NORMAL RANGE DEVIATION DIAGNOSIS DEVICE, NORMAL RANGE DEVIATION DIAGNOSIS SYSTEM, NORMAL RANGE DEVIATION DIAGNOSIS METHOD, AND PROGRAM

Abstract

The normal range deviation diagnosis device (1) includes a data acquisition unit (11) that acquires data indicating the latest value of a feature amount related to an external device (3); a data waveform learning unit (13) that extracts a data waveform, which is a predetermined number of consecutive data points, from feature amount data, which is time-series data in which data indicating feature amounts related to the external device (3) is accumulated, and performs learning to aggregate similar data waveforms to generate a predicted waveform, which is the aggregated data waveform; a future data estimation unit (14) that estimates future data, which is a value of the future feature amount data, using the predicted waveform and the most recent feature amount data; a normal range definition unit (15) that defines a normal range for each value of the future data; and a normal range deviation determination unit (16) that determines whether the state of the external device (3) deviates from the normal range based on whether the latest value of the feature amount related to the external device (3) deviates from the corresponding normal range.

Description

[Technical field]

[0001] The present disclosure relates to a normal range deviation diagnostic device, a normal range deviation diagnostic system, a normal range deviation diagnostic method, and a program. [Background technology]

[0002] In the manufacturing industry, there is a system that judges whether the state of a facility or equipment is outside the normal range based on whether the measurement value of a sensor installed in the facility or equipment is outside the normal range. For example, Patent Document 1 discloses a control device that acquires the measurement value (sensor signal and state amount) of a sensor installed in the facility or equipment, identifies the plant state by clustering the state amount, and judges that there is an abnormality if there is a state amount outside the normal range among the latest state amounts. The control device classifies the state amounts accumulated in the past into clusters, identifies the cluster of the plant state to which the state amount in the newly acquired sensor measurement value corresponds, and judges that the state amount outside the normal range is identified. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-109271 A Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in Patent Document 1 has a problem in that if there is no past state quantity data sufficient for cluster classification, it is not possible to identify the state of the plant, and it is not possible to determine whether the state of external devices such as facilities or equipment has deviated from the normal range.

[0005] The present disclosure has been made to solve the problems described above, and aims to make it possible to determine whether the state of an external device has deviated from the normal range even if there is not enough past data for cluster classification. [Means for solving the problem]

[0006] In order to achieve the above object, the normal range deviation diagnosis device according to the present disclosure includes a data acquisition unit, a data waveform learning unit, a future data estimation unit, a normal range definition unit, and a normal range deviation determination unit. The data acquisition unit acquires data indicating the latest value of a feature amount related to an external device. The data waveform learning unit extracts a data waveform, which is a predetermined number of consecutive data points, from feature amount data, which is time-series data that accumulates data indicating feature amounts related to the external device, and performs learning to aggregate similar data waveforms to generate a predicted waveform, which is an aggregated data waveform. The future data estimation unit estimates future data, which is a value of the future feature amount data, using the predicted waveform and the most recent feature amount data. The normal range definition unit defines a normal range for each value of the future data. The normal range deviation determination unit determines whether the state of the external device has deviated from the normal range based on whether the latest value of the feature amount related to the external device has deviated from the corresponding normal range. Effect of the Invention

[0007] According to the present disclosure, by extracting data waveforms from time series data that accumulates data indicating features related to an external device, aggregating similar data waveforms to generate a predicted waveform, and defining a normal range for each value of future data estimated using the predicted waveform and the most recent feature data, it becomes possible to determine whether the state of the external device has deviated from the normal range even if there is not enough past data for cluster classification. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a normal range deviation diagnosis system according to a first embodiment. [Diagram 2] FIG. 1 is a diagram showing an example of feature amount data according to the first embodiment; [Diagram 3] FIG. 1 is a diagram showing an example of parameters according to the first embodiment; [Figure 4] FIG. 1 is a diagram showing an example of extraction of a data waveform according to the first embodiment; [Diagram 5] FIG. 1 is a diagram showing an example of data waveform aggregation according to the first embodiment; [Figure 6] FIG. 13 is a diagram showing an example of extraction of a predicted waveform with the highest degree of matching according to the first embodiment; [Figure 7] FIG. 1 is a diagram showing an example of estimation of future measurement values ​​according to the first embodiment; [Figure 8] FIG. 1 shows an example of a definition of a normal range according to the first embodiment. [Figure 9] FIG. 13 is a diagram showing an example of determining whether or not there is a deviation from the normal range according to the first embodiment; [Figure 10A] FIG. 13 is a diagram showing an example of a determination that there is no deviation from the normal range in the determination of whether or not there is a deviation from the normal range according to the first embodiment; [Figure 10B] FIG. 13 is a diagram showing an example of a determination that a deviation has occurred in a determination of whether or not a deviation has occurred from a normal range according to the first embodiment; [Figure 11] 1 is a flowchart showing normal range deviation diagnosis processing according to the first embodiment. [Figure 12] 1 is a flowchart showing a data waveform learning process according to the first embodiment. [Figure 13] Flowchart showing future data estimation processing according to the first embodiment [Figure 14] FIG. 13 is a diagram showing a configuration example of a normal range deviation diagnosis system according to a second embodiment. [Figure 15] Flowchart showing parameter adjustment processing according to the second embodiment [Figure 16] FIG. 13 is a diagram showing an example of the configuration of a normal range deviation diagnosis system according to a third embodiment. [Figure 17A] FIG. 13 is a diagram showing an example of time-series data before processing for removing high-frequency components according to the third embodiment; [Figure 17B] FIG. 13 is a diagram showing an example of time-series data after a process for removing high-frequency components according to the third embodiment is performed; [Figure 18] Flowchart showing normal range deviation diagnosis processing according to embodiment 3 [Figure 19] FIG. 1 is a diagram showing an example of a hardware configuration of a normal range deviation diagnosis device according to first to third embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] A normal range deviation diagnostic device, a normal range deviation diagnostic system, a normal range deviation diagnostic method, and a program according to the present embodiment will be described in detail below with reference to the drawings. Note that the same or corresponding parts in the drawings are given the same reference numerals. In this embodiment, an example will be described in which it is determined whether the state of a facility or equipment installed inside or outside a factory deviates from the normal range.

