ABNORMALITY DETERMINATION DEVICE

Provided is an abnormality determination device capable of correctly determining an abnormality even when data having different environmental temperatures is mixedly present. This abnormality determination device includes: a normal data storage unit; a diagnostic data storage unit; a compensation value derivation unit that obtains a feature amount of normal data at each environmental temperature and derives, as a compensation value, statistics obtained from the feature amount; a compensation value interpolation unit that uses at least two compensation values for the environmental temperature to obtain, by interpolation, the compensation value of the environmental temperature when diagnostic data is acquired; a normal data compensation unit that compensates the feature amount of the normal data using the compensation value; a learning unit that learns, as learning data, the compensated feature amount of the normal data and constructs a learning model; a diagnostic data compensation unit that compensates a feature amount of the diagnostic data using the compensation value; and a machine abnormality determination unit that determines whether or not the diagnostic data is normal on the basis of the degree of deviation between the compensated feature amount of the diagnostic data and the learning model.

FIELD OF THE INVENTION

The present invention relates to an abnormality determination device.

BACKGROUND OF THE INVENTION

There is known a device that determines abnormality of an industrial apparatus such as a machine tool. For example, PTL1describes a device that calculates, by a machine learning method, a degree of divergence between data acquired when an industrial apparatus normally operates, and data acquired from an industrial apparatus that is a target of diagnosis, and determines abnormality, based on the obtained degree of divergence.

PATENT LITERATURE

SUMMARY OF THE INVENTION

FIG.3AtoFIG.3Dillustrate examples of torque command waveforms acquired from a machine.FIG.3AtoFIG.3Dillustrate torque waveforms in cases where an identical torque command is applied to an electric motor that drives a certain shaft of the machine. Specifically,FIG.3Aillustrates a torque command waveform201in a case where an environmental temperature is low and the machine is normal,FIG.3Billustrates a torque command waveform202in a case where the environmental temperature is low and the machine is abnormal,FIG.3Cillustrates a torque command waveform203in a case where the environmental temperature is high and the machine is normal, andFIG.3Dillustrates a torque command waveform204in a case where the environmental temperature is high and the machine is abnormal.

Referring toFIG.3AtoFIG.3D, it can be understood that the torque command waveform201at the normal time at the low temperature and the torque command waveform202at the abnormal time at the low temperature are distinguishable from features of waveforms (a maximum value, a minimum value, Peak to Peak (P2P), and the like). It can also be understood that the torque command waveform203at the normal time at the high temperature and the torque command waveform204at the abnormal time at the high temperature are distinguishable from the features of waveforms. In other words, when the environmental temperature is identical between the normal time and the abnormal time, the abnormality of the machine can be determined by calculating the degree of divergence between the torque command waveforms. However, since the torque command waveform202at the abnormal time at the low temperature and the torque command waveform203at the normal time at the high temperature are similar, a sufficient degree of divergence does not appear between the torque command waveforms, and there is a possibility that the abnormality of the machine cannot be determined.

According to one mode of the present disclosure, an abnormality determination device that determines abnormality of a machine includes a normal data storage unit configured to correlate and store normal data that is data relating to a state of the machine at a time when the machine normally operates, and an environmental temperature of the machine at a time when the normal data is acquired; a diagnosis data storage unit configured to correlate and store diagnosis data that is data relating to the state of the machine at a time of diagnosing the machine, and the environmental temperature of the machine at a time when the diagnosis data is acquired; a compensation value deriving unit configured to calculate a predetermined feature at each of the environmental temperatures in regard to the normal data stored in the normal data storage unit, and derive a statistical quantity obtained from the calculated feature at each of the environmental temperatures, as a compensation value at each of the environmental temperatures; a compensation value interpolation unit configured to calculate, by interpolation, the compensation value in regard to the environmental temperature at a time when the diagnosis data is acquired, by using the compensation values in regard to at least two environmental temperatures with respect to which the normal data is included in the normal data storage unit; a normal data compensation unit configured to compensate the feature of the normal data by using the compensation value at each of the environmental temperatures; a learning unit configured to construct a learning model by performing learning by using the feature of the compensated normal data as training data at a normal time; a diagnosis data compensation unit configured to compensate the feature of the diagnosis data by using the compensation value calculated by the compensation value interpolation unit; and a machine abnormality determination unit configured to determine whether the diagnosis data is normal, based on a degree of divergence between the feature of the compensated diagnosis data and the learning model.

