JP6765769B2 - State change detection device and state change detection program - Google Patents

Description

The present invention relates to a state change detection device and a state change detection program.

For example, when the current, voltage, and vibration of a machine are measured to detect a state change such as an abnormality, the abnormality is generally determined based on past experience by a person or the like. That is, the threshold value of the state change is set by a person, and it is almost impossible for a machine having no abnormal data to perform the state change.

Patent Document 1 discloses a highly versatile anomaly detection device that does not depend on the type and number of objects and events that detect abnormal situations, and the space (place, time zone, etc.) that monitors those objects and events. ing.

Specifically, it is a device that detects that an abnormality has occurred in a target event. In this device, the time series of the input pattern, which is the data that changes depending on the event, is acquired, the characteristics of the transition of the acquired input pattern in the time series are analyzed, and the analyzed characteristics of the transition and the characteristics of the transition in advance are analyzed. It is compared with the determined reference values, and if they are not close to each other within a certain range, it is determined that an abnormality has occurred in the event. In this way, when it is determined that an abnormality has occurred as a result of the above comparison, an output means for performing an output operation to that effect is provided.

That is, an acquisition of a time series in which data that changes according to changes in events such as the movement of a person or object to be monitored is used as an input pattern, attention is paid to the transition of the pattern in the time series, and the feature amount in the transition is extracted. However, the occurrence of an abnormal situation is detected by comparing it with that in a normal case. Here, the "event" is a phenomenon that can be expressed as computer-processable data by a signal from a device sensor or the like, a report (data input) from a person, or the like, and "human behavior" in the house. It is also applicable to.

Cited Document 2 discloses a data prediction method or a data prediction system that uses a prediction model that realizes highly accurate prediction and that allows the prediction process to be understood.

The invention of Patent Document 2 is a data prediction method for predicting the future based on past achievements, wherein the prediction model includes a first prediction model that outputs a prediction value for at least one feature amount on the prediction target date. It is composed of a second prediction model that includes the prediction value output from the first prediction model as an input factor and outputs the prediction value for each predetermined time on the prediction target day.

Then, a prediction model construction means for constructing a prediction model using the collected nearest actual data and past actual data, and prediction execution for obtaining a predicted value by inputting input data for prediction into the constructed prediction model. Means, predictive error calculation means for calculating prediction error or model error from collected closest actual data and predicted value, calculation of correction coefficient or correction amount to correct the predicted value based on prediction error or model error, and correction It is realized by the predicted value correction means for obtaining the predicted value.

Japanese Unexamined Patent Publication No. 2003-256957 JP 2015-127914

However, as described above, the conventional prediction model aims to improve the accuracy of prediction, and a state change detection device capable of accurately capturing the state change only from the data when there is no state change is known. I wasn't.

The present invention has been made in view of the current state of the state change detection device as described above, and an object of the present invention is to obtain state change from only data when there is no state change, or only data in which state change occurs. It is an object of the present invention to provide a state change detection device and a state change detection program capable of detecting no state change.

The state fluctuation detection device according to the present invention is an explanatory variable obtained by simultaneously measuring each time using a plurality of sensors including a sensor for measuring an explanatory variable and a sensor for measuring an objective variable. A state fluctuation detection device that detects state fluctuations using the measurement explanatory variables and the obtained objective variables, the teacher objective variable, which is the objective variable of a fixed value, and the explanation of the fixed value. A prediction model created using teacher data composed of teacher explanatory variables, which are variables, and predicting the objective variable from the explanatory variables by machine learning , and the teacher explanatory variables of the teacher data are input to the prediction model. and teacher prediction data acquisition means for obtaining teacher prediction data, and the standard error obtaining means for obtaining a standard error which is the difference between the teacher object variables of the teacher data and the teacher predicted data, and the measurement objective variable, the measurement described It is the difference between the measurement prediction data acquisition means for obtaining the measurement prediction data by inputting the measurement explanatory variable into the prediction model using the measurement data composed of the variables, and the measurement prediction data and the measurement objective variable. It is characterized by comprising an error acquiring means for acquiring an error and a state variation detecting means for obtaining a degree of dissociation between the standard error and the error and detecting a state variation based on the dissociation degree.

The state change detection device according to the present invention is characterized by comprising a predictive model creating means for creating the predictive model using the teacher data.