[0010] (Embodiment 1) The configuration of a normal range deviation diagnosis system 100 according to the first embodiment will be described with reference to Fig. 1. The normal range deviation diagnosis system 100 includes a normal range deviation diagnosis device 1 that determines whether or not the state of an external device 3 deviates from a normal range, a sensor 2 that measures a feature amount related to the external device 3, and an external device 3 that is equipment or a device installed inside or outside a factory. The external device 3 is, for example, a processing machine, a robot, a motor, or the like. In the figure, one external device 3 is shown as a representative, but multiple external devices may be used. The normal range deviation diagnosis device 1 is capable of communicating with the sensor 2 and the external device 3 via a network. The network may be wired or wireless.

[0011] The sensor 2 and the external device 3 periodically transmit data indicating the latest values ​​of the feature quantities related to the external device 3 to the normal range deviation diagnosis device 1, for example, at a constant cycle. The latest values ​​of the feature quantities related to the external device 3 are, for example, values ​​measured immediately before the measurement values ​​of sound, vibration, temperature, etc. generated by a processing machine measured by the sensor 2, and the joint values ​​of a robot, the number of rotations of a motor, etc. measured by a measurement unit included in the external device 3. The measurement units included in the sensor 2 and the external device 3 are examples of measurement devices.

[0012] The normal range deviation diagnosis device 1 includes a data acquisition unit 11 that acquires data indicating the latest values ​​of feature quantities related to the external device 3 from the sensor 2 and the external device 3, and a data storage unit 12 that accumulates and stores the data indicating the feature quantities related to the external device 3 acquired by the data acquisition unit 11. The data indicating the feature quantities related to the external device 3 stored in the data storage unit 12 includes at least time information indicating either the measurement time or the time acquired by the data acquisition unit 11, and numerical information indicating the measurement value. Hereinafter, the time-series data obtained by accumulating the data indicating the feature quantities related to the external device 3 stored in the data storage unit 12 is referred to as feature quantity data.

[0013] The normal range deviation diagnosis device 1 also includes a data waveform learning unit 13 that extracts data waveforms from feature data and generates a predicted waveform by learning to aggregate similar data waveforms, and a future data estimation unit 14 that estimates a value of future feature data using the predicted waveform and the most recent feature data. Hereinafter, the value of future feature data will be referred to as future data. The normal range deviation diagnosis device 1 also includes a normal range definition unit 15 that defines a normal range for the future data estimated by the future data estimation unit 14, and a normal range deviation determination unit 16 that compares data indicating the latest value of the feature related to the external device 3 newly stored in the data storage unit 12 with the normal range defined by the normal range definition unit 15, and determines whether the state of the external device 3 deviates from the normal range based on whether the latest value deviates from the normal range.

[0014] Every time the data acquisition unit 11 acquires data indicating the latest values ​​of feature quantities related to the external device 3 from the sensor 2 and the external device 3, it stores the data in the data storage unit 12. The data storage unit 12 stores feature quantity data which is time-series data in which data indicating feature quantities related to the external device 3 are accumulated.

[0015] The feature amount data stored in the data storage unit 12 will be described with reference to FIG. 2. In the example of FIG. 2, the feature amount data has an item of "measurement time" indicating the measurement time when the feature amount was measured, and items of "measurement value A", "measurement value B", and "measurement value C" indicating the values ​​of the measured feature amount. Here, the number of measurement values ​​for one piece of time information is three, but this is not limited thereto and may be one or more. In addition, the unit and number of digits of the measurement value are arbitrary. Hereinafter, the type of measurement value of the feature amount included in the feature amount data is referred to as a data dimension.

[0016] Returning to Fig. 1, the data waveform learning unit 13 reads out the feature data from the data storage unit 12, learns the data waveform, and generates a predicted waveform. The data waveform learning unit 13 extracts data waveforms, which are consecutive data points (m) from the feature data of the maximum number of data points (N), which is the maximum number of data points stored in the data storage unit 12, and performs a data waveform learning process of aggregating similar data waveforms until the number of data waveforms becomes equal to or less than the maximum number of data waveforms (pt). The data waveform learning unit 13 stores the aggregated data waveforms that are equal to or less than the maximum number of data waveforms (pt) in the data storage unit 12 as predicted waveforms.

[0017] As shown in Fig. 3, the data storage unit 12 stores, as parameters, for example, the maximum number of data points (N), the number of data waveform points (m), the maximum number of data waveforms (pt), and the number of predicted points (p) for estimating future data. These parameters can be set by the user. The number of predicted points (p) is a parameter used by the future data estimation unit 14 described later. The normal range deviation diagnosis device 1 may be connected to a user terminal used by the user, and the data storage unit 12 may store the values ​​of the parameters acquired from the user terminal.

[0018] In the example of FIG. 3, the maximum number of data points (N) is 5000. However, for example, when the normal range deviation diagnosis device 1 is started, even if the number of data points of the feature amount data stored in the data storage unit 12 is less than 5000, from the feature amount data of that number of data points, a data waveform that is m consecutive data points of the number of data waveform points (m) is extracted, and when the data waveform learning process that aggregates similar data waveforms until the number of data waveforms becomes less than or equal to the maximum number of data waveforms (pt) becomes executable, the data waveform learning unit 13 may start the data waveform learning process.

