JP6879863B2 - Servo mechanism diagnostic device, diagnostic method and diagnostic program - Google Patents

Description

The present invention relates to a diagnostic device, a diagnostic method, and a diagnostic program of a servo mechanism.

As a method of detecting an abnormality in a servo mechanism of an NC machine tool or the like, a method of obtaining a numerical model of a servo system in advance and diagnosing the abnormality by using this numerical model is known. For example, in Patent Document 1, a numerical model of a hydraulic servo system is prepared, and parameters such as the time constant and steady gain of the first-order lag system and the resonance frequency, attenuation coefficient, and steady gain of the second-order lag system used in the numerical model are prepared. Is disclosed, and the servo mechanism is diagnosed and managed based on the combination of these parameters.

Japanese Unexamined Patent Publication No. 2004-212239

However, in the method of Patent Document 1 using a numerical model, the accuracy of the numerical model is required, so that it is necessary to perform complicated processing for diagnosing the device.

The present invention has been made in view of such circumstances, and is a diagnostic device and a diagnostic method for a servo mechanism capable of easily diagnosing a servo mechanism without the need to use a highly accurate numerical model. The purpose is to provide a diagnostic program.

A first aspect of the present invention relates to an input signal relating to m (m ≧ 1) operation amounts and k (k ≧ 1) control amounts obtained when a predetermined target value is given to the normal servo mechanism. Among the output signals, a normal data storage unit that stores a normal state locus of m + k dimensions or less generated by using the signal value of the input signal and the signal value of the output signal that are synchronized with each other, and the servo mechanism at the time of observation. A data acquisition unit that acquires an input signal related to the m operation amounts and an output signal related to the k control amounts when the predetermined target value is given to the data acquisition unit, and the input signal acquired by the data acquisition unit. Among the output signals, an observation state locus generator that generates an observation state locus of m + k dimension or less by using the signal value of the input signal and the signal value of the output signal that are synchronized with each other, the normal state locus, and the observation. It is a diagnostic device of a servo mechanism including an abnormality detection unit that detects an abnormality using a state locus.

According to the above configuration, an input signal relating to m (m ≧ 1) manipulated quantities and an output signal relating to k (k ≧ 1) control quantities obtained when a predetermined target value is given to the normal servo mechanism. Of these, the signal value of the input signal and the signal value of the output signal that are synchronized with each other (for example, acquired at the same sampling time) are regarded as one data aggregate, and the normal state locus of m + k dimension or less is used using these data. Is generated. This normal state locus is stored in the normal data storage unit and used for diagnosing the servo mechanism. Further, at the time of observation, an input signal related to m operation amounts and an output signal related to k control amounts when a predetermined target value is given to the servo mechanism are acquired by the data acquisition unit, and are acquired by the data acquisition unit. Of the input signal and output signal, the signal value of the input signal and the signal value of the output signal that are synchronized with each other (for example, acquired at the same sampling time) are regarded as one data aggregate, and these data are used in m + k dimensions. The following observation state locus is generated by the observation state locus generator. In other words, the observed state locus is generated by the same method as the normal state locus described above. Then, the abnormality detection unit performs abnormality detection using the normal state locus and the observation state locus.
In this way, instead of comparing the input signal and the output signal acquired in the time series, among the time series signals, the signal values that are synchronized with each other are regarded as one data set, and these data sets are used. Since the state locus is created and the state locus in the normal state is compared with the state locus in the observation time to detect the abnormality, the abnormality can be detected without the need for a highly accurate numerical model.

In the diagnostic device of the servo mechanism, the normal state locus uses the signal value of the input signal and the signal value of the output signal that are synchronized with each other among the input signal and the output signal in the normal state with each of the operation amounts and each. The observation state locus is generated by plotting on the coordinate axes corresponding to the control amount, and the observation state locus is a signal value of the input signal synchronized with each other among the input signal and the output signal acquired by the data acquisition unit. And may be generated by plotting the signal values of the output signals on the coordinate axes corresponding to each of the manipulated amounts and each of the controlled amounts.

According to the above configuration, an input signal relating to m (m ≧ 1) manipulated quantities and an output signal relating to k (k ≧ 1) control quantities obtained when a predetermined target value is given to the servo mechanism in the normal state. Of these, the signal values of the input signals and the signal values of the output signals that are synchronized with each other (for example, acquired at the same sampling time) are regarded as one data aggregate, and are on the coordinate axes corresponding to each manipulated variable and each controlled variable. By plotting each signal value, a normal state locus of m + k dimension or less is generated. Similarly, among the input signal and the output signal acquired by the data acquisition unit, the signal value of the input signal and the signal value of the output signal that are synchronized with each other (for example, acquired at the same sampling time) are one data aggregate. Then, by plotting each signal value on the coordinate axes corresponding to each operation amount and each control amount, an observation state locus of m + k dimension or less is generated. Then, the abnormality detection unit performs abnormality detection using the normal state locus and the observation state locus generated in this way.