In the state fluctuation detection device according to the present invention, the teacher data includes a plurality of sets of data composed of a teacher objective variable which is an objective variable of a fixed value and a teacher explanatory variable which is an explanatory variable of a fixed value. The data is provided with a set, and the measurement data is a set of data composed of a measurement objective variable which is a measured objective variable and a measurement explanatory variable which is a measured explanatory variable, which is obtained for each measurement. A plurality of data are collected according to the measurement times, the standard error acquisition means obtains the average of the standard errors of each set of the plurality of sets of the teacher data and sets the standard error, and the error acquisition means is used for each measurement time. It is characterized in that the average of the errors of is calculated and used as the error.
In the state change detection device according to the present invention, the state change detection means determines the state change based on the magnification of how many times the error amount obtained from each measurement data is compared with the reference error obtained from the teacher data. detecting, or error of deviation σt1 of each measurement times and deviation σST standard error, Shigumati2, and detects the state change on the basis of the ratio between the Shigumati3.

In the state change detecting apparatus according to the present invention, the state change detecting means has a dissociation degree threshold value, and when the obtained dissociation degree exceeds the dissociation degree threshold value, it is determined that there is a state change.

In the state fluctuation detecting apparatus according to the present invention, the state fluctuation detecting means has a dissociation degree abnormality threshold value to be determined as abnormal, and when the obtained dissociation degree exceeds the dissociation degree abnormality threshold value, it is determined that an abnormality has occurred. It is characterized by that.

The state fluctuation detection program according to the present invention uses a plurality of sensors composed of a sensor for measuring an explanatory variable and a sensor for measuring an objective variable, and simultaneously measures each time with the obtained explanatory variable. The computer of the state fluctuation detection device that detects the state change using a certain measurement explanatory variable and the obtained objective variable, the teacher objective variable, which is the objective variable of the fixed value, and the fixed value by the teacher explanatory variable is an explanatory variable generated using the teacher data composed, enter the explanatory variable by the machine learning prediction model for predicting the target variable, the teacher explanatory variable of the training data to the predictive model teacher prediction data acquisition means for obtaining teacher prediction data, standard error obtaining means for obtaining the standard error which is the difference between the teacher object variables of the teacher data and the teacher predicted data, and the measurement objective variable, and the measured explanatory variables A measurement prediction data acquisition means for inputting the measurement explanatory variable into the prediction model to obtain measurement prediction data, and acquiring an error which is a difference between the measurement prediction data and the measurement objective variable, using the measurement data composed of It is characterized in that it functions as a state fluctuation detecting means for obtaining a degree of dissociation between the standard error and the error and detecting a state change based on the degree of dissociation.

In the state fluctuation detection program according to the present invention, the teacher data is a set of data composed of a teacher objective variable which is an objective variable of a fixed value and a teacher explanatory variable which is an explanatory variable of a fixed value. The data includes a plurality of sets, and the measurement data is a set of data composed of a measurement objective variable which is a measured objective variable and a measurement explanatory variable which is a measured explanatory variable, which is obtained for each measurement. The data is a plurality of sets according to the measurement times, and the computer is used as the standard error acquisition means to obtain the average of the standard errors of each set of the plurality of sets of the teacher data and use it as the standard error. It is characterized in that a computer functions as the error acquisition means so as to obtain an average of the errors for each measurement and use it as an error.
In the state change detection program according to the present invention, the computer is used as the state change detection means, and the error amount obtained from each measurement data is based on a magnification of how many times as compared with the reference error obtained from the teacher data. It is characterized in that the state change is detected or the state change is detected based on the ratio of the deviation value σST of the standard error and the deviation values σt1, σt2, and σt3 of the error for each measurement .

In the state change detection program according to the present invention, the computer is used as the state change detection means to standardize the ratio of the standard error to the error, the magnification of the error with respect to the standard error, and the ratio or magnification. It is characterized in that it functions to detect state fluctuations based on the degree of dissociation, which is a value.

In the state change detection program according to the present invention, the computer is used as the state change detection means to have a dissociation degree threshold value, and when the obtained dissociation degree exceeds the dissociation degree threshold value, it is determined that there is a state change. It is characterized by making it work.

In the state change detection program according to the present invention, the computer is used as the state change detection means to have a dissociation degree abnormality threshold value to be determined as abnormal, and an abnormality occurs when the obtained dissociation degree exceeds the dissociation degree abnormality threshold value. It is characterized in that it functions so as to determine that it has occurred.

According to the present invention, it is possible to detect that there is a state change only from the data when there is no state change, or that there is no state change only from the data in which the state change occurs.