[0019] Here, the method of learning the data waveform performed by the data waveform learning unit 13 will be described with reference to FIGS. 4 and 5. The data waveform learning unit 13 cuts out a data waveform of m consecutive points (m < N) from the past from the starting point among the N points of the feature amount data stored in the data storage unit 12. This data waveform cutting process is repeated N - m + 1 times by shifting the starting point one data point at a time from the latest value to the past, so that N - m + 1 data waveforms can be obtained. In the example of FIG. 4, since N = 6 and m = 4, 6 - 4 + 1 = 3 data waveforms are cut out. The data waveform learning unit 13 learns the data waveform using machine learning and aggregates it so that the number of data waveforms becomes less than or equal to the maximum number of data waveforms (pt).

[0020] As the machine learning algorithm used by the data waveform learning unit 13, for example, known algorithms such as supervised learning, unsupervised learning, and reinforcement learning can be used. As an example, the case where "self-supervised learning", which is unsupervised learning, is applied will be described. Unsupervised learning refers to a method of learning a feature in learning data by giving the learning device learning data that does not include a result (label). Self-supervised learning is a method in which a teacher signal (the label is automatically generated) is learned in the first stage, and the learned teacher signal is used for some supervised learning task after the second stage.

[0021] Specifically, the data waveform learning unit 13 uses one of the data waveforms as teacher data, learns the similarity with the data, and aggregates the data waveforms with high similarity into the teacher data. Next, the data waveform learning unit 13 uses the data waveform with low similarity with the previous teacher data as the next teacher data, learns the similarity with the data waveform in the same manner, and aggregates the data waveform into the teacher data. This is repeated, and when the aggregation of the data waveforms progresses and the number of data waveforms becomes equal to or less than the maximum number of data waveforms pt, the learning is terminated. In the example of FIG. 5, three data waveforms with high similarity are aggregated into one data waveform. The data waveform learning unit 13 stores the aggregated data waveforms that are equal to or less than the maximum number of data waveforms pt as predicted waveforms in the data storage unit 12.

[0022] Returning to FIG. 1, the future data estimation unit 14 uses the predicted waveform and the most recent feature amount data stored in the data storage unit 12 to estimate future data for the number of predicted points (p).

[0023] Here, the method of estimating future data executed by the future data estimation unit 14 will be described with reference to FIG. 6 and FIG. 7. The future data estimation unit 14 reads out the predicted waveform from the data storage unit 12, and calculates the degree of matching between the feature amount data for past (mp) points from the latest time, which is the most recent feature amount data, and the data for (mp) points from the start point of the predicted waveform. The degree of matching is calculated by a method in which the smaller the absolute error between the feature amount data and the predicted data for (mp) points of data, the higher the degree of matching. For example, for (mp) points of data, the average value of the absolute error between the feature amount data and the predicted data is calculated, and the reciprocal of the average value is set as the degree of matching. In this case, when the absolute error between the feature amount data and the predicted data is 0, the degree of matching is the highest. The future data estimation unit 14 extracts the predicted waveform with the highest degree of matching. The future data estimation unit 14 overlaps and fits the data for (mp) points from the start point of the extracted predicted waveform and the data for past (mp) points from the latest time of the feature amount data, and calculates an estimate for the predicted points (p) points in the future from the latest time. The future data estimation unit 14 stores in the data storage unit 12 future data indicating the estimated values ​​for the calculated predicted number of points (p).

[0024] In the example of FIG. 6, since m=4 and p=2, the future data estimation unit 14 calculates the degree of matching between the data for two points in the past from the latest time and the data for two points from the starting points of the predicted waveforms W1, W2, and W3. The future data estimation unit 14 extracts the predicted waveform W1 with the highest degree of matching. As shown in FIG. 7, the future data estimation unit 14 overlaps and fits the data for two points in the past from the starting point of the extracted predicted waveform W1 and the data for two points in the past from the latest time, and calculates values ​​corresponding to the third and fourth points of the predicted waveform W1 as estimates for two points in the future from the latest time. Hereinafter, the estimate of the first point in the future from the latest time is referred to as the one-step ahead estimate, and the estimate of the second point is referred to as the two-step ahead estimate.

[0025] Returning to FIG. 1, the normal range definition unit 15 reads future data from the data storage unit 12 and defines a normal range for each value of the future data. The normal range may be defined as an estimated value ± a fixed value, or may be defined based on statistics (data range, standard deviation, variance, etc.) obtained when the data waveform learning unit 13 learns the data waveform. For example, the width of the normal range is set to the estimated value ± (1 / N of the data range: N≧2), or the normal range is set to the estimated value ± standard deviation. By defining the normal range based on the statistics of past data for each data dimension, it is possible to set a normal range that matches the characteristics of each feature amount data.

[0026] Here, a method of defining the normal range executed by the normal range defining unit 15 will be described with reference to Fig. 8. In the example of Fig. 8, the normal range defining unit 15 defines the normal range for each of the one-step-ahead estimated value and the two-step-ahead estimated value shown in Fig. 7 as an estimated value ± a fixed value.

[0027] 1, the normal range deviation determination unit 16 determines whether or not the state of the external device 3 deviates from the normal range based on whether or not the latest value of the feature amount related to the external device 3 stored in the data storage unit 12 deviates from the normal range defined by the normal range definition unit 15. The normal range deviation determination unit 16 stores determination result information indicating the determination result in the data storage unit 12.

[0028] Here, a method for determining whether or not the state of the external device 3 deviates from the normal range will be described with reference to Fig. 9, Fig. 10A, and Fig. 10B. In the example of Fig. 9, the normal range deviation determination unit 16 determines that the state of the external device 3 deviates from the normal range when the latest value of the feature amount related to the external device 3 (the value at the latest time in the figure) does not fall within the normal range set in the one-step-ahead estimated value. In the examples of Figs. 10A and 10B, the normal range deviation determination unit 16 determines that the state of the external device 3 deviates from the normal range when the latest value of the feature amount related to the external device 3 deviates from the normal range twice consecutively.

