facility or plant such as a gas turbine or steam turbine. The facility 101 outputs sensor data 1002 representing the state and event data 1001.
In the present example, processing of the estimation time determination phase is first performed offline, and processing of the learning phase is thereafter performed offline using an outcome of the processing of the estimation time determination phase. Thereafter, the online processing of the abnormality sensing phase is performed using the outcome of the processing of the estimation time determination phase and an outcome of the learning phase.
Sensor data items 1002 are multidimensional time-sequential data items acquired from each of plural sensors, which are attached to the facility 101, at regular intervals. The number of sensors may range from several hundreds of sensors to several thousands of sensors which depends on the size of the facility or plant. The type of sensors may include, for example, a type of sensing the temperature of a cylinder, oil, or cooling water, a type of sensing the pressure of the oil or cooling water, a type of sensing the rotating speed of a shaft, a type of sensing a room temperature, and a type of sensing a running time. The sensor data may not only represent an output or state but also be control data with which something is controlled to attain a certain value.
A flow of processing for estimation time determination will be described below in conjunction with
More particularly, the event data analysis block 1021 of the data preprocessing unit 102 inputs the event data items 1001 outputted from the database 111 and the user instruction 1003 (S131), and retrieves the initiation time of a sequence, which is specified with the user instruction 1003, from among the inputted event data items 1001 (S132). The sensor data cutting block 1022 inputs the sensor data items 1002 outputted from the database 111 (S134), calculates the sensor data cutting initiation time, which is associated with the sequence initiation time obtained by the event data analysis block 1021, and the sensor data cutting termination time, and cuts sensor data items from among the sensor data items 1002 inputted from the database 111 (S135).
Thereafter, the cut sensor data items are sent to the sensor data time adjustment block 1023, have the times thereof adjusted by the sensor data time adjustment block 1023 (S136), and are sent to the estimation time determination unit 112 in order to determine sensor data estimation times (S137). The determined estimation times are preserved or outputted (S138).
A flow of processing for learning will be described below in conjunction with
In
The sensor data cutting block 1022 inputs the sensor data items 1002 outputted from the database 111 (S104), calculates the sensor data cutting initiation time, which is associated with the sequence initiation time obtained by the event data analysis block 1021, and the sensor data cutting termination time, and cuts sensor data items from among the sensor data items 1002 inputted from the database 111 (S105). The sensor data time adjustment block 1023 adjusts the times of the cut sensor data items (S106).
Thereafter, learning is performed using sensor data items that have times thereof adjusted. The sensor data estimation times outputted from the estimation time determination unit 112 are inputted to the sensor data estimation unit 103 (S103). Based on the information on the inputted sensor data estimation times, the sensor data estimation unit 103 estimates the times of sensor data items (S107). Thereafter, the statistical probability distribution estimation unit 104 estimates statistical probability distributions of the sensor data items having the times thereof estimated (S108). Based on the estimated statistical probability distributions, the feature quantity extraction unit 105 extracts the feature quantity of the estimated sensor data items (S109).
Finally, when the single-class discriminator 107 is employed as described in
In contrast, when the multi-class discriminator 107′ is employed as described in
Next, a flow of processing for abnormality sensing to be performed on newly observed sensor data items will be described below in conjunction with
The sensor data cutting block 1022 inputs the sensor data items 1002 outputted from the facility 101 (S124), calculates the sensor data cutting initiation time, which is associated with the sequence initiation time obtained by the event data analysis block 1021, and the sensor data cutting termination time, and cuts sensor data items (S125). The sensor data time adjustment block 1023 adjusts the times of the cut sensor data items (S126).
Thereafter, the sensor data estimation times determined and preserved in advance by the estimation time determination unit 112 during learning are inputted into the sensor data estimation unit 103 (S123). The sensor data estimation unit 103 estimates sensor data items at the sensor data estimation times, which are inputted from the estimation time determination unit 112, in relation to the sensor data items that have the times thereof adjusted and are inputted from the sensor data time adjustment block 1023 (S127). The statistical probability distribution estimation unit 104 estimates the statistical probability distributions of the estimated sensor data items (S128), and the feature quantity extraction unit 105 extracts a feature quantity on the basis of the estimated statistical probability distributions (S129).