The block diagram which shows the structure of the computer system which realizes the state change detection apparatus which concerns on this invention. The figure which shows the means realized by the state change detection program provided in the computer system which realizes the state change detection device which concerns on this invention. The flowchart for demonstrating the operation of the state change detection apparatus which concerns on this invention. The figure which shows the teacher data used for the state change detection apparatus which concerns on this invention, the prediction data obtained by applying it to a prediction model, and the value of a standard error. The figure which shows the procedure until the prediction model is made using the teacher data in the state change detection apparatus which concerns on this invention. The figure which shows the procedure from inputting the teacher data to the prediction model in the state change detection apparatus which concerns on this invention, and obtaining the standard error. The figure which shows the measurement data of three time times used for the state change detection apparatus which concerns on this invention, the prediction data obtained by applying them to a prediction model, and the value of an error. The figure which shows the procedure from inputting the measurement data of three time time into a prediction model in the state change detection apparatus which concerns on this invention, and obtaining an error. The figure which conceptually showed the magnification of the error with respect to the standard error obtained from the measurement data of three time in the state fluctuation detection apparatus which concerns on this invention. The figure which showed the difference of the peak which is the degree of dissociation by using the distribution of a standard error and the distribution of an error in the state change detection apparatus which concerns on this invention by a graph. It is a figure explaining that the state change detection apparatus which concerns on this invention detects a state change based on the degree of dissociation and deviation value which are the values obtained by standardizing the error for each measurement.

Hereinafter, embodiments of the state change detection device and the state change detection program according to the present invention will be described with reference to the accompanying drawings. In each figure, the same components are designated by the same reference numerals, and duplicate description will be omitted. The state change detection device according to the embodiment of the present invention can be configured by, for example, a personal computer, a workstation, or other computer system as shown in FIG. This computer system operates as a state change detection device by controlling each part based on a program or data stored in the main memory 11 or read into the main memory 11 by the CPU 10 and executing necessary processing. is there.

An external storage interface 13, an input interface 14, a display interface 15, and a data input interface 16 are connected to the CPU 10 via a bus 12. A program such as a state change detection program and an external storage device 23 in which necessary data and the like are stored are connected to the external storage interface 13. An input device 24 such as a keyboard as an input device for inputting commands and data and a mouse 22 as a pointing device are connected to the input interface 14.

A display device 25 having a display screen such as an LED or an LCD is connected to the display interface 15. Sensors 26-1, 26-2, ..., 26-m for obtaining measurement data are connected to the data input interface 16. This computer system may be provided with other configurations, and the configuration of FIG. 1 is only an example.

In the above, in the CPU 10, each means and the like shown in FIG. 2 are realized by the state change detection program in the external storage device 23. That is, the prediction model creating means 31, the prediction model 30, the teacher prediction data acquisition means 32, the standard error acquisition means 33, the measurement prediction data acquisition means 34, the error acquisition means 35, and the state change detection means 36 are realized. Further, teacher data is stored in the external storage device 23. The prediction model creating means 31 creates the prediction model 30 using the teacher data. The prediction model 30 predicts the objective variable from the explanatory variables by machine learning. Here, as a machine learning algorithm, a random forest of pattern mining can be mentioned, but in addition to this, an algorithm such as regression analysis or a regression tree may be adopted.

Regarding the explanatory variable and objective variable prediction model, when y is the objective variable, xi is the explanatory variable, and f is the prediction model, y = f (x1, x2, ..., Xi, ..., Xn). Can be represented by. The teacher prediction data acquisition means 32 predicts the teacher explanatory variable by using the teacher data composed of the teacher objective variable which is the objective variable of the fixed value and the teacher explanatory variable which is the explanatory variable of the fixed value. It is input to the model 30 to obtain teacher prediction data. The standard error acquisition means 33 acquires a standard error which is a difference between the teacher prediction data and the teacher objective variable.

The measurement prediction data acquisition means 34 inputs the measurement explanatory variable into the prediction model 30 by using the measurement data composed of the measurement objective variable which is the measured objective variable and the measurement explanatory variable which is the measured explanatory variable. To obtain measurement prediction data. The measurement data can be the data obtained by the sensors 26-1, 26-2, ..., 26-m. The error acquisition means 35 acquires an error which is a difference between the measurement prediction data and the measurement objective variable.

The state change detecting means 36 obtains the degree of dissociation between the standard error and the error, and detects the state change based on the degree of dissociation.

Since the state change detection device configured by the above means and the like executes the processing operation according to the flowchart shown in FIG. 3, the operation will be described with reference to this flowchart. First, the prediction model 30 is created using the teacher data (STEP 1). For example, as shown in FIG. 4, the teacher data includes the objective variable data to be obtained by the sensor A, the first explanatory variable data obtained by the sensor B, and the second explanatory variable obtained by the sensor C. It is composed of variable data. The number of times data is acquired (the number of data in the vertical direction in FIG. 4) is arbitrary. As shown in FIG. 5, the prediction model creating means 31 creates the prediction model 30 using the teacher data T as shown in FIG.