In the diagnostic device of the servo mechanism, the abnormality detecting unit may detect an abnormality based on an evaluation value relating to a difference between the normal state locus and the observed state locus.
More specifically, the abnormality detection unit calculates a difference vector between the normal state locus and the observation state locus for each coordinate axis, and uses the difference vector and a preset threshold value to detect an abnormality. It may be detected.

It can be judged that the closer the observed state locus is to the normal state locus, the closer to the normal state. Therefore, it is possible to easily detect an abnormality by using the evaluation value regarding the difference between the normal state locus and the observed state locus. The threshold value may be set for each operation amount and each control amount.

In the diagnostic device of the servo mechanism, the abnormality detecting unit calculates an evaluation value regarding the difference between the normal state locus and the observed state locus by using the weighting information determined for each coordinate axis, and uses the evaluation value and the evaluation value. An abnormality may be detected using a preset threshold value, and the weighting information may be set according to the degree of variation of each signal value.

Depending on the amount of operation and the amount of control, there are those with large variation in signal values and those with small variation in signal values. In this case, with respect to the manipulated variable and the controlled variable having a large variation, the difference between the normal state locus and the observed state locus becomes large, and there is a possibility that an erroneous abnormality is detected. Therefore, by calculating the evaluation value using the weighting information (for example, the weighting coefficient) in consideration of the variation of the signal value, the influence of the signal value having a large variation can be reduced, and the false detection of abnormality can be suppressed. It becomes possible.

In the diagnostic device of the servo mechanism, the normal state locus is created by using the input signal and the output signal obtained when the predetermined target value is given to the numerical model modeling the servo mechanism in the normal state. It may be done.

According to the above configuration, since the numerical model of the servo mechanism in the normal state is used, the input signal and the output signal can be easily acquired.

The diagnostic device of the servo mechanism may further include a display unit that compares and displays the normal state locus and the observed state locus.

According to the above configuration, since the normal state locus and the observation state locus are displayed in comparison on the display unit, it is possible to visually notify the user of the change in the current state with respect to the normal time. ..

The diagnostic device of the servo mechanism is an input signal relating to the m operation amounts when the predetermined target value is given to each numerical model of the servo mechanism created for each of a plurality of different abnormal factors, and the k. An abnormal data storage unit that generates an abnormal state locus for each abnormal state using an output signal related to each control amount, and stores the generated abnormal state locus and the abnormal factor in association with each other, and each of the abnormal states. Further provided is an abnormality factor identification unit that identifies the cause of the abnormality of the servo mechanism from the similarity between the evaluation value related to the difference between the locus and the normal state locus and the evaluation value related to the difference calculated by the abnormality detection unit. You may do it.

According to the above configuration, a plurality of different abnormal factors are assumed, a numerical model of the servo mechanism is prepared for each assumed abnormal factor, and an input signal and an output signal when a predetermined target value is given to this numerical model are acquired. Then, an abnormal state locus is generated from these acquired data by the same method as the normal state locus. As a result, an abnormal state locus is generated for each abnormal factor. Then, an evaluation value regarding the difference between the abnormal state locus and the normal state locus in each abnormal factor is calculated, and this evaluation value and the evaluation value calculated by the abnormality detection unit (that is, the difference between the normal state locus and the observed state locus) are calculated. The cause of the abnormality of the servo mechanism is identified based on the degree of similarity with the evaluation value).

In the diagnostic device of the servo mechanism, an input signal relating to the m operation amounts when the predetermined target value is given to each numerical model of the servo mechanism created for each of a plurality of different abnormal factors, and the k. An abnormal data storage unit that stores an evaluation value related to a difference between the abnormal state locus for each abnormal state locus generated based on an output signal related to the control amount and the normal state locus in association with the abnormal factor, and the above. An abnormality that identifies the cause of the abnormality of the servo mechanism from the similarity between the evaluation value related to the difference for each of the abnormality factors stored in the abnormality data storage unit and the evaluation value related to the difference calculated by the abnormality detection unit. A factor identification unit may be further provided.

According to the above configuration, an evaluation value based on the difference between the abnormal state locus and the normal state locus is calculated in advance for each abnormality factor, and the calculation result is stored in the abnormal data storage unit in advance. Compared with the case where the abnormal state locus is stored, it is possible to reduce the burden of arithmetic processing of the evaluation value related to the difference.

