Patent Application
20180149551
The present invention relates to an oil leakage detection device for detecting a point of generation of oil leakage in a machine using an information processing means.
A machine tool is installed therein with a hydraulic pressure supply device for supplying oil to a plurality of drive units to clamp a spindle, table, and tool magazine or drive an APC (auto pallet changer).
While a hydraulic pressure is always applied to the hydraulic pipes 7, distribution units 6, and the like during operation, in the hydraulic pressure supply device 2 constructed in this manner, oil leakage may sometimes be generated at junctions 8 (represented by black spots in
Conventionally, if the generation of oil leakage is detected, it is necessary to stop the machine and make an investigation tracing a hydraulic path (ranging from the hydraulic unit 5 to drive units and including the hydraulic pipes 7, distribution units 6, and junctions 8). If there are a large number of drive units as supply destinations, however, the investigation of oil leakage that requires orderly tracing of the hydraulic pipes 7, which are intricately stretched around in the machine tool 1, imposes a heavy burden on the manager.
Possibly, this problem may be solved by detecting the oil flow rate by means of sensors, such as flowmeters, attached to various parts of the hydraulic path and identifying the place of generation of oil leakage based on a change of the detected flow rate. In this case, however, there arises a problem that if the sensors are attached to all possible parts of the hydraulic path, troubles due to management (failure handling/adjustment) of the sensors increase, rather resulting in an increase in running cost.
Accordingly, the object of the present invention is to provide an oil leakage detection device for detecting a point of generation of oil leakage in a machine.
As described above, it is not realistic to attach sensors for flow rate monitoring to all possible parts of a hydraulic path, due to a probable increase in cost. In the present invention, a sensor for detecting the flow rate is attached to a proximal part of the hydraulic path and used to acquire, as a change pattern, a change of the flow rate of oil caused when drive units of a machine tool are activated. The acquired change pattern is compared with a change pattern in a normal state (free from oil leakage), and a point of generation of oil leakage is identified based on the result of the comparison. A change pattern at the time of the oil leakage generation cannot be determined by a simple method, since it is affected by errors due to the respective connection states of various parts of the hydraulic path, disturbances such as loads on the drive units, and the like. According to the present invention, therefore, the change pattern at the time of the oil leakage generation is learned by machine learning so that the point of oil leakage generation can be identified by using the result of the learning.
An embodiment of an oil leakage detection device according to the present invention is configured to detect generation of oil leakage in a hydraulic pressure supply device for supplying oil to activate at least one drive unit of a machine tool, and comprises a state observation unit configured to acquire, as a change pattern, information on a change of the flow rate of oil supplied by the hydraulic pressure supply device corresponding to operating conditions of the drive unit, among log data recorded during operation of the machine tool, and create input data based on the acquired change pattern, a label acquisition unit configured to acquire information on the presence of generation of oil leakage in the oil leakage detection device and information on a point of generation of oil leakage in the oil leakage detection device, among the log data recorded during the operation of the machine tool, and create teacher data based on the acquired information on the presence of generation of oil leakage and information on the generation point of oil leakage in the oil leakage detection device, a learning unit configured to perform supervised learning based on the input data created by the state observation unit and the teacher data created by the label acquisition unit, thereby constructing a learning model, and a learning model storage unit configured to store the learning model.
Another embodiment of the oil leakage detection device of the present invention is configured to detect generation of oil leakage in a hydraulic pressure supply device for supplying oil to activate at least one drive unit of a machine tool, and comprises a learning model storage unit configured to store a learning model constructed by supervised learning using input data based on a change pattern indicative of information on a change of the flow rate of oil supplied by the hydraulic pressure supply device corresponding to operating conditions of the drive unit and teacher data based on information on the presence of generation of oil leakage in the oil leakage detection device and information on a point of generation of oil leakage in the oil leakage detection device, a state observation unit configured to create input data based on the change pattern indicative of the information on the change of the flow rate of oil supplied by the hydraulic pressure supply device corresponding to the operating conditions of the drive unit during operation of the machine tool, and a detection unit configured to detect the presence of generation of oil leakage in the oil leakage detection device and the generation point of oil leakage in the oil leakage detection device from the input data created by the state observation unit by using the learning model.
Another embodiment of the oil leakage detection device of the present invention is characterized by comprising an alarm unit configured to output an alarm based on the result of detection of the presence of generation of oil leakage in the oil leakage detection device and the generation point of oil leakage in the oil leakage detection device.
According to the present invention, a point of generation of oil leakage can be automatically identified by only attaching a sensor to a proximal part of a hydraulic path, so that generation of oil leakage, if any, can be immediately overcome and the running cost of a machine tool can be reduced.
The above and other objects and features of the present invention will be obvious from the ensuing description of embodiments with reference to the accompanying drawings, in which:
Embodiments of the present invention will now be described with reference to the accompanying drawings.