As described above, according to the present embodiment, exact diagnosis can be performed by calculating a compensation value from normal data in accordance with an environmental temperature, and compensating data relating to a state of a machine.

From a detailed description of typical embodiments of the present invention illustrated in the accompanying drawings, the objects, features and advantageous effects of the present invention, and other objects, features and advantageous effects of the invention, will be clearer.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Next, embodiments of the present disclosure will be described with reference to the accompanying drawings. In the drawings to be referred to, similar structural parts or functional parts are denoted by the same reference numerals. These drawings use different scales as appropriate to facilitate understanding. The mode illustrated in each drawing is one example for carrying out the present invention, and the present invention is not limited to the modes illustrated in the drawings.

FIG.1is a block diagram illustrating a hardware configuration of an abnormality determination device10according to an embodiment. As illustrated inFIG.1, the abnormality determination device10includes a configuration in which a CPU1, a ROM2, a RAM3and a nonvolatile memory (flash memory, HDD, or the like)4are interconnected via a bus9. In addition, a display device (a liquid crystal display, or the like)21is connected to the bus9via an interface11, an input device (a keyboard, a mouse, or the like)22is connected to the bus9via an interface12, and a machine learning device24is connected to the bus9via an interface14. The abnormality determination device10, display device21and input device22can be implemented by general personal computers. A machine50is connected to the abnormality determination device10via an external interface13. As will be described below in detail, the abnormality determination device10acquires data (hereinafter, also referred to as “state data”) relating to the state of the machine50, and executes abnormality determination of the machine50.

FIG.2is a functional block diagram of the abnormality determination device10. The abnormality determination device10includes an environmental temperature acquisition unit101that acquires an environmental temperature of the machine50directly from the machine50, or via the input device22; and a state data acquisition unit102that acquires data relating to the state of the machine50. The abnormality determination device10further includes a normal data storage unit113that correlates and stores the state data of the machine50at a normal operation time, and the environmental temperature of the machine50at the time when the state data is acquired; a compensation value deriving unit105that calculates a predetermined feature at each of environmental temperatures in regard to the state data stored in the normal data storage unit113, and derives a statistical quantity calculated from the calculated feature at each of the environmental temperatures, as a compensation value at each environmental temperature; and a normal data compensation unit103that compensates the feature of the state data in regard to each environmental temperature, by using the compensation value derived by the compensation value deriving unit105. Moreover, the abnormality determination device10includes a diagnosis data storage unit114that correlates and stores the state data at a time of diagnosing the machine50and the environmental temperature of the machine50at a time when the state data is acquired; a compensation value interpolation unit104that calculates, by interpolation, a compensation value at the environmental temperature at a time when the diagnosis data of the machine50is acquired, by using the compensation values relating to at least two environmental temperatures with respect to which the state data is included in the normal data storage unit113; and a diagnosis data compensation unit106that compensates the feature of the state data at the time of executing diagnosis, by using the compensation value derived by the compensation value interpolation unit104.

Besides, the abnormality determination device10includes a machine learning device24. The machine learning device24includes a learning unit242that constructs a learning model by performing learning by using the feature of the state data compensated by the normal data compensation unit103as training data at a normal time; a learning model storage unit241that stores the learning model constructed by the learning unit242; and a machine abnormality determination unit243that determines whether the diagnosis data is normal, based on a degree of divergence between the diagnosis data compensated by the diagnosis data compensation unit106and the learning model.

The machine50includes a machine tool, an industrial robot, and other various machines. As the state data of the machine50, various data representative of physical states of the machine50is included. Here, by way of example, a case is described in which the machine50is a machine tool, and the state data is a torque command (torque control) waveform for an electric motor that drives an axis of the machine tool.

In the abnormality determination device10, the normal data compensation unit103compensates the state data in accordance with a predetermined rule by using a compensation value of the state data at each of temperatures. The predetermined rule is, for example, an arithmetic operation of dividing the feature of the state data that is a compensation target by the compensation value in regard to each of environmental temperatures. Thereby, when the state data that is the compensation target is the state data at the normal time, a feature after compensation becomes approximately 1, regardless of the environmental temperature, and a comparison in diagnosis can easily be executed.

The compensation value used in the compensation in the normal data compensation unit103is a predetermined statistical quantity calculated by the compensation value deriving unit105with respect to the feature calculated from the waveforms of the state data at the normal time at each environmental temperature. The compensation value deriving unit105extracts state data at a specific environmental temperature from a state data group that is stored in the normal data storage unit113by being correlated with environmental temperature data. A predetermined statistical quantity calculated with respect to the feature of the extracted state data waveform is set as the compensation value at the environmental temperature. As the feature of the waveform, a P2P (Peak to Peak) is used. Note that a maximum value or a minimum value may be used as the feature of the waveform. In the present embodiment, although it is assumed that an average value is used as the predetermined statistical quantity, a median, a mode or the like may be used, or two or more features may be used. By executing a similar operation, if state data at a corresponding environmental temperature exists in the normal data storage unit113, the compensation value at this environmental temperature can be derived.

By the above-described configuration, the state data at the normal time of the machine50can be obtained in regard to each of environmental temperatures. However, in general, the environmental temperature of the machine does not greatly change, and it is difficult to acquire in advance the state data at the normal time in regard to all environmental temperatures, and to accumulate the state data in the normal data storage unit113. Accordingly, there arises such a problem that a compensation value is not obtained for the environmental temperature with respect to which the state data could not be obtained in advance. As regards this point, the abnormality determination device10according to the present embodiment is configured to acquire, in regard to an environmental temperature with respect to which a compensation value has not been obtained, the compensation value by an interpolation operation, based on compensation values at environmental temperatures with respect to which state data have been obtained. In the abnormality determination device10, the compensation value interpolation unit104executes this function.

An interpolation operation of a compensation value by the compensation value interpolation unit104is described. By way of example, as illustrated inFIG.4, it is assumed that compensation values301and302in regard to two environmental temperatures (here, 20° C. and 30° C.) are acquired in advance by the compensation value deriving unit105. The compensation value interpolation unit104calculates a straight line311(relational expression) representing a relation between the environmental temperature and the compensation value, by linear regression using the known compensation values301and302. The compensation value interpolation unit104calculates a compensation value Cl at a specific environmental temperature (here, 25° C.) by using the straight line311.

As another example of the interpolation calculation, as illustrated inFIG.5, a condition is assumed in which known compensation values321,322and323are present in relation to three environmental temperatures. In this case, using the three compensation values321,322and323, the compensation value interpolation unit104may calculate a curve331(relational expression) representing a relation between the environmental temperature and the compensation value by curve approximation. The compensation value interpolation unit104calculates a compensation value C2at a specific environmental temperature (here, 25° C.) by using the curve331.

The diagnosis data compensation unit106calculates, by using the compensation value interpolation unit104, a compensation value at an environmental temperature corresponding to the diagnosis data stored in the diagnosis data storage unit114. Then, the diagnosis data compensation unit106compensates the diagnosis data by using a similar method to the method in the normal data compensation unit103.

Next, learning by the machine learning device24is described. The machine learning device24includes a learning model storage unit241, a learning unit242, and a machine abnormality determination unit243. The learning unit242executes machine learning by using the feature of the compensated normal state data waveform calculated by the normal data compensation unit103, and constructs a learning model. The constructed learning model is stored in the learning model storage unit241, and is used for abnormality determination by the machine abnormality determination unit243.

In the present embodiment, the machine learning device24executes abnormality determination by an MT method (Mahalanobis Taguchi method). Using the feature of the normal data compensated by the normal data compensation unit103, the learning unit242provides a mathematical model below, by which the machine abnormality determination unit243executes abnormality determination, based on the degree of divergence from the normal data.

wherein d is a Mahalanobis distance representative of a degree of divergence of the diagnosis data from the normal data; x is a vector in which features of diagnosis data waveforms compensated by the diagnosis data compensation unit106are arranged; μ is a vector in which average values of features of normal state data waveforms compensated by the normal data compensation unit103are arranged; and Σ is a variance-covariance matrix of features of normal state data waveforms compensation by the normal data compensation unit103.

The learning unit242provides the mathematical model (learning model) expressed by the above equation (1) to the machine abnormality determination unit243. The machine abnormality determination unit243calculates the degree of divergence from the normal data as the Mahalanobis distance d by the above equation (1) in regard to the feature x of the diagnosis data waveform compensated by the diagnosis data compensation unit106. Then, when the Mahalanobis distance d calculated by equation (1) is greater than a preset threshold, the machine abnormality determination unit243determines that the diagnosis data is abnormal. The threshold used here may be set, for example, based on an experimental value or an empirical value. Such a configuration may be adopted that a user can set the threshold for the abnormality determination device10.

Hereinafter, embodiments of the abnormality determination device10are described. It is assumed that the learning model is already constructed by the learning unit242.FIG.6is a flowchart representing a diagnosis process of a first embodiment. The diagnosis process ofFIG.6(andFIG.7andFIG.8) is executed under the control by the CPU1of the abnormality determination device10. In the first embodiment, it is assumed that the known compensation values that are necessary for the interpolation of the compensation value are already derived by the compensation value deriving unit105. To start with, state data for diagnosis (diagnosis data) is acquired via the state data acquisition unit102(step S101). Next, for a subsequent step, compensation values already calculated by the compensation value deriving unit105are taken in (step S102).

Next, by the above-described interpolation operation, the compensation value interpolation unit104calculates a compensation value in regard to the environmental temperature at the time when the diagnosis data is obtained (step S103). Furthermore, at this time, the diagnosis data compensation unit106compensates the diagnosis data by using the compensation value relating to the diagnosis data, which is acquired by the interpolation operation of the compensation value interpolation unit104(step S103). Next, the machine abnormality determination unit243calls the learning model (i.e. the above equation (1)) already constructed by the learning unit242(step S104). Subsequently, the machine abnormality determination unit243calculates the degree of divergence of the diagnosis data from the learning model, and determines normality/abnormality of the diagnosis data by comparing the degree of divergence with a predetermined threshold (step S105).

FIG.7is a flowchart representing a diagnosis process of a second embodiment. To start with, the abnormality determination device10(compensation value interpolation unit104) determines whether acquisition of additional data is necessary. The additional data is indicative of state data at normal time, which can be acquired after the start of the main operation of the machine50. For example, in the state in which the interpolation operation cannot be executed by the presently already acquired state data, the acquisition of additional data is determined to be necessary (step S201). When the acquisition of additional data is unnecessary (S201: NO), the diagnosis process of the above-described steps5101to5104is executed.

When the acquisition of additional data is necessary (S201: YES), the abnormality determination device10acquires additional state data (additional data) for learning from the machine50(step S202). A compensation value corresponding to the additional data is derived by the compensation value deriving unit105(step S203). Known compensation values necessary for the update of the compensation values (the update of the relational expression) are taken in (step S204). Then, update is executed to add the compensation value corresponding to the additional data to the known compensation values that are taken in. A set of the thus updated compensation values is prepared (step S205).

The process returns to step S201, and when the acquisition of further additional data is unnecessary (S201: NO), the diagnosis process by the above-described steps S101to S105is executed. In the diagnosis process of steps S101to S105, the compensation value prepared in step S205is applied (step S205, S102).

FIG.8is a flowchart representing a diagnosis process of a third embodiment. To start with, the abnormality determination device10(compensation value interpolation unit104) determines whether acquisition of additional data is necessary. For example, in the state in which the interpolation operation cannot be executed by the presently already acquired state data, the acquisition of additional data is determined to be necessary (step S301). When the acquisition of additional data is necessary (S301: YES), the abnormality determination device10acquires additional state data (additional data) for learning from the machine50(step S302). A compensation value corresponding to the additional data is derived by the compensation value deriving unit105(step S303). Known compensation values necessary for the update of the compensation values (the update of the relational expression) are taken in (step S304).

Then, update of the compensation values is executed to add the compensation value corresponding to the additional data to the known compensation values that are taken in. A set of the thus updated compensation values is prepared (step S305). The learning unit242calls the trained learning model (step S306). The learning unit242executes re-learning by adding the additional data to the already acquired state data, and reconstructs the learning model (update of the learning model) (step S307). The process returns to step S301, and whether the acquisition of additional data is necessary is determined once again (step S301).

When the acquisition of additional data is unnecessary (S301: NO), the diagnosis process by the above-described steps S101to S105is executed. In the diagnosis process of steps S101to S105, the new compensation value and new learning model updated in steps S305and S307are applied (step S305, S307, S102, S104).

The diagnosis process illustrated inFIG.7andFIG.8corresponds to a process in which the acquisition of state data (additional data) by the state data acquisition unit is continuously executed, and when the acquisition of additional data becomes unnecessary, the compensation value interpolation unit104updates the relational expression and utilizes the updated relational expression. In a modification of this process, the compensation value interpolation unit104may update the relational expression when the compensation value interpolation becomes possible, or when the number of additional data reaches a specified number, or when the degree of change of the environmental temperature exceeds a specified value (when the environmental temperature has sharply changed).

As described above, according to the present embodiment, exact diagnosis can be executed by calculating the compensation value from the normal data in accordance with the environmental temperature, and compensating the state data. In addition, by calculating, by interpolation, a compensation value at an unknown environmental temperature from known compensation values, compensation becomes possible in regard to the environmental temperature at which normal data could not be acquired in advance.

The present invention has been described above by using typical embodiments. It can be understood, however, that a person skilled in the art can make changes, various other modifications, omissions and additions to the above-described embodiments, without departing from the scope of the present invention.

In the above embodiments, the example was described in which the torque command waveform is used as the data relating to the state of the machine, but this is merely an example. As the data indicative of the state of the machine, use can be made of various data, such as data of various sensors, various data (velocity, acceleration, and the like) relating to the input and output of the electric motor, and the like.

In the above embodiments, the example was described in which the MT method is used as the machine learning, but methods other than the MT method may be used as methods for evaluating the degree of divergence of the diagnosis data from the normal data. For example, when both the data at normal time and the data at abnormal time are sufficiently acquired as the data relating to the state of the machine, a learning model may be constructed by applying supervised learning in the machine learning device, and the abnormality determination may be executed by using the learning model.

In the above embodiments, the abnormality determination device10is configured to acquire the state data from the machine50. Instead of this configuration, the abnormality determination device may be configured to acquire the state data from the input device such as a keyboard, or from an external computer.

The functional blocks of the abnormality determination device10illustrated inFIG.2may be implemented by the execution of various kinds of software stored in a storage device by the CPU1of the abnormality determination device10, or may be implemented by a configuration constituted mainly by hardware such as an ASIC (Application Specific Integrated Circuit).

A program for executing the diagnosis process (FIG.6toFIG.8) in the above embodiments can be stored in various computer-readable storage media (for example, a semiconductor memory such as a ROM, an EEPROM or a flash memory, a magnetic storage medium, or an optical disc such as a CD-ROM or a DVD ROM).

REFERENCE SIGNS LIST