Finally, using the feature quantity extracted by the feature quantity extraction unit 105, and the normal space or decision boundary 1004 created by the learning unit 113 of the discriminator 107 (107′), the abnormality sensing unit 106 performs abnormality discrimination (S130), and outputs or displays an outcome of sensing (S131).
Next, actions of the components mentioned in
[Determination of Cutting Initiation and Termination Times]
In the sensor data cutting block 1022, first, sensor data cutting initiation and termination times are calculated. Then, sensor data items observed between the times are cut by using the cutting initiation and termination times.
Next, a flow of processing of calculating cutting initiation and termination times for the purpose of cutting sensor data items, and cutting initiation and termination discrimination indices will be described below in conjunction with
In a case where calculation of a cutting initiation time is automated, a window is used to cut partial sensor data items (S206), an initiation discrimination index is calculated (S207), and initiation is discriminated (S208). If a No decision is made, the window is moved in a direction in which the time augments (S209), and initiation discrimination (S206 to S208) is repeated. If a Yes decision is made, the sensor data cutting initiation time is outputted or preserved (S211).
In contrast, if automatic calculation is not performed, the initiation time of a specified sequence is regarded as a sensor data cutting initiation time (S210), and the sensor data cutting initiation time is outputted (S211).
After the sensor data cutting initiation time is calculated, calculation of a sensor data cutting termination time is performed. The calculation of the sensor data cutting termination time is begun on receipt of the cutting initiation time obtained at S211 and the outcome of determination on whether a termination mode is automated or not automated which is obtained at S202 (S212).
If the calculation of the cutting termination time is automatically performed, sensor data items having been observed since the cutting initiation time are concerned, and part of the sensor data items is cut using a window (S213). A termination discrimination index is calculated (S214), and termination discrimination is performed (S215). If a No decision is made, the window is moved in a direction in which the time augments (S216), and termination discrimination (S213 to S215) is repeated. If a Yes decision is made, the sensor data cutting termination time is outputted or preserved (S218).
If automatic calculation is not performed, a time when a predetermined number of sensor data items has been observed since the sensor data cutting initiation time is regarded as a sensor data cutting termination time (S217), and the sensor data cutting termination time is outputted (S218).
[Adjustment of Times of Sensor Data Items]
Processing in the sensor data time adjustment block 1023 is performed using the cutting initiation time obtained by the sensor data cutting block 1022.
Thus, a corrected sensor data stream (c) having undergone time adjustment ensues.
[Determination of Sensor Data Estimation Times]
Referring to
In the present invention, an intensity evaluation index of time-sequential data items is defined to be quantized depending on whether a frequency of a time-sequential wave is high or low, or a magnitude of a rise or fall of the time-sequential wave. In other words, if the frequency of the time-sequential wave is high or the magnitude of the rise or fall of the time-sequential wave is large, intensity is large. In contrast, if the frequency of the time-sequential wave is low or the magnitude of the rise or fall of the time-sequential wave is small, the intensity is small.
More particularly, for example, Fourier analysis is performed on partial data items, which are cut with a window, in order to calculate a power spectrum. A frequency relevant to a maximum value of the power spectrum is regarded as the frequency of the data stream. A frequency of the data stream normalized with a certain maximum frequency is regarded as an intensity Ifreq in terms of a frequency. A maximum value of a difference between adjoining ones of the data items is normalized with a difference of certain maximum data, and the resultant value is regarded as an intensity I|Δy| in terms of a difference of data. As for the difference of a certain frequency or certain data, for example, a maximum value statistically calculated using all sensor data items may be utilized. However, the present invention is not limited to the maximum value. The intensity of the data stream is calculated according to a formula below.