Next, as shown in the flowchart of FIG. 3 and the operation sequence diagram of FIG. 6, explanatory variables (data of sensor B and data of sensor C in FIG. 4) are input to the prediction model 30 using the teacher data T, and prediction is performed. The data (teacher prediction data TF (teacher data prediction result in FIG. 6)) is obtained, and the error (standard error) which is the difference between the prediction data (teacher prediction data TF) and the objective variable of the teacher data T is obtained (standard error). STEP2). Here, the explanatory variables are the data of the sensor B and the sensor C in the teacher data of FIG. As shown in FIG. 6, if the objective variable of the teacher data T is TT and the objective variable TT is represented by, for example, a perfect circle, the teacher prediction data TF is distorted with respect to this objective variable TT. Therefore, the part that protrudes or retracts from the perfect circle becomes an error. Actually, as shown in the column of "predicted value standard / error of teacher data" in FIG. 4, the predicted data (teacher prediction data) and the error (standard error STER) are obtained as numerical values. Since these are obtained by calculating the same number as the number of times the data is acquired, the same quantity as the number of times the data is acquired can be obtained. Therefore, the average of these is calculated and obtained, and this is held as the average standard error (AVSTER).

Next, as shown in the flowchart of FIG. 3, the explanatory variables M1 and M2 are input to the prediction model 30 using the measurement data M by the sensors B and C to obtain the prediction data (measurement prediction data), and the prediction data is obtained. The error, which is the difference between (measurement prediction data) and the actually measured objective variable of the measurement data M, is obtained (STEP 3).

Here, the measurements t1, t2, and t3 are performed at different times to obtain the explanatory variables t1M1 and t1M2 of the measurement t1, the explanatory variables t2M1 and t2M2 of the measurement t2 are obtained, and the explanatory variables t3M1 and t3M1 of the measurement t3 are obtained. Obtain t3M2 (Fig. 7). As shown in FIG. 8, these are input to the same prediction model 30 to obtain prediction data (measurement prediction data) t1F, t2F, and t3F (right column in FIG. 7). In FIG. 8, it is drawn as if three prediction models 30 are provided, but one prediction model 30 may be used for sequential prediction. Of course, three prediction models 30 may be provided. As shown in FIGS. 7 and 8, the difference between the predicted data (measurement predicted data) t1F, t2F, t3F and the measured objective variables t1MT, t2MT, t3MT of the measured data M is an error (individual error) t1ER, t2ER, t3ER. Ask as. Since the errors t1ER, t2ER, and t3ER are obtained by calculating the same number as the number of times the data is measured, the same quantity as the number of times of acquisition can be obtained. Therefore, the average value of these is calculated and calculated for each measurement t1, t2, t3. It is held as an average error (t1AVER, t2AVER, t3AVER).

Next, as shown in STEP 4 of FIG. 3, it is calculated how many times the amount of error obtained from each measurement data is compared with the reference error obtained from the teacher data. We conclude that the larger this magnification is, the more the state changes. In FIG. 9, as the comparison main body, “measurement t1 error (average) (t1AVER)”, “measurement t2 error (average) (t2AVER)”, and “measurement t3 error (average) (t3AVER)” are shown. It is assumed that there is a value corresponding to the area of the circle, and there is a value corresponding to the area of the circle as shown in "Standard error (mean) (AVSTER)" as a comparison target. Assuming that the magnification is 1.2, 2.3, and 3.8, (measurement t1 error) <(measurement t2 error) <(measurement t3 error) holds, so measurement 3 has the most state fluctuation. It can be concluded that the state change is occurring, then the state change is occurring in the measurement 2, and the measurement 1 is the least probable that the state change is occurring.

Alternatively, instead of calculating the average of the standard error and the error, a distribution graph of the standard error value is created, a distribution graph of the error value is created, and the state variation is detected using these distribution graphs. The distribution graph of the standard error values is as shown in FIG. 10 (a), the distribution graph of the error values of the measurement t1 is as shown in FIG. 10 (b), and the distribution graph of the error values of the measurement t2 is as shown in FIG. It is assumed that the distribution graph of the error value of the measurement t3 is as shown in FIG. 10 (d).