[0029] 10A, if the value of the feature amount at time T1 falls within the normal range set in the one-step-ahead estimated value and the value of the feature amount at time T2 does not fall within the normal range set in the two-step-ahead estimated value, the normal range deviation determination unit 16 determines that the state of the external device 3 does not fall within the normal range and is normal. As shown in FIG. 10B, if the value of the feature amount at time T1 does not fall within the normal range set in the one-step-ahead estimated value and the value of the feature amount at time T2 does not fall within the normal range set in the two-step-ahead estimated value, the normal range deviation determination unit 16 determines that the state of the external device 3 falls outside the normal range.

[0030] The method of determining whether the state of the external device 3 deviates from the normal range may be to determine that the state is outside the normal range when the state does not fall within the normal range once as shown in FIG. 9, or to determine that the state is outside the normal range when the state does not fall within the normal range twice consecutively as shown in FIG. 10A and FIG. 10B. Alternatively, the state may be determined to be outside the normal range when the state does not fall within the normal range three or more consecutive times. In this case, the prediction score (p) is 3 or more. Alternatively, a condition may be set such that the state is determined to be outside the normal range when the number of times that the state does not fall within the normal range is equal to or exceeds a threshold value among a predetermined number of times. In this case, the prediction score (p) is a predetermined number of times. The accuracy of the deviation determination can be improved by evaluating the number of times that the state does not fall within the normal range.

[0031] Next, the normal range deviation diagnostic process executed by the normal range deviation diagnostic device 1 will be described with reference to FIG. 11. The normal range deviation diagnostic process shown in FIG. 11 starts, for example, when the power supply to the normal range deviation diagnostic device 1 is turned on. If the data acquisition unit 11 of the normal range deviation diagnostic device 1 does not acquire data indicating the latest value of the feature amount related to the external device 3 from the sensor 2 and the external device 3 (step S11; NO), the process proceeds to step S21. If the data acquisition unit 11 acquires data indicating the latest value of the feature amount related to the external device 3 from the sensor 2 and the external device 3 (step S11; YES), the data indicating the latest value of the feature amount related to the external device 3 is stored in the data storage unit 12 (step S12). The data storage unit 12 stores time-series feature amount data as shown in FIG. 2. In the example of FIG. 2, the feature amount data has an item of "measurement time" indicating the measurement time at which the feature amount was measured, and items of "measurement value A", "measurement value B", and "measurement value C" indicating the value of the measured feature amount.

[0032] Returning to FIG. 11, if the number of data points of the feature data stored in the data storage unit 12 has not reached the maximum number of data points (step S13; NO), the process proceeds to step S21. If the number of data points of the feature data stored in the data storage unit 12 has reached the maximum number of data points (step S13; YES), the data waveform learning unit 13 reads out the feature data from the data storage unit 12 and performs a data waveform learning process to learn the data waveform (step S14). The data waveform learning unit 13 stores the data waveform aggregated by the data waveform learning process as a predicted waveform in the data storage unit 12 (step S15). Note that even if the number of data points of the feature data stored in the data storage unit 12 does not reach the maximum number of data points, in the case of a configuration in which data waveform learning is started at the time when the data waveform learning process becomes executable, it is determined in step S13 whether the data waveform learning process is executable.

[0033] The future data estimation unit 14 reads out the predicted waveform stored in the data storage unit 12, and performs a future data estimation process for estimating future data using the predicted waveform and the most recent feature amount data (step S16). The future data estimation unit 14 stores the estimated future data in the data storage unit 12 (step S17). The normal range definition unit 15 defines a normal range for the future data stored in the data storage unit 12 (step S18). In the example of FIG. 8, a normal range is defined for the one-step-ahead estimated value and the two-steps-ahead estimated value, which are future data.

[0034] Returning to FIG. 11, the normal range deviation determination unit 16 determines whether the state of the external device 3 has deviated from the normal range based on whether the latest value of the feature amount regarding the external device 3 acquired by the data acquisition unit 11 in step S11 and stored in the data storage unit 12 has deviated from the normal range defined by the normal range definition unit 15 (step S19). The normal range deviation determination unit 16 stores determination result information indicating the determination result in the data storage unit 12 (step S20). If the power of the normal range deviation diagnosis device 1 is not OFF (step S21; NO), the process returns to step S11, and steps S11 to S21 are repeated. When the power is turned OFF (step S21; YES), the process ends.

[0035] The data waveform learning process executed in step S14 of the flowchart shown in FIG. 11 will be described with reference to FIG. 12. The data waveform learning unit 13 of the normal range deviation diagnosis device 1 sets i = 0 (step S31), and cuts out a data waveform of m consecutive points (m < N) in the past with the latest value - the i-th value among the N-point feature amount data stored in the data storage unit 12 as the starting point (step S32). The data waveform learning unit 13 sets i = i + 1 (step S33), and determines whether m + i ≤ N (step S34). If m + i ≤ N (step S34; YES), the process returns to step S32, and steps S32 to S34 are repeated. If m + i > N (step S34; NO), the data waveform learning unit 13 learns the data waveform using machine learning and aggregates it until the number of data waveforms becomes less than or equal to the maximum number of data waveforms (pt) (step S35), and the process ends.

[0036] The future data estimation process executed in step S16 of the flowchart shown in Fig. 11 will be described with reference to Fig. 13. The future data estimation unit 14 of the normal range deviation diagnosis device 1 calculates the degree of matching between feature amount data for past (mp) points from the latest time, which is the most recent feature amount data, and data for (mp) points from the start point of the predicted waveform (step S41). The future data estimation unit 14 extracts the predicted waveform with the highest degree of matching (step S42). The future data estimation unit 14 overlaps and fits the extracted data for (mp) points from the start point of the predicted waveform and the most recent feature amount data for past (mp) points from the latest time, which is the most recent feature amount data, to calculate estimates for the predicted number of points (p) in the future from the latest time (step S43), and ends the process.