[Math. 2]
I=max(Ifreq(freq),I|Δy|(|Δy|)) (2)
As for the intensity evaluation index, any other definition may be adopted.
The relational expression between the intensity evaluation index and sampling interval is obtained separately by conducting in advance experiments or simulation (S404). As shown in the drawing, a maximum value of the sampling interval is a sampling interval for data acquisition, and a minimum value is one second. The intensity evaluation index and sampling interval have an inversely proportional relationship.
As for the determination of sensor data estimation times, the sensor data estimation time may be determined at intervals of a predetermined certain time. Alternatively, the estimation time may be determined at regular intervals so that a specified number of sensor data items can be estimated.
As mentioned above, by determining sensor data estimation times, a processing cost can be reduced and processing can be highly efficiently carried out.
[Estimation of Sensor Data Items]
Referring to
y(x) is calculated according to formula (3).
In a second example shown in
where α denotes a weight coefficient, and a is obtained using x, which has undergone higher-order mapping, according to a formula below.
A higher-order mapping function employed is expressed as follows:
where λ denotes an experimentally determined coefficient.
Further, β1i, and β2i denote weight coefficients that are calculated based on a variance among peripheral acquired sensor data items.
For estimation of sensor data, a spline method and bi-cubic method are available. Any of some techniques may be adopted or the techniques may be switched for use. For switching, for example, an intensity index is employed.
If different techniques are employed in estimating sensor data items within a section partitioned with acquired sensor data items, a displacement of data like a vertical step takes place at a point where the techniques are switched.
As mentioned above, corrected sensor data y′(x) within the correction space is obtained according to a formula below.
[Math. 7]
y′(x)=(1−w(x))y(xj−1)+w(x)y(xj+1)((xj−1)≦x≦(xj+1)) (7)
A weight coefficient w(x) is calculated as follows:
As mentioned above, by performing sensor data estimation, data items that cannot be acquired due to a restriction imposed on equipment can be estimated. In particular, a severe change in a sequence preventing acquisition of data items can be reproduced.
[Estimation of Statistical Probability Distributions]
Estimation of statistical probability distributions to be performed by the statistical probability distribution estimation unit 104, that is, a method of estimating probability distributions at respective estimation times using estimated values of sensor data items supposed to be acquired at different times within each of the same specified sequences will be described below in conjunction with
An example shown in
In contrast, an example shown in
The aforesaid estimation of a statistical probability distribution G makes it possible to grasp a distributing situation of sensor data at each time. In addition, as for sensor data newly observed at each time, what is a ratio of normality to abnormality can be discerned.
[Extraction of a Feature Quantity]
A flow of feature quantity extraction processing to be performed by the feature quantity extraction unit 105 will be described below in conjunction with
Thereafter, a degree of abnormality v(t) is calculated using the statistical probability distribution G at each estimation time according to formula 10 below (S702).
[Math. 11]
v(t)=1−G(x;μ,σ) (11)
A sequence convergence time obtained through discrimination of convergence of a sensor wave to be performed by the feature quantity extraction unit 105 as described later is inputted (S703). A likelihood that is a feature quantity is calculated by accumulating the degree of abnormality v from a sensor data cutting initiation time to the sequence convergence time by using formula 12 (S704).
The processing from S701 to S703 is performed with respect to all sensors. Finally, likelihoods concerning all the sensors are integrated in order to obtain a likelihood histogram expressed by formula 13 below. Subscripts S1 to Sn denote sensor numbers.
The sequence convergence time inputted at S703 mentioned in
What is referred to as a sequence convergence time is a time at which after a sequence is begun, sensor data items observed within the sequence begin to converge on a certain value, or a time when the sensor data items begin oscillating with a certain value around a constant value.