A second aspect of the present invention relates to an input signal relating to m (m ≧ 1) operation amounts and k (k ≧ 1) control amounts obtained when a predetermined target value is given to the normal servo mechanism. Among the output signals, in the step of reading out the normal state locus of m + k dimension or less generated by using the signal value of the input signal and the signal value of the output signal that are synchronized with each other from a predetermined storage unit, and at the time of observation, the said Of the steps of acquiring the input signal related to the m operation amounts and the output signal related to the k control amounts when the predetermined target value is given to the servo mechanism, and the input signal and the output signal at the time of observation. , A step of generating an observation state locus of m + k dimension or less by using the signal value of the input signal and the signal value of the output signal that are synchronized with each other, and detecting an abnormality by using the normal state locus and the observation state locus. It is a method of diagnosing a servo mechanism having a step of performing.

A third aspect of the present invention relates to an input signal relating to m (m ≧ 1) operation amounts and k (k ≧ 1) control amounts obtained when a predetermined target value is given to the normal servo mechanism. Among the output signals, the process of reading out the normal state locus of m + k dimension or less generated by using the signal value of the input signal synchronized with each other and the signal value of the output signal from a predetermined storage unit, and at the time of observation, the above-mentioned Of the process of acquiring the input signal related to the m operation amounts and the output signal related to the k control amounts when the predetermined target value is given to the servo mechanism, and the input signal and the output signal at the time of observation. , A process of generating an observation state locus of m + k dimension or less by using the signal value of the input signal and the signal value of the output signal that are synchronized with each other, and detecting an abnormality by using the normal state locus and the observation state locus. It is a diagnostic program of the servo mechanism for causing the computer to execute the processing to be performed.

According to the present invention, it is not necessary to use a highly accurate numerical model, and the servo mechanism can be easily diagnosed.

It is a figure which showed the hardware structure of the diagnostic apparatus of the servo mechanism which concerns on one Embodiment of this invention. It is a functional block diagram which showed an example of the function provided in the diagnostic apparatus of the servo mechanism which concerns on one Embodiment of this invention. It is a figure for demonstrating the normal state locus which concerns on one Embodiment of this invention. It is a figure for demonstrating the normal state locus which concerns on one Embodiment of this invention. It is a figure for demonstrating the normal state locus which concerns on one Embodiment of this invention. It is a figure for demonstrating the difference vector between the normal state locus and the observed state locus which concerns on one Embodiment of this invention. It is a flowchart which showed the procedure of the process executed by the diagnostic apparatus of the servo mechanism which concerns on one Embodiment of this invention. It is a figure for demonstrating the content of the process executed by the abnormal factor identification part which concerns on one Embodiment of this invention. It is a figure for demonstrating the content of the process executed by the abnormal factor identification part which concerns on one Embodiment of this invention. It is a figure which compared and showed the time-series signals of a load position, a motor speed, and a torque before and after the adjustment when the gear interval is adjusted in the servo mechanism which has a gear. It is a figure which compared and showed the state locus before adjustment and after adjustment generated by using the time-series signals about a load position, a motor speed, and a torque shown in FIG.

Hereinafter, a diagnostic device and a diagnostic method of the servo mechanism according to the present invention, and an embodiment of the diagnostic program will be described with reference to the drawings.
FIG. 1 is a diagram showing a hardware configuration of a diagnostic device for a servo mechanism according to an embodiment of the present invention. As shown in FIG. 1, the diagnostic device 1 of the servo mechanism is a so-called computer system, and includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12 for storing programs executed by the CPU 11, and each program. A RAM (Random Access Memory) 13 that functions as a work area during execution, a hard disk drive (HDD) 14 as a large-capacity storage device, a communication interface 15 for connecting to a network, an input unit 16 consisting of a keyboard, a mouse, and the like, and Each of the display units 17 and the like including a liquid crystal display device and the like for displaying data are provided. Each of these parts is connected via a bus 18.
Various programs (for example, a diagnostic program of a servo mechanism) are stored in the ROM 12, and various functions are realized by the CPU 11 reading the programs from the ROM 12 to the RAM 13 and executing the programs. The storage medium for storing the program or the like executed by the CPU 11 is not limited to the ROM 12, and may be another recording medium composed of a magnetic disk, a magneto-optical disk, a semiconductor memory, or the like.

FIG. 2 is a functional block diagram showing an example of the functions included in the diagnostic device 1 of the servo mechanism according to the present embodiment. As shown in FIG. 2, the diagnostic device 1 of the servo mechanism (hereinafter, simply referred to as “diagnostic device 1”) includes, for example, a normal data storage unit 21, an abnormal data storage unit 22, a data acquisition unit 23, and an observation state locus generation. A unit 24, an abnormality detection unit 25, an abnormality factor identification unit 26, and a display unit 27 are provided.