First, an outline of oil leakage location processing performed by an oil leakage detection device of one embodiment according to the present invention will be described with reference to
The log data storage unit 200 is recorded, as log data, with the operating conditions of the individual drive units, including a spindle 9, the table 10, the tool magazine, and the APC 11, established when the machine tool 1 illustrated in
In the machine tool 1, its drive units are controlled to machine a workpiece, and log data on the operation of the machine tool 1 are created in response to signals obtained from the various parts, based on the states of the drive units, detected values of the sensor 12, and the like, and are stored in a non-volatile memory (not shown) of the machine tool 1. The log data are created so that time series can be ascertained for the operating conditions of the drive units, the detected values of the oil flow rate detected by the sensor 12, and the like. Moreover, the log data include various pieces of information (the presence of generation of oil leakage, generation point of oil leakage, etc.) input through a machine control panel (not shown) by the manager who operates the machine tool 1 or a maintenance personnel who deals with the occurrence of abnormality, if any, in the machine tool 1. The log data thus stored in the non-volatile memory (not shown) of the machine tool 1 are collected into the log data storage unit 200 through the network or an external storage device or the like carried by an operator such as the maintenance personnel of the machine tool. This collection may be performed either every time the log data are created or regularly at appropriate intervals.
An outline of the learning performed by the oil leakage detection device 13 will now be described before describing various configurations of the oil leakage detection device 13.
The “input data and teacher data for the case where oil leakage is generated during operation” are created based on the log data obtained before and after the point in time when the generation of oil leakage is detected by the manager or maintenance personnel of the machine tool, among the log data stored in the log data storage unit 200. If the generation of oil leakage is detected during the operation of the machine tool 1, for example, information such as “oil leakage generated: APC” is input through the machine control panel (not shown) of the machine tool 1 by the manager or maintenance personnel of the machine tool and recorded together with time information into the log data. In this case, the “input data for the case where oil leakage is generated during operation” is created based on the log data obtained before the point in time when the “oil leakage generated: APC” is input. The input data used for the machine learning by the oil leakage detection device 13 of the present embodiment is data indicative of a difference between the reference change pattern of the oil flow rate in the normal state and the change pattern of the oil flow rate obtained when oil leakage is generated. This data should just be created based on the change pattern of the oil flow rate in the abnormal state, which is created by combining the values of oil flow rate changes detected by the sensor 12 when the immediate past drive units are activated since the point in time when the generation of oil leakage is detected, and a change pattern of the oil flow rate in the normal state as a predetermined reference. In the case of this example, the “teacher data for the case where oil leakage is generated during operation” is the “oil leakage generated: APC”.
On the other hand, the “input data and teacher data for the case where oil leakage is not generated during operation” are created based on the log data obtained before and after the point in time when oil leakage is not generated, among the log data stored in the log data storage unit 200. For example, the “input data for the case where oil leakage is not generated during operation” sampled at random or at predetermined time intervals should just be used, among the log data obtained when the operation of the machine tool 1 is safely finished without the detection of the generation of oil leakage during the operation. The input data used for the machine learning by the oil leakage detection device 13 of the present embodiment is data indicative of a difference between the reference change pattern of the oil flow rate in the normal state and the change pattern of the oil flow rate obtained at the time of sampling. This data should just be created based on the change pattern of the oil flow rate in the normal state, which is created by combining the values of oil flow rate changes detected by the sensor 12 when the immediate past drive units are activated since the time of sampling, and a change pattern of the oil flow rate in the normal state as a predetermined reference. (It is to be noted that the sampled change pattern of the oil flow rate in the normal state is not always coincident with the reference change pattern of the oil flow rate in the normal state, due to various variation factors such as disturbances.) In the case of this example, the “teacher data for the case where oil leakage is not generated during operation” is “no oil leakage”.
The following is a description of the various configurations of the oil leakage detection device 13. The oil leakage detection device 13 comprises a learning unit 111, state observation unit 112, label acquisition unit 113, and learning model storage unit 114.
The learning unit 111 is a function means that performs the supervised learning based on input data acquired by the state observation unit 112 and teacher data (also called a label) acquired by the label acquisition unit 113 and constructs and stores a learning model into the learning model storage unit 114. The learning model constructed by the learning unit 111 is used to detect the generation of oil leakage, as described later. The learning model constructed by the learning unit 111 may be any model that can detect the generation of oil leakage from the input data described with reference to
The state observation unit 112 creates the input data among the log data stored in the log data storage unit 200 and outputs the created input data to the learning unit 111. In the oil leakage detection device 13 of the present embodiment, the input data are difference values between the change patterns of the oil flow rates and are represented by numeric columns. Therefore, the state observation unit 112 can use these numeric columns directly as input data for the learning unit 111. If the data used as the input data are represented by character strings or any information other than numerical values, the state observation unit 112 may previously store a memory (not shown) with a conversion table for conversion of the character strings to numerical values so that information other than numerical values can be quantified and included in the input data by using the conversion table.