In the above case, the difference between the peaks of the distribution graph is adopted as the degree of dissociation. The difference between the peak of the distribution graph of the standard error value and the peak of the distribution graph of the error value of the measurement t1 is as shown in d1 shown in FIG. 10 (b). The difference between the peak of the distribution graph of the standard error value and the peak of the distribution graph of the error value of the measurement t2 is as shown in d2 shown in FIG. 10 (c). The difference between the peak of the distribution graph of the standard error value and the peak of the distribution graph of the error value of the measurement t3 is as shown in d3 of FIG. 10 (d). Since the difference between the peaks is t1 <t2 <t3, the determination of the state change is as described with reference to FIG.

Further, as shown in STEP 4 of FIG. 3, a threshold value for abnormality determination is set for the magnification obtained above or for the value σ obtained by standardizing the error for each measurement number, so that the abnormality is detected. Is also good. For example, in the example of FIG. 9, if the threshold value is "3.5", it can be determined that the measurement 3 is abnormal.

An example of using the deviation value σ is shown in FIG. It is assumed that the above-mentioned error distribution is as shown in the graph of FIG. The average value of this error is obtained (S11). Using this average value, the deviation value σ is obtained (S12). Next, the state change is detected using the deviation value σ obtained above (S13). For example, since the standard error is also obtained for each measurement, the deviation value σ _{ST of the} standard error can be obtained from these distributions. On the other hand, the deviation values σ _t1 , σ _t2 , and σ _t3 are also obtained for the measurements t1, t2, and t3. Of the deviation values σ _t1 , σ _t2 , and σ _t3 , the larger the difference or ratio or magnification of the standard error with the deviation value σ _ST , the higher the probability of a state change. The threshold value σ _SH for the deviation values σ _t1 , σ _t2 , and σ _t3 can be set, and if it exceeds this, it can be considered that there is a state change (abnormality).

In the above, one objective variable is obtained from two explanatory variables, but it can be applied to a prediction model in which one objective variable is obtained from one or more explanatory variables. Further, the state change is not limited to the change to an abnormality, and can be applied to various state change detections such as an excess state, a shortage state, a low state, and a high state.

In the above embodiment, when the data is measured by the sensors A, B, and C, the objective variable is the data obtained by the sensor A, and the explanatory variable is the data obtained by the sensors B and C. Was made to configure only one prediction model. However, in such a system that measures data of three or more systems, a plurality of prediction systems can be constructed.

For example, when data is measured by sensors A, B, and C, sensor A predicts that the objective variable is the data measured by sensor A and the explanatory variable is the data measured by sensors B and C. The case where the model is called a model, the objective variable is the data measured by the sensor B, and the explanatory variables are the data measured by the sensors A and C is called the sensor B prediction model, and the objective variable is the data measured by the sensor C. When the explanatory variables are the data measured by the sensors A and B and are referred to as the sensor C prediction model, three prediction models such as the sensor A prediction model, the sensor B prediction model, and the sensor C prediction model are used. Can be created.

It is assumed that the time-series measured values obtained by measuring the data of the sensors A, B, and C are t1, t2, and t3. All of t1, t2, and t3 are composed of three types of data, sensors A, B, and C. In the sensor A prediction model, the measured values t1, t2, and t3 of the sensors B and C can be input to obtain three types of average errors of the sensor A prediction model, A_t1AVER, A_t2AVER, and A_t3AVER. In the sensor B prediction model, the measured values t1, t2, and t3 of the sensors A and C can be input to obtain three types of average errors of the sensor B prediction model, B_t1AVER, B_t2AVER, and B_t3AVER. In the sensor C prediction model, the measured values t1, t2, and t3 of the sensors A and B can be input to obtain three types of average errors of the sensor C prediction model, C_t1AVER, C_t2AVER, and C_t3AVER.

When the reference error of each model obtained from the teacher data is A_AVSTER, B_AVSTER, C_AVSTER, the magnification and error distribution (deviation) of the pair A_t1AVER, A_t2AVER, and A_t3AVER based on the reference error A_AVSTER can be obtained. It is possible to obtain the magnification and error distribution (deviation) of B_t1AVER, B_t2AVER, and B_t3AVER based on the reference error B_AVERSER, and the magnification and error of C_t1AVER, C_t2AVER, and C_t3AVER based on the reference error C_AVERSER, respectively. The distribution (deviation) can also be obtained.

When the deviation is obtained in the above, for example, the deviation value σA_t1 of the measured value t1 in the sensor A prediction model, the deviation value σB_t1 of the measured value t1 in the sensor B prediction model, and the deviation value t1 of the measured value t1 in the sensor C prediction model. By obtaining the average of σC_t1 and the like, a new feature amount of the measured value t1 can be generated, and by setting a threshold value for this, it is possible to detect an abnormality. The above is an example of the measured value t1