The servo mechanism is a mechanism in which when a predetermined target value is given, at least one input signal for matching the control amount with the target value is input, and the control amount changes as a response. Examples of the control amount include a position, an orientation, a posture, and the like. Examples of the servo mechanism include robots, NC machine tools, and the like.

The normal state locus is stored in advance in the normal data storage unit 21. For example, the normal state locus is generated by the following procedure.
For example, as shown in FIG. 3, a numerical model that models the servo mechanism in the normal state is prepared. In the present embodiment, when a predetermined target value is given to this numerical model, m (m ≧ 1) time-series input signals [U1 ^* (t), U2 ^* (t), ... Um ^* (t)] is given to the mechanical system 10 of the servo mechanism to be controlled, and k (k ≧ 1) time-series output signals [Y1 ^* (t), Y2) are used as the response signals (control amount). ^* (T), ... Yk ^* (t)] shall be obtained.

Next, as shown in FIG. 4, time-series input signals [U1 ^* (t), U2 ^* (t), ... Um ^* (t)] and time-series output signals [Y1 ^* (t), Y2 ^* (t), ... Yk ^* (t)] are acquired at signal values that are synchronized with each other, for example, at the same sampling time ti. The signal value of the input signal [U1 ^* (ti), U2 ^* (ti), ... Um ^* (ti)] and the signal value of the output signal [Y1 ^* (ti), Y2 ^* (ti), ... • Yk ^* (ti)] is regarded as one data aggregate, and as shown in FIG. 5, signal values are plotted on the coordinate axes corresponding to each operation amount and each control amount. Then, by connecting the plotted signal values, a m + k-dimensional state locus is generated. Then, by performing this work for each sampling time, a number of state loci corresponding to the sampling number N + 1 is drawn.
The state locus is represented by, for example, the following equation (1).

The plurality of state loci corresponding to each sampling time generated in this way are stored in the normal data storage unit 21 as normal state loci.
Here, the numerical model of the servo mechanism in the normal state described above does not have to be highly accurate, and it is sufficient that the qualitative properties of the basic physical phenomenon are incorporated into the model. Further, in the present embodiment, the input signal and the output signal are obtained by using the numerical model of the servo mechanism in the normal state, but instead of this, the servo mechanism in the normal state, for example, the servo mechanism at the time of shipment is predetermined. The normal state locus may be generated by giving the target value of and obtaining the input signal and the output signal at this time.
Further, in the above example, the state locus of m + k dimension is used, but it is not necessary to use all the signal values of the input signal and the output signal. For example, a predetermined operation amount and a predetermined control amount are omitted, and the state locus is less than m + k dimension. It may be possible to generate a state locus.

A plurality of abnormal state loci acquired in advance are stored in the abnormal data storage unit 22. For example, the abnormal state locus is generated by the following procedure.
First, a plurality of different abnormal factors a to n are assumed, and a numerical model of the servo mechanism corresponding to the abnormal factors a to n is generated. Examples of abnormal factors include changes in various friction parameters acting on the servo mechanism and changes in dead zones (play, backlash, etc.). The type and expected number of abnormal factors are not particularly limited. The numerical model of the servo mechanism of each abnormal factor a to n created here is, for example, a numerical model in which predetermined parameters related to each abnormal factor are changed with respect to the above-mentioned numerical model of the servo mechanism in the normal state. You may.

Subsequently, the same target value as when the normal state locus was created is given to the numerical model of the servo mechanism created for each abnormal factor, and the time-series input signal and time at that time are given in the same manner as in the normal state described above. Obtain a series output signal. Then, by performing the same processing as the above-mentioned normal state locus using the input signal and the output signal, an abnormal state locus (first abnormal state locus to nth abnormal state locus) for each abnormal factor is generated. The abnormal state locus generated in this way is stored in the abnormal data storage unit 22 in association with the abnormal factor.
The abnormal state locus in each abnormal factor is represented by, for example, the following equation (2).

The data acquisition unit 23 acquires a time-series input signal and a time-series output signal when the servo mechanism is operating (observation). For example, when the data acquisition unit 23 gives the same target value as when the normal state locus is created to the servo mechanism to be observed at a predetermined timing such as when the servo mechanism is started or immediately after maintenance. Acquires a series input signal and a time series output signal.

The observation state locus generation unit 24 generates the observation state locus by performing the same processing as the above-mentioned normal state locus using the time-series input signal and the time-series output signal acquired by the data acquisition unit 23. To do. Specifically, among the time-series input signal and the time-series output signal acquired by the data acquisition unit 23, the signal value and output signal of the input signal acquired at the same sampling time i (i = 0 to N). The signal value of is regarded as one data aggregate, and the signal value is plotted on the coordinate axes corresponding to each operation amount and each control amount. Then, by connecting the plotted signal values, an m + k-dimensional observation state locus is generated.
The observation state locus is represented by, for example, the following equation (3).