[0037] 11 may be performed for each data dimension, or may be performed for multiple data dimensions showing similar data trends. Steps S14 to S18 do not have to be performed every cycle. For example, after the first execution, they may be configured to be performed every predetermined number of cycles, such as once every 5 cycles or once every 10 cycles.

[0038] According to the normal range deviation diagnosis device 1 of the first embodiment, a data waveform is extracted from feature amount data, which is time-series data indicating feature amounts related to the external device 3, similar data waveforms are aggregated to generate a predicted waveform, and a normal range is defined for each value of future data estimated using the predicted waveform and the most recent feature amount data, thereby making it possible to determine whether or not the state of the external device 3 has deviated from the normal range even if there is no past data sufficient for cluster classification. Furthermore, in the technology described in Patent Document 1, it is necessary to cluster classify state amount data accumulated in the past and label various states, which results in a large calculation load, but in the normal range deviation diagnosis device 1 of the first embodiment, the amount of data to be learned is smaller than in the technology described in Patent Document 1, and labeling is not required, so that the calculation load can be reduced.

[0039] (Embodiment 2) In the second embodiment, the parameters stored in data storage unit 12 are automatically adjusted to achieve the conditions that provide the best accuracy in prediction results. The configuration of normal range deviation diagnostic system 100 according to the second embodiment will be described with reference to Fig. 14. As shown in Fig. 14, normal range deviation diagnostic device 1 of normal range deviation diagnostic system 100 includes data acquisition unit 11, data storage unit 12, data waveform learning unit 13, future data estimating unit 14, normal range defining unit 15, normal range deviation determining unit 16, and in addition, parameter adjusting unit 17 that adjusts parameters.

[0040] Parameter adjustment unit 17 reads the parameter to be adjusted and the variation range of the parameter. The parameter to be adjusted and the variation range of the parameter may be acquired from an external device or system (not shown), may be input by a user, may be acquired from a user terminal, or may be written in a setting file accessible by parameter adjustment unit 17. Alternatively, an API (Application Programming Interface) for directly calling parameter adjustment unit 17 may be created and passed as an argument to this API.

[0041] The parameter adjustment unit 17 sets the value of the parameter to be adjusted to the minimum value of the variation range, and causes the data waveform learning unit 13 and the future data estimation unit 14 to execute the data waveform learning process and the future data estimation process, respectively. At this time, the learning data used by the data waveform learning unit 13 to learn the data waveform is not based on the current time, but is obtained by extracting data of a number of data greater than the number of data waveform points (m) from data older than the latest time of the feature amount data stored in the data storage unit 12 by P points or more, and using this data as learning data. The future data estimation unit 14 calculates an estimate of point P, which is a time in the future of the latest point of the learning data. The error between this estimate of point P and the actual measurement value included in the feature amount data stored in the data storage unit 12 is evaluated. The parameter adjustment unit 17 adds new data of a time one point in the future of the latest point of the learning data to the learning data, and repeats the above process, and ends the process when there are no more latest points that can be added. This process is hereinafter referred to as error evaluation process.

[0042] The parameter adjustment unit 17 repeatedly executes the error evaluation process by increasing the parameter value by one unit within the variation range, and obtains a combination of the parameter value and the error evaluation result. When the parameter value reaches the maximum value of the variation range, the parameter adjustment unit 17 determines the optimal value of the parameter. The optimal value is determined based on the error evaluation result. For example, as a selection criterion for the optimal value, an average error rate for each parameter value, an average value of the absolute values ​​of the error rates, or the variance or standard deviation of the error may be used. In addition, the number of times and the amount of deviation of the actual measurement value from the normal range may be used as the error using the normal range obtained by applying the processing of the normal range definition unit 15. The parameter adjustment unit 17 determines the optimal value of the parameter based on the error evaluation method and the selection criterion for the optimal value that are predetermined or specified by the user. The parameter adjustment unit 17 rewrites the parameter stored in the data storage unit 12 to the optimal value. Other functions of the normal range deviation diagnosis device 1 are the same as those of the first embodiment.

[0043] Here, the parameter adjustment process executed by parameter adjustment unit 17 will be described with reference to Fig. 15. The parameter adjustment process shown in Fig. 15 may be automatically executed every time one cycle of the normal range deviation diagnosis process is executed, or may be executed in response to a command from a user, or may be configured to be executed when the prediction accuracy falls below a threshold by adding a function for automatically evaluating the prediction accuracy after one cycle of the normal range deviation diagnosis process is executed.

[0044] The parameter adjustment unit 17 reads the parameter to be adjusted and the variation range of the parameter (step S51). The parameter adjustment unit 17 sets j=the minimum value of the variation range (step S52), and causes the data waveform learning unit 13 and the future data estimation unit 14 to execute the data waveform learning process and the future data estimation process, respectively (step S53). The parameter adjustment unit 17 evaluates the error between the estimated value of point P calculated by the future data estimation unit 14 and the actual measurement value included in the feature amount data stored in the data storage unit 12 (step S54). The parameter adjustment unit 17 sets j=j+1 (step S55), and judges whether j≦the maximum value of the variation range (step S56). If j≦the maximum value of the variation range (step S56; YES), the process returns to step S53, and steps S53 to S56 are repeated. If j is not equal to or less than the maximum value of the variation range (step S56; NO), parameter adjustment unit 17 determines the optimum value of the parameter based on the error evaluation method and optimum value selection criteria designated by the user (step S57), and ends the process. Parameter adjustment unit 17 rewrites the parameter stored in data storage unit 12 to the optimum value.

[0045] Although the above description has been given with reference to an example in which only one parameter is adjusted in the parameter adjustment process, two or more parameters may be evaluated simultaneously. In this case, the process of changing the parameter value by one unit within the variation range may be performed in multiple stages.