[GUI]
Next, a GUI to be employed in performing pieces of processing will be described in conjunction with
The GUI includes: a panel 900 on which feature quantities are displayed; a Reference button 9012 for use in selecting a folder that contains a set of files in which sensor data items, indices indicating whether the sensor data items are normal or abnormal, event data items, and parameters are preserved; an Input Folder box 9011 in which the selected folder is indicated; a Reference button 9022 to be depressed in order to select a folder that contains a set of files preserving a normal space (normality/abnormality decision boundary) received at the processing step S111 (S111′), determined estimation times received at the processing steps S138 and S409, cutting initiation and termination times received at the processing steps S211 and S218, a likelihood histogram into which feature quantities received at the processing step S705 are integrated, a sensor wave convergence time received at the processing step S806, and followings which are not shown in the drawing, estimated sensor data items received at the processing step S107 (S127), a halfway outcome such as extracted statistical probability distributions received at the processing step S108 (S128), and an outcome of abnormality sensing received at the processing step S131; an Output Folder box 9021 in which the selected folder is indicated; a Data Period Registration box 903 for use in registering data relevant to learning and an abnormality sensing test that are conducted currently; an Abnormality Sensing Technique selection box 904 for use in selecting a sensing technique; a Miscellaneous Settings button 905 to be depressed in order to designate details of abnormality sensing; an Execute Learning and Abnormality Sensing Test button 906 to be depressed in order to execute learning and an abnormality sensing test using data items read from the database 111; an Execute Abnormality Sensing button 907 to be depressed in order to perform abnormality sensing on data items fed from the facility 111; a Display Period box 908 in which a display period of an outcome of abnormality sensing is indicated; a Display Item box 909 for use in selecting display items such as display of feature quantities and an abnormal outcome; a Display Format box 910 for use in selecting two-dimensional display or three-dimensional display; a Display Outcome of Abnormality Sensing button 911 to be depressed in order to perform abnormality sensing on the basis of the display-related settings and display an outcome of abnormality sensing received at the estimation processing step S107 (S127); and a Display Halfway Outcome button 912 to be depressed in order to receive and display estimated sensor data items and statistical probability distributions which are included in a halfway outcome received at the statistical probability distribution step S108 (S128).
The Input Folder box 9011 and Output Folder box 9021 are used to select folders, the Data Period Registration box 903 is used to register a data period, the Abnormality Sensing Technique selection box 904 is used to select an abnormality sensing technique, and Miscellaneous Settings button 905 is used to enter miscellaneous settings. After these boxes and button are operated, the Execute Learning and Abnormality Sensing Text button 906 is depressed to execute learning processing described in
After the learning processing and abnormality sensing test processing are completed, a state in which the Execute Abnormality Sensing button 907 can be depressed ensues. In this state, the Display Outcome of Abnormality Sensing button 911 and Display Halfway Outcome button 912 can also be depressed. In this case, when a display period of learning data or abnormality sensing test data is registered in the Display Period box 908, and the Display Item box 909 and the Display Format box 910 are selected, by depressing the Display Outcome of Abnormality Sensing button 911 or Display Halfway Outcome button 912, the halfway outcome or the outcome of abnormality sensing that is available during the display period is displayed on the Display panel 900.
Thereafter, the Execute Abnormality Sensing button 907 is depressed. Accordingly, data items available during a period registered in the Data Period Registration box 903 are read from a storage medium for temporary data storage that is connected to the facility 101 but is not shown. When execution of abnormality sensing is completed, a display period of abnormality sensing data is registered in the Display Period box 908. After the display items are specified in the Display Item box 909 and the display format is specified in the Display format box 910, and the Display Outcome of Abnormality Sensing button 911 or Display Halfway Outcome button 912 is depressed, a halfway outcome of abnormality sensing data available during the display period or an outcome of abnormality sensing is displayed on the Display panel 900.
Before a display-related button is depressed, a progress of execution is displayed on the Display panel 900. For example, first, “Please designate settings.” is displayed. Once designation is begun, the message is immediately switched to “Designation is in progress.” After designation of the input folder and output folder, registration of a data period, and designation of an abnormality sensing technique are completed, when the Execute Learning and Abnormality Sensing Test button 906 is depressed, “Learning and an abnormality sensing test are in progress.” appears.