The abnormality detection unit 25 detects an abnormality by comparing the observation state locus generated by the observation state locus generation unit 24 with the normal state locus stored in the normal data storage unit 21. Here, the comparison between the observed state locus and the normal state locus is performed by comparing the loci that are synchronized with each other, in other words, the state loci acquired at the same sampling time (i = 0 to N). ..

Specifically, the abnormality detection unit 25 calculates an evaluation value related to the difference by using the normal state locus and the observation state locus that are synchronized with each other. Here, the difference vector between the normal state locus and the observed state locus that are synchronized with each other is calculated, and the average value of the absolute values of the calculated difference vectors is calculated as the evaluation value related to the difference. As shown in FIG. 6, the difference vector is calculated for each manipulated variable and controlled variable. For example, the difference vector is represented by the following equation (4). The anomaly detection unit 25 takes an absolute value at all sampling times and integrates the aggregate of the difference vectors at each sampling time i calculated in this way, and calculates the average value as the evaluation value X regarding the difference. The evaluation value X regarding the difference is expressed by the following equation (5).

The abnormality detection unit 25 compares the evaluation value X related to the difference with the preset threshold value, and detects the abnormality when the evaluation value X is the threshold value abnormality. That is, since it is considered that the cause of the difference vector is a change in the characteristics of the machine, it can be considered that an abnormality has occurred when the change in the characteristics exceeds a certain range. Therefore, the abnormality detection unit 25 determines that an abnormality has occurred when the difference vector exceeds a predetermined threshold value.

Depending on the amount of operation and the amount of control, some have a large variation and some have a small variation. Therefore, for the manipulated variable and the controlled variable having a large variation in the values, a relatively small weighting coefficient may be set to reduce the influence of the variation. As described above, by reducing the value of the weighting coefficient for the operation amount and the control amount having a large variation, it is possible to suppress erroneous detection of an abnormality due to the variation.

Instead of using the value obtained by averaging the difference vectors as the evaluation value X as described above, the abnormality is determined by using the difference vector between the normal state locus and the observed state locus at the sampling time of a part of the section. It may be that.

When the abnormality is detected by the abnormality detection unit 25, the cause of the abnormality is subsequently identified by the abnormality factor identification unit 26.
The abnormality factor identification unit 26 identifies an abnormality using the first abnormal state locus to the nth abnormal state locus stored in the abnormal data storage unit 22.
Specifically, the evaluation value related to the difference is calculated from each abnormal state locus and the normal state locus in the same manner as described above. Specifically, the difference vector is calculated using each abnormal state locus and the normal state locus. At this time, when the weighting coefficient is used in the abnormality detection unit 25, the difference vector in each operation amount and control amount is calculated by using the same weighting coefficient. In the present embodiment, the abnormal state locus is stored in the abnormal data storage unit 22 in advance, but instead of this, the evaluation value regarding the difference between the abnormal state locus of each abnormal factor with respect to the normal state locus, specifically May store the difference vector in the abnormal data storage unit 22.

The difference vector for each anomalous factor a to n is represented by the following equation (6).

Next, the evaluation value regarding the difference in each abnormality factor (difference vector in the present embodiment) is compared with the evaluation value regarding the difference calculated by the abnormality detection unit 25, and the degree of similarity between the two is evaluated. At this time, the similarity Va to Vn are evaluated by comparing the difference vectors of the sampling times that are synchronized with each other. The abnormal factor identification unit 26 identifies the abnormal factor having the highest degree of similarity as the factor of the current abnormality.

For example, the evaluation of similarity can be calculated by using the inner product of vectors as expressed by the following equation (7). By using the inner product, the calculation can be simplified and the processing speed can be increased.

The display unit 17 displays a graph showing a comparison between the normal state locus and the observation state locus, displays the detection result of the abnormality detection unit 25, and displays the abnormality factor identified by the abnormality factor identification unit 26. To do.

Next, the flow of arithmetic processing of the diagnostic apparatus 1 having the above configuration will be described with reference to FIG. 7. FIG. 7 is a flowchart showing a procedure of processing executed by the diagnostic apparatus 1.