[0046] According to the normal range deviation diagnostic device 1 according to the second embodiment, the parameters can be constantly maintained at optimal values ​​by performing the parameter adjustment process. Also, the effort required for the user to set the parameters can be reduced.

[0047] (Embodiment 3) In the third embodiment, a process of removing high frequency components is performed from the feature amount data written in the data storage unit. The configuration of a normal range deviation diagnostic system 100 according to the third embodiment will be described with reference to Fig. 16. As shown in Fig. 16, a normal range deviation diagnostic device 1 of the normal range deviation diagnostic system 100 includes a data acquisition unit 11, a data storage unit 12, a data waveform learning unit 13, a future data estimation unit 14, a normal range definition unit 15, and a normal range deviation determination unit 16, as well as a high frequency component removal unit 18 that performs a process of removing high frequency components from the feature amount data.

[0048] The high frequency component removal unit 18 performs a process of removing high frequency components from the feature data stored in the data storage unit 12. A general low pass filter process can be used for the process of removing high frequency components. FIG. 17A shows an example of feature data stored in the data storage unit 12 before the process of removing high frequency components is performed. By performing the process of removing high frequency components, the data waveform is smoothed and noise is removed, as shown in FIG. 17B. Other functions of the normal range deviation diagnosis device 1 are the same as those of the first embodiment.

[0049] Here, the normal range deviation diagnostic processing executed by the normal range deviation diagnostic device 1 according to the third embodiment will be described with reference to Fig. 18. The normal range deviation diagnostic processing shown in Fig. 18 is a flowchart in which a step of removing high frequency components from the feature amount data stored in the data storage unit 12 is added before the data waveform learning processing of the flowchart of the normal range deviation diagnostic processing according to the first embodiment shown in Fig. 11. Steps S61 to S63 and steps S66 to S72 are similar to steps S11 to S13 and steps S15 to S21, respectively, and therefore description thereof will be omitted.

[0050] If the number of data points of the feature data stored in the data storage unit 12 has reached the maximum number of data points (step S63; YES), the high frequency component removal unit 18 performs a process of removing high frequency components from the feature data stored in the data storage unit 12 (step S64). The data waveform learning unit 13 reads out the feature data from which the high frequency components have been removed from the data storage unit 12, and performs a data waveform learning process of learning the data waveform (step S65).

[0051] According to the normal range deviation diagnosis device 1 of embodiment 3, by performing a process of removing high frequency components from the feature data before performing a data waveform learning process, it is possible to determine deviation from the normal range without being affected by noise, even for feature data that includes fine noise components.

[0052] The hardware configuration of normal range deviation diagnostic device 1 will be described with reference to Fig. 19. As shown in Fig. 19, normal range deviation diagnostic device 1 includes temporary storage unit 101, storage unit 102, calculation unit 103, input unit 104, transmission / reception unit 105, and display unit 106. Temporary storage unit 101, storage unit 102, input unit 104, transmission / reception unit 105, and display unit 106 are all connected to calculation unit 103 via a BUS.

[0053] The calculation unit 103 is, for example, a CPU (Central Processing Unit). The calculation unit 103 executes the processes of the data waveform learning unit 13, the future data estimation unit 14, the normal range definition unit 15, the normal range deviation determination unit 16, the parameter adjustment unit 17, and the high frequency component removal unit 18 in accordance with the control program stored in the storage unit 102.

[0054] The temporary storage unit 101 is, for example, a random-access memory (RAM). The temporary storage unit 101 loads a control program stored in the storage unit 102 and is used as a working area for the calculation unit 103.

[0055] The storage unit 102 is a non-volatile memory such as a flash memory, a hard disk, a DVD-RAM (Digital Versatile Disc - Random Access Memory), or a DVD-RW (Digital Versatile Disc - ReWritable). The storage unit 102 pre-stores a program for causing the calculation unit 103 to perform the processing of the normal range deviation diagnosis device 1, and also supplies data stored by this program to the calculation unit 103 according to an instruction from the calculation unit 103, and stores the data supplied from the calculation unit 103. The data storage unit 12 is configured in the storage unit 102.

[0056] The input unit 104 is an interface device that connects input devices such as a keyboard, a pointing device, and a voice input device to the BUS. Information input by the user is supplied to the calculation unit 103 via the input unit 104. In a configuration in which the user inputs parameters to be stored in the data storage unit 12, the user inputs the parameter values ​​via the input unit 104. In a configuration in which the user inputs parameters to be adjusted and the parameter variation ranges, the input unit 104 functions as the parameter adjustment unit 17.

[0057] The transmitting / receiving unit 105 is a network termination device or a wireless communication device that connects to the network, and a serial interface or a LAN (Local Area Network) interface that connects to them. The transmitting / receiving unit 105 functions as the data acquiring unit 11. Information input by the user is supplied to the calculating unit 103 via the input unit 104. In a configuration in which the parameters stored in the data storage unit 12 are acquired from a user terminal, the parameter values ​​are acquired from the user terminal via the transmitting / receiving unit 105. In a configuration in which the parameters to be adjusted and the parameter variation ranges are acquired from the user terminal, the transmitting / receiving unit 105 functions as the parameter adjusting unit 17.

[0058] Display unit 106 is a display device such as an LCD (Liquid Crystal Display) or an organic EL (electroluminescence) display. In a configuration in which the user inputs parameters stored in data storage unit 12, display unit 106 displays an input screen for inputting parameter values. In a configuration in which the user inputs parameters to be adjusted and the variation ranges of the parameters, display unit 106 displays an input screen for inputting parameters to be adjusted and the variation ranges of the parameters.