After execution of learning and an abnormality sensing test is completed, “Execution of learning and an abnormality sensing test has been completed. Depress the Execute Abnormality Sensing button 907 so as to perform abnormality sensing. Otherwise, designate the display-related settings, and depress the display button for display.” appears. If the Execute Abnormality Sensing button 907 is not depressed but designation of any of the display-related settings for Display Period, Display Item, and Display Format is begun, the message is switched to “Designation of the display-related setting is in progress.” When designation of the display-related setting is completed, “Designation of the display-related setting has been completed. Depress the display button for display.” appears.
When the Display Outcome of Abnormality Sensing button 911 or Display Halfway Outcome button 912 is depressed, an outcome of learning and an abnormality sensing test is displayed according to settings. In contrast, when the Execute Abnormality Sensing button 907 is depressed, “Execution of an abnormality sensing test is in progress.” appears. When execution of an abnormality sensing test is completed, “Execution of an abnormality sensing test has been completed. Designate the display-related settings.” appears. Once designation of any of the display-related settings is begun, the message is switched to “Designation of the display-related setting is in progress.” When designation of the display-related setting is completed, “Designation of the display-related setting has been completed. Depress the display button for display.” appears. When the Display Outcome of Abnormality Sensing button 911 or Display Halfway Outcome button 912 is depressed, an outcome of abnormality sensing is displayed according to settings.
The example shown in
By displaying the GUI like the one shown in
The GUI includes a Sequence Settings field 1001, a Sensor Data Estimation Settings field 1002, a Data Settings field 1003, a Discriminator Settings field 1004, a Designating Situation List display panel 1005, and a Preserve button 1006.
In the Sequence Settings field 1001, when an Edit button 10016 is depressed, all items can be edited. Editing items include Type of Sequence and Sequence Cutting. The Type of Sequence includes a box 10011 for use in selecting a type of sequence. The Sequence Cutting includes check boxes 100121 and 100123 which are used to indicate Yes for the items of sequence cutting initiation time automatic calculation and sequence cutting termination time automatic calculation, and boxes 100122 and 100124 which succeed the respective Yes boxes and are used to select an index to be employed in automatic calculation. The Type of Sequence selection box 1011 can be used to select a type of sequence such as activation or suspension for which an abnormality should be sensed. In the Sequence Cutting, whether the initiation and termination times are automatically calculated can be determined.
When automatic calculation is performed, the Yes check boxes 100121 and 100123 are ticked. In the index boxes 100122 and 100124, indices to be employed are specified. In case automatic calculation is not to be performed, the Yes check boxes are not ticked, and the use index selection boxes are left blank. In this case, default sequence cutting initiation and termination times are employed.
The example shown in
In the Sensor Data Estimation Settings field 1002, when an Edit button 10026 is depressed, all items can be edited. Editing items includes Estimation Technique, Parameter, and Estimation Interval. The Estimation Technique includes check boxes 100211, 100213, and 100215 for use in selecting a linear method, nonlinear method, and mixed method respectively, and boxes 100212, 100214, and 100216 for use in selecting detailed methods associated with the respective classification methods.
For selecting the estimation technique, any of the check boxes 100211, 100213, and 100215 for use in selecting the linear method, nonlinear method, and mixed method respectively of the Estimation Technique is ticked. The succeeding boxes 100212, 100214, or 100216 for use in selecting the associated technique is used to determine the estimation technique. The Parameter includes a selection box 100221 for use in selecting a kind of parameter, a box 100222 for use in entering concrete numerals of the selected parameter, and an Add button 100223 to be depressed in order to select another kind of parameter and enter other numerals after completion of selecting one kind of parameter and entering numerals.