First, as described above, the normal state locus is stored in advance in the normal data storage unit 21, and a plurality of abnormal state loci (first abnormal state locus to nth abnormal state locus) are stored in the abnormal data storage unit 22. It is associated with the anomalous factor and stored in advance.
In this state, the data acquisition unit 23 acquires the time-series input signal and output signal (observation data) when a predetermined target value is given to the servo mechanism at the time of observation (SA1). Subsequently, the observation state locus generation unit 24 generates an observation state locus using the input signal and the output signal at the time of observation acquired by the data acquisition unit 23 (SA2). Subsequently, the abnormality detection unit 25 calculates an evaluation value related to the difference from the observation state locus and the normal state locus stored in the normal data storage unit 21 (SA3), and detects an abnormality using the evaluation value related to the difference. (SA4). Specifically, the difference vector between the normal state locus and the observed state locus at the same sampling time is calculated, and the abnormality is detected by comparing this difference vector with a preset threshold value. At this time, a preset weighting coefficient for each operation amount and each control amount may be used.

Then, when the abnormality detection unit 25 does not detect an abnormality, the process ends (“NO” in SA5). On the other hand, when the abnormality is detected by the abnormality detection unit 25 (“YES” in SA5), the abnormality factor identification unit 26 identifies the abnormality factor. Specifically, as shown in FIG. 8, the evaluation value regarding the difference is calculated from the first abnormal state locus to the nth abnormal state locus and the normal state locus stored in the abnormal data storage unit 22 (SA6). .. Then, the abnormal factor is specified based on the calculated evaluation value related to the difference and the evaluated value related to the difference calculated in step SA3 (SA7). Specifically, in step SA6, the difference vector of each abnormal state locus with respect to the normal state locus is calculated using the first abnormal state locus to the nth abnormal state locus and the normal state locus, and each difference vector and step SA3 By calculating the inner product with the difference vector calculated in step 1, the degree of similarity Va to Vn for each anomalous factor is calculated. This calculation formula is as shown in the above formula (7). Then, the abnormal factors a to n having the highest degree of similarity are specified as the factors of the current abnormality. For example, as shown in FIG. 9, when the similarity Va to Vd for each abnormality factor a to d (n = 4) is calculated, the abnormality factor a having the highest degree of similarity is identified as the abnormality factor this time. To.
The abnormal factors and the like identified in this way are appropriately displayed on the display unit 27 and notified to the user.

As described above, according to the diagnostic device, the diagnostic method, and the diagnostic program of the servo mechanism according to the present embodiment, m pieces (m ≧ 1) obtained when a predetermined target value is given to the normal servo mechanism. Of the input signal related to the operation amount of) and the output signal related to the control amount of k (k ≧ 1), the signal value of the input signal and the signal value of the output signal acquired at the same sampling time are regarded as one data aggregate. , M + k-dimensional normal state locus created by plotting each signal value on the coordinate axes corresponding to each manipulated amount and each controlled amount, and generated by the same method from the input signal and output signal at the time of observation. The evaluation value related to the difference is calculated by comparing with the observed state locus, and the abnormality is detected based on the evaluation value related to this difference.

This makes it possible to detect an abnormality more easily than when comparing the input signal and the output signal acquired in time series. For example, FIG. 10 is a diagram showing a comparison of time-series signals of load position, motor speed, and torque before and after adjustment when the gear spacing is adjusted in a servo mechanism having gears. As shown in FIG. 10, in the time series signal, the change before and after the adjustment of the gear interval does not appear remarkably. However, as shown in FIG. 11, when a state locus is generated from these two signals, a locus that is completely different from the state locus before the gear adjustment and the state locus after the gear adjustment is drawn, and the adjustment is performed. The effect can be seen. In this way, by determining the abnormality using the state locus, it is possible to intuitively grasp how much the current state of the servo mechanism has changed with respect to the normal state.

Further, according to the diagnostic device, the diagnostic method, and the diagnostic program of the servo mechanism according to the present embodiment, the abnormal state locus for each abnormal factor is created, and the evaluation value regarding the difference between each abnormal state locus and the normal state locus is calculated. , The abnormal factor is determined from the similarity between the calculated evaluation value and the evaluation value obtained from the normal state locus and the observed state locus. By using the difference between the abnormal state locus and the normal state locus, the model error can be eliminated, and the cause of the abnormality can be identified without using a numerical model in which the servo mechanism is modeled with high accuracy. As described above, according to the diagnostic device, the diagnostic method, and the diagnostic program of the servo mechanism according to the present embodiment, since the numerical model generated with high accuracy is not required, the complicated processing can be avoided and the processing can be avoided. It is possible to reduce the burden and shorten the processing.