[0059] The processing of the data acquisition unit 11, data storage unit 12, data waveform learning unit 13, future data estimation unit 14, normal range definition unit 15, normal range deviation determination unit 16, parameter adjustment unit 17, and high frequency component removal unit 18 of the normal range deviation diagnosis device 1 shown in Figures 1, 14, and 16 is executed by a control program processing using temporary storage unit 101, calculation unit 103, storage unit 102, input unit 104, transmission / reception unit 105, display unit 106, etc. as resources.

[0060] Furthermore, the above hardware configuration and flowchart are merely examples and can be changed or modified as desired.

[0061] Core parts of the normal range deviation diagnostic device 1, such as the calculation unit 103, temporary storage unit 101, storage unit 102, input unit 104, transmission / reception unit 105, and display unit 106, can be realized by using a normal computer system, not a dedicated system. For example, the normal range deviation diagnostic device 1 that executes the above-mentioned processes may be configured by storing and distributing a computer-readable recording medium such as a flexible disk, a CD-ROM (Compact Disc - Read Only Memory), or a DVD-ROM (Digital Versatile Disc - Read Only Memory), and installing the computer program on a computer. Alternatively, the normal range deviation diagnostic device 1 may be configured by storing the computer program in a storage device of a server device on a communication network, such as the Internet, and downloading the computer program by a normal computer system.

[0062] In addition, when the functions of the normal range deviation diagnostic device 1 are realized by sharing the functions of an OS (Operating System) and an application program, or by cooperation between the OS and an application program, only the application program portion may be stored in a recording medium or storage device.

[0063] It is also possible to superimpose a computer program on a carrier wave and provide it via a communication network. For example, the computer program may be posted on a bulletin board system (BBS) on the communication network and provided via the communication network. The computer program may then be started and executed under the control of the OS in the same way as other application programs, thereby enabling the above-mentioned processing to be performed.

[0064] In the above first to third embodiments, examples have been described in which it is determined whether the state of a facility or device installed inside or outside a factory deviates from a normal range, but the present invention is not limited to this. The normal range deviation diagnosis device 1 can determine whether the state of an external device capable of measuring some feature value deviates from a normal range.

[0065] In the above first to third embodiments, the normal range deviation diagnosis device 1 is connected to the sensor 2 and the external device 3, but is not limited thereto. For example, when the data indicating the characteristic quantities related to the external device 3 are only the measurement values ​​of the sound, vibration, temperature, etc. generated by the processing machine measured by the sensor 2, the normal range deviation diagnosis device 1 may be connected only to the sensor 2. When the data indicating the characteristic quantities related to the external device 3 are only the measurement values ​​of the joint values ​​of the robot, the number of rotations of the motor, etc. measured by a measurement unit included in the external device 3, the normal range deviation diagnosis device 1 may be connected only to the external device 3. Alternatively, the data acquisition unit 11 of the normal range deviation diagnosis device 1 may acquire data indicating the latest values ​​of the characteristic quantities related to the external device 3 from an external device or system.

[0066] In the above-mentioned first to third embodiments, the data indicating the feature quantities related to the external device 3 stored in the data storage unit 12 includes time information as information indicating a time series, but is not limited to this. For example, in a configuration in which the sensor 2 and the external device 3 periodically transmit data indicating the latest values ​​of the feature quantities related to the external device 3 to the normal range deviation diagnosis device 1 at regular intervals, the information indicating the time series may be information indicating the order of acquisition.

[0067] In the above-mentioned first to third embodiments, the normal range deviation judgment unit 16 stores the judgment result information indicating the judgment result in the data storage unit 12, but this is not limited thereto. For example, when the normal range deviation judgment unit 16 judges that the state of the external device 3 deviates from the normal range, the normal range deviation judgment unit 16 may be configured to notify the user that the state of the external device 3 deviates from the normal range, or to notify the control unit that controls the external device 3 that the state of the external device 3 deviates from the normal range, or, when the normal range deviation diagnosis device 1 is applied to the control unit that controls the external device 3, to notify the control unit that controls the external device 3 that the state of the external device 3 deviates from the normal range. An example of the control unit that controls the external device 3 is a programmable logic controller (PLC). When the control unit or the control unit that controls the external device 3 is notified that the state of the external device 3 deviates from the normal range, the control unit or the control unit controls the external device 3 to return the state of the external device 3 to the normal range. When the feature amount of the external device 3 is a parameter that can be controlled by the control unit or the control unit, the stable operation of the external device 3 can be realized by keeping the value within the normal range.

[0068] In the above-mentioned third embodiment, the data waveform learning unit 13 performs the data waveform learning process on the feature data after removing the high-frequency components, but this is not limited thereto. The data waveform learning unit 13 may perform the data waveform learning process on both the feature data before removing the high-frequency components and the feature data after removing the high-frequency components to generate two types of predicted waveforms. In this case, the future data estimation unit 14 estimates two types of future data using the two types of predicted waveforms and the most recent feature data, the normal range definition unit 15 defines two types of normal ranges for each value of the two types of future data, and the normal range deviation determination unit 16 determines whether the state of the external device 3 has deviated from the normal range based on whether the latest value of the feature related to the external device 3 has deviated from the corresponding two types of normal ranges. By using both the raw data with large fluctuations and the low-frequency components with small fluctuations, the probability of erroneous determination can be reduced.

[0069] Although the preferred embodiments have been described in detail above, the present invention is not limited to the above-described embodiments, and various modifications and substitutions can be made to the above-described embodiments without departing from the scope of the claims.