The label acquisition unit 113 creates teacher data (the presence of generation of oil leakage and generation point of oil leakage) corresponding to the input data created by the state observation unit 112 in synchronism with this creation, based on the log data stored in the log data storage unit 200, and outputs the created teacher data to the learning unit 111. If the multi-layer neural network shown in
With the configurations described above, the oil leakage detection device 13 can promote learning of the state of oil leakage relative to the operating conditions of the individual drive units during the operation of the machine tool 1, thereby constructing the learning model.
The following is a description of the oil leakage detection device 13 for detecting the generation of oil leakage by using the constructed learning model.
The oil leakage detection device 13 comprises the state observation unit 112, the learning model storage unit 114, and a detection unit 115.
The state observation unit 112 acquires the data used as the input data during the learning process described above through the PMC 17 while the operation of the machine tool 1 is being started, and creates input data based on the acquired information and outputs them to the detection unit 115. The state observation unit 112 creates change patterns of the oil flow rates by storing the oil leakage changes detected by the sensor 12 when the drive units of the machine tool 1 are activated in association with the individual drive units. Then, differences between the reference change pattern of the oil flow rate in the normal state and the created change patterns of the oil flow rates are output as input data to the detection unit 115.
Based on the input data input from the state observation unit 112, the detection unit 115 performs detection of the generation of oil leakage using the learning model stored in the learning model storage unit 114.
In this way, the detection unit 115 detects the generation of oil leakage based on the oil flow rate changes of the hydraulic pressure supply device 2 relative to the operating conditions of the drive units of the machine tool 1 and outputs detected values as the result of the detection to the PMC 17 of the machine tool 1. If the detected values for the generation of oil leakage input from the detection unit 115 are indicative of the generation of oil leakage, the PMC 17 commands the alarm unit 18 to generate an alarm.
When commanded to generate an alarm from the PMC 17, the alarm unit 18 notifies the operator of the generation of oil leakage and the point of the oil leakage generation by means of a lamp on the machine control panel (not shown) of the machine tool 1, display of a display device, a sound, or the like. The alarm unit 18 may be configured to command various parts of the machine tool 1 to stop their operations instead of generating an alarm.
Thus, the oil leakage detection device 13 can detect the generation of oil leakage during the operation of the machine tool 1 by using the learning model obtained as a result of learning based on information on oil leakage generated in at least one machine tool 1. If the oil leakage detection device 13 detects the generation of oil leakage, the machine tool 1, on receiving the result of the detection, gives an alarm to the operator to urge him/her to stop the operation of the machine tool 1 so that the occurrence of a malfunction or failure can be prevented.
While an embodiment of the present invention has been described herein, the invention is not limited to the above-described embodiment and may be suitably modified and embodied in various forms.
The above-described embodiment shows a form of learning and use for the single the oil leakage detection device 13. Since the learning model itself, which is constructed by the learning unit 111 and stored in the learning model storage unit 114, is a set of data indicative of the result of learning, however, it can be configured to be shared in common with another oil leakage detection device 13 by means of an external storage device (not shown), network, or the like. In the learning process, with this configuration, the time required for the completion of learning can be reduced in such a manner that a plurality of oil leakage detection devices 13 perform learning in parallel with one another with the single learning model shared by the oil leakage detection devices 13. For the use of the learning model, on the other hand, the shared learning model can be used to enable the individual oil leakage detection devices 13 to detect the generation of oil leakage. The method of sharing the learning model is not limited to a specific one. For example, the learning model may be previously stored in a host computer in a factory so that it can be shared by the oil leakage detection devices 13. Alternatively, the learning model may be previously stored on a server installed by a manufacturer so that it can be shared by the oil leakage detection devices 13 of customers.
Moreover, the configurations of the oil leakage detection device 13 for the learning and detection are described separately in connection with the above-described embodiment. Alternatively, however, the oil leakage detection device 13 may be simultaneously provided with the configurations for the learning and detection. In this case, the oil leakage detection device 13 can be configured so that the learning unit 111 performs additional learning based on the information input by the manager or maintenance personnel of the machine tool 1 as the generation of oil leakage is detected.
Furthermore, although the above-described embodiment shows an example in which the oil leakage detection device 13 is constructed separately from the machine tool 1, the oil leakage detection device 13 may alternatively be constructed as a part of a control device of the machine tool 1.
While an embodiment of the present invention has been described herein, the invention is not limited to the above-described embodiment and may be suitably modified and embodied in other forms.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2016-229258 | Nov 2016 | JP | national |