1: Diagnostic device 10: Mechanical system 21: Normal data storage unit 22: Abnormal data storage unit 23: Data acquisition unit 24: Observation state trajectory generation unit 25: Abnormality detection unit 26: Abnormal factor identification unit 27: Display unit

Claims (15)

Of the input signals related to the operation amount of m (m ≧ 1) and the output signals related to the control amount of k (k ≧ 1) obtained when a predetermined target value is given to the normal servo mechanism, they are synchronized with each other. A normal data storage unit that uses the signal value of the input signal and the signal value of the output signal as one data aggregate and stores a normal state locus of m + k dimensions or less generated by using all or a part of the data aggregate. When,
At the time of observation, a data acquisition unit that acquires an input signal related to the m operation quantities and an output signal related to the k control quantities when the predetermined target value is given to the servo mechanism.
Of the input signal and the output signal acquired by the data acquisition unit, the signal value of the input signal and the signal value of the output signal that are synchronized with each other are regarded as one data aggregate, and all or one of the data aggregates. An observation state locus generator that uses the unit to generate an observation state locus of m + k dimensions or less,
A diagnostic device for a servo mechanism including an abnormality detection unit that detects an abnormality by comparing the normal state locus and the observation state locus that are synchronized with each other. Of the input signals related to the operation amount of m (m ≧ 1) and the output signals related to the control amount of k (k ≧ 1) obtained when a predetermined target value is given to the normal servo mechanism, they are synchronized with each other. A normal data storage unit that uses the signal value of the input signal and the signal value of the output signal as one data aggregate and stores a normal state locus of m + k dimensions or less generated by using all or a part of the data aggregate. When,
(I) Of the input signals related to the m operation amounts and the output signals related to the k control amounts when the predetermined target values are given to the servo mechanism, which are acquired at the time of observation, the signals are synchronized with each other. The signal value of the input signal and the signal value of the output signal are regarded as one data aggregate, and the observation state locus of m + k dimension or less generated by using all or a part of the data aggregate, and (ii) the observation state. An abnormality detection unit that detects an abnormality by comparing the trajectory with the normal state trajectory that synchronizes with the trajectory.
A diagnostic device for a servo mechanism provided with. Of the input signals related to the operation amount of m (m ≧ 1) and the output signals related to the control amount of k (k ≧ 1) obtained when a predetermined target value is given to the normal servo mechanism, they are synchronized with each other. A normal data storage unit that uses the signal value of the input signal and the signal value of the output signal as one data aggregate and stores a normal state locus of m + k dimensions or less generated by using all or a part of the data aggregate. When,
(I) Of the input signals related to the m operation quantities and the output signals related to the k control quantities when the predetermined target values are given to the servo mechanism during observation, the signals of the input signals synchronized with each other. The value and the signal value of the output signal are regarded as one data aggregate, and the observation state locus of m + k dimension or less based on all or part of the data aggregate, and (ii) the normal state locus synchronized with the observation state locus. With the anomaly detection unit that detects anomalies by comparing with
A diagnostic device for a servo mechanism provided with. The normal state locus is generated by plotting the signal value included in the data set for each data set on the coordinate axes corresponding to the manipulated variable and the controlled variable.
From claim 1, the observation state locus is generated by plotting the signal value included in the data set for each data set on the coordinate axes corresponding to each manipulated variable and each controlled variable. 3. The servo mechanism diagnostic device according to any one of 3. The diagnostic device for a servo mechanism according to any one of claims 1 to 4, wherein the abnormality detection unit detects an abnormality based on an evaluation value relating to a difference between the normal state locus and the observation state locus that are synchronized with each other. The anomaly detection unit calculates a difference vector between the normal state locus and the observation state locus that are synchronized with each other for each coordinate axis, and detects an abnormality by using the difference vector and a preset threshold value. 5. The servo mechanism diagnostic device according to 5. The abnormality detection unit calculates an evaluation value regarding the difference between the normal state locus and the observation state locus that are synchronized with each other by using the weighting information determined for each coordinate axis, and the evaluation value and a preset threshold value are calculated. Detect anomalies using and
The diagnostic device for a servo mechanism according to claim 5 , wherein the weighting information is set according to the degree of variation of each signal value. The normal state locus is claimed from claim 1 to claim 1 created by using the input signal and the output signal obtained when the predetermined target value is given to the numerical model modeling the servo mechanism in the normal state. 7. The servo mechanism diagnostic device according to any one of 7. The diagnostic device for a servo mechanism according to any one of claims 1 to 8 , further comprising a display unit that compares and displays the normal state locus and the observed state locus that are synchronized with each other. Input signals related to the m operation amounts and output signals related to the k control amounts when the predetermined target values are given to each numerical model of the servo mechanism created for each of a plurality of different abnormal factors. An abnormal data storage unit that generates an abnormal state locus for each abnormal factor using the above and stores the generated abnormal state locus and the abnormal factor in association with each other.