[0070] In addition, various embodiments and modifications of the present disclosure are possible without departing from the broad spirit and scope of the present disclosure. Moreover, the above-mentioned embodiment 1 is intended to explain the present disclosure and does not limit the scope of the present disclosure. That is, the scope of the present disclosure is indicated by the claims, not the embodiments. Various modifications made within the scope of the claims and within the scope of the disclosure equivalent thereto are considered to be within the scope of the present disclosure. [Explanation of symbols]

[0071] 1 Normal range deviation diagnosis device, 2 Sensor, 3 External device, 11 Data acquisition section, 12 Data storage section, 13 Data waveform learning section, 14 Future data estimation section, 15 Normal range definition section, 16 Normal range deviation determination section, 17 Parameter adjustment section, 18 High frequency component removal section, 100 Normal range deviation diagnosis system, 101 Temporary storage section, 102 Storage section, 103 Calculation section, 104 Input section, 105 Transmission / reception section, 106 Display section.

Claims

1. A data acquisition unit that acquires data showing the latest values ​​of feature quantities related to external devices, A data waveform learning unit extracts a predetermined number of consecutive data points, which are data waveforms, from feature data, which is time-series data that stores data indicating features related to the external device, performs learning to aggregate similar data waveforms, and generates a predicted waveform, which is the aggregated data waveform. A future data estimation unit estimates future data, which is the future value of the feature data, using the predicted waveform and the most recent feature data. A normal range definition unit defines a normal range for each value of the aforementioned future data, A normal range deviation determination unit determines whether the state of the external device has deviated from the normal range based on whether the latest value of the feature quantity related to the external device has deviated from the corresponding normal range, A diagnostic device for detecting deviations from the normal range, equipped with the necessary components.

2. The normal range deviation determination unit determines that the state of the external device has deviated from the normal range if the latest value of the feature quantity relating to the external device deviates from the normal range for all of the specified normal ranges, or if it deviates from the normal range more than a threshold number of times within a predetermined number of times. The normal range deviation diagnostic device according to claim 1.

3. The data waveform learning unit extracts data waveforms, which are a predetermined number of consecutive data points, from feature data with a predetermined maximum number of data points, and performs a data waveform learning process to generate the predicted waveform by aggregating similar data waveforms from the extracted data waveforms until the number of similar data waveforms is less than or equal to a predetermined maximum number of data waveforms. The future data estimation unit performs a future data estimation process that estimates a predetermined number of predicted points of future data using the predicted waveform and the most recent feature data. The system further includes a parameter adjustment unit that varies the value of at least one of the parameters, the maximum number of data points, the number of data waveform points, the maximum number of data waveforms, and the number of predicted points, within a predetermined range of variation, causes the data waveform learning unit and the future data estimation unit to perform the data waveform learning process and the future data estimation process, respectively, repeatedly performs an error evaluation process to evaluate the error with the measured value, and determines the optimal value of the parameter based on the evaluation result of the error evaluation process. A diagnostic device for diagnosing deviation from the normal range according to claim 1 or 2.

4. The system further includes a high-frequency component removal unit that performs a process to remove high-frequency components from the aforementioned feature data. The data waveform learning unit extracts a predetermined number of data points from the feature data from which high-frequency components have been removed, performs learning to aggregate similar data waveforms, and generates the predicted waveform. A diagnostic device for diagnosing deviation from the normal range according to claim 1 or 2.

5. The data waveform learning unit extracts a predetermined number of data points from the feature data before and after the removal of high-frequency components, performs learning to aggregate similar data waveforms, and generates two types of predicted waveforms. The future data estimation unit estimates two types of future data using the two types of predicted waveforms and the most recent feature data. The normal range definition unit defines two types of normal ranges for each of the two types of future data values, The normal range deviation determination unit determines whether the state of the external device has deviated from the normal range based on whether the latest value of the feature quantity related to the external device has deviated from the two corresponding normal ranges. The normal range deviation diagnostic device according to claim 4.

6. The normal range definition unit defines the normal range based on the statistical quantities obtained when the data waveform learning unit learns the data waveform. A diagnostic device for diagnosing deviation from the normal range according to claim 1 or 2.

7. The system further includes a control unit for controlling the aforementioned external device, The control unit, when the normal range deviation determination unit determines that the state of the external device has deviated from the normal range, performs control to return the state of the external device to the normal range. A diagnostic device for diagnosing deviation from the normal range according to claim 1 or 2.

8. A normal range deviation diagnostic device according to claim 1 or 2, A measuring device for measuring the characteristic quantities of the external device, Equipped with, The data acquisition unit acquires data from the measuring device that shows the latest values ​​of the feature quantities related to the external device. A diagnostic system for diagnosing deviations from the normal range.

9. A normal range deviation diagnostic device according to claim 1 or 2, A control device for controlling the external device, Equipped with, The control device, when the normal range deviation determination unit determines that the state of the external device has deviated from the normal range, performs control to return the state of the external device to the normal range. A diagnostic system for diagnosing deviations from the normal range.

10. The diagnostic device for deviations from the normal range performs the following: The steps include obtaining data showing the latest values ​​of features related to external devices, The steps include: extracting a predetermined number of data points from feature data, which is time-series data containing data representing features related to the external device; performing learning to aggregate similar data waveforms; and generating a predicted waveform, which is the aggregated data waveform. The steps include: estimating future data, which is the future value of the feature data, using the predicted waveform and the most recent feature data; The steps include defining a normal range for each value of the aforementioned future data, A step of determining whether the state of the external device has deviated from the normal range, based on whether the latest value of the feature quantity relating to the external device has deviated from the corresponding normal range, A diagnostic method for deviating from the normal range, comprising the following features.

11. Computers, A data waveform learning unit extracts a predetermined number of data points from feature data, which is time-series data containing data indicating features related to external devices, performs learning to aggregate similar data waveforms, and generates a predicted waveform, which is the aggregated data waveform. A future data estimation unit estimates future data, which is the future value of the feature data, using the predicted waveform and the most recent feature data. A normal range definition unit defines a normal range for each value of the aforementioned future data, and A normal range deviation determination unit determines whether the state of the external device has deviated from the normal range based on whether the latest value of the feature quantity related to the external device has deviated from the corresponding normal range. A program that makes it function as such.

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