The Estimation Interval includes a check box 10232 to be ticked when an estimation interval is Designated, and a box 100233 in which the estimation interval is entered when the Designated box is ticked. In case the estimation interval is not to be designated, the Designated check box is not ticked and the number of seconds is not entered in a succeeding space. In this case, normal learning data items are automatically used to determine estimation times according to the intensity of each sensor wave. In case the estimation interval is to be designated, the Designated check box is ticked, and the number of seconds is entered in the succeeding space. Accordingly, the estimation time is designated at intervals of the designated number of seconds.
In the example shown in
In the Data Settings field 1003, when an Edit button 10036 is depressed, all items can be edited. Editing items include Learning/evaluation Data Separation Designation and Exclusionary Data. Further, the Learning/evaluation Data Separation Designation includes a Yes check box 100311, a box 100312 in which a learning data period is entered when Yes is selected for designation, a box 100313 in which an evaluation data period is entered, a No check box 100321 to be ticked when No is selected for designation, and a box 100322 in which the number of folds employed in an evaluation technique for automatically separating learning data and evaluation data from each other is entered. The Exclusionary Data includes a Yes check box 100331, and a Data Registration box 100332 in which data is registered when Yes is selected.
In the example shown in
In the Discriminator Settings field 1004, when an Edit button 10046 is depressed, all items can be edited. Editing items include Type of Discriminator and Detailed Item. A Type of Discriminator box 10041 and Detailed Item box 10042 are associated with the respective editing items. The Type of Discriminator box 10041 enables selection of a type of discriminator. For example, a support vector machine, Bayes discriminator, k-nearest neighbor discriminator, neural network, and others are available. In the Detailed Item box 10042, a detailed item associated with a discriminator selected using the Type of Discriminator box 10041 can be selected. For example, as for the number of classes to be handled by the discriminator, a single class or multiple classes can be selected. If the single class is selected, learning is performed according to the processing flow for learning described in
At the time when the entered contents are inputted, the contents are automatically displayed in the Designating Situation List 1005. In a case where each of setting items is edited, “Being edited.” is displayed subsequently to the item name. When determination is made, if the Determine button 10016, 10026, 10036, or 10046 is depressed, “Being edited.” succeeding each item name is changed to “Determined.” If any item should be corrected, the Edit button in the field in which the setting item to be corrected is present is depressed for editing. After editing is completed, if the Determine button 10017, 10027, 10037, or 10047 in the field concerned is depressed, correction is completed.
After the contents of display in the Designating Situation List 1005 are verified, and the Preserve button 1006 is depressed, the contents of designation shown in
After the GUI shown in
A GUI concerned with checking of an estimated measurement curve that is a halfway outcome, a sensor model, and a statistical probability distribution at a certain time in the sensor model, which are obtained after performing learning, an abnormality sensing test, and abnormality sensing, will be described below in conjunction with
A GUI shown in
A date of display data is entered in a Date of Display Data box 11021. The contents of display are selected using a Contents of Display selection box 110221. Designation Property 110222 below the selection box is used to select the property of the contents of display. The Probability Distribution Display includes a check box 110231 to be ticked in the case of Yes. In the case of Yes, Designation Property 110232 for designation can be used.
Setting items relating to the graph are displayed in a field 11053. Display items encompass items designated through the GUI mentioned in conjunction with
In
Further, a time relevant to a statistical probability distribution that is requested to be seen is selected using a mouse (a position indicated with an arrow in the graph 1106), and the right button of the mouse is clicked in order to select display of the distribution. Then, the statistical probability distribution 1107 observed at the specified time is, as shown in
Owing to the GUI described in conjunction with
The invention devised by the present inventor has been concretely described based on the example. Needless to say, the present invention is not limited to the example, but may be modified in various manners without a departure from the gist of the invention.
Number | Date | Country | Kind |
---|---|---|---|
2012-188649 | Aug 2012 | JP | national |
Filing Document | Filing Date | Country | Kind |
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PCT/JP2013/068531 | 7/5/2013 | WO | 00 |