Anomaly factor identification unit that identifies the cause of the abnormality of the servo mechanism from the similarity between the evaluation value related to the difference between each of the abnormal state loci and the normal state locus and the evaluation value related to the difference calculated by the abnormality detection unit. The diagnostic device for a servo mechanism according to any one of claims 5 to 9 , further comprising. Input signals related to the m operation amounts and output signals related to the k control amounts when the predetermined target values are given to each numerical model of the servo mechanism created for each of a plurality of different abnormal factors. An abnormal data storage unit that stores an evaluation value related to a difference between the abnormal state locus for each abnormal factor generated based on the above and the normal state locus in association with the abnormal factor.
The cause of the abnormality of the servo mechanism is specified from the similarity between the evaluation value related to the difference for each abnormality factor stored in the abnormality data storage unit and the evaluation value related to the difference calculated by the abnormality detection unit. The diagnostic device for a servo mechanism according to any one of claims 5 to 9 , further comprising an abnormality factor identifying unit. Of the input signals related to m (m ≧ 1) manipulated quantities and the output signals related to k (k ≧ 1) control quantities obtained when a predetermined target value is given to the normal servo mechanism, they are synchronized with each other. The signal value of the input signal and the signal value of the output signal are regarded as one data aggregate, and the normal state locus of m + k dimension or less generated by using all or a part of the data aggregate is read from a predetermined storage unit. The process of starting and
At the time of observation, a step of acquiring an input signal relating to the m operation quantities and an output signal relating to the k control quantities when the predetermined target values are given to the servo mechanism, and a step of acquiring the output signals.
Of the input signal and the output signal at the time of observation, the signal value of the input signal and the signal value of the output signal that are synchronized with each other are regarded as one data aggregate, and all or a part of the data aggregate is used. The process of generating the observation state locus of m + k dimensions or less,
A method of diagnosing a servo mechanism including a step of detecting an abnormality by comparing the normal state locus and the observed state locus that are synchronized with each other. Of the input signals related to m (m ≧ 1) manipulated quantities and the output signals related to k (k ≧ 1) control quantities obtained when a predetermined target value is given to the normal servo mechanism, they are synchronized with each other. The signal value of the input signal and the signal value of the output signal are regarded as one data aggregate, and the normal state locus of m + k dimension or less generated by using all or a part of the data aggregate is read from a predetermined storage unit. The process of starting and
(I) Of the input signals related to the m operation quantities and the output signals related to the k control quantities when the predetermined target values are given to the acquired servo mechanism at the time of observation, the said signals are synchronized with each other. The signal value of the input signal and the signal value of the output signal are regarded as one data aggregate, and the observation state locus of m + k dimension or less generated by using all or a part of the data aggregate, and (ii) the observation state. A process of detecting an abnormality by comparing the trajectory with the normal state trajectory synchronized with the trajectory.
A method of diagnosing a servo mechanism having. Of the input signals related to m (m ≧ 1) manipulated quantities and the output signals related to k (k ≧ 1) control quantities obtained when a predetermined target value is given to the normal servo mechanism, they are synchronized with each other. A step of combining the signal value of the input signal and the signal value of the output signal into one data aggregate, and reading out a normal state locus of m + k dimensions or less based on all or a part of the data aggregate from a predetermined storage unit.
(I) Of the input signals related to the m operation quantities and the output signals related to the k control quantities when the predetermined target values are given to the servo mechanism during observation, the signals of the input signals synchronized with each other. The value and the signal value of the output signal are regarded as one data aggregate, and the observation state locus of m + k dimension or less based on all or part of the data aggregate, and (ii) the normal state locus synchronized with the observation state locus. By comparing with the process of detecting anomalies
A method of diagnosing a servo mechanism having. Of the input signals related to m (m ≧ 1) manipulated quantities and the output signals related to k (k ≧ 1) control quantities obtained when a predetermined target value is given to the normal servo mechanism, they are synchronized with each other. The signal value of the input signal and the signal value of the output signal are regarded as one data aggregate, and the normal state locus of m + k dimension or less generated by using all or a part of the data aggregate is read from a predetermined storage unit. Processing and processing
At the time of observation, a process of acquiring an input signal related to the m operation amounts and an output signal related to the k control amounts when the predetermined target value is given to the servo mechanism, and
Of the input signal and the output signal at the time of observation, the signal value of the input signal and the signal value of the output signal that are synchronized with each other are regarded as one data aggregate, and all or a part of the data aggregate is used. Processing to generate observation state loci of m + k dimensions or less,
A diagnostic program of a servo mechanism for causing a computer to execute a process of detecting an abnormality by comparing the normal state locus and the observed state locus that are synchronized with each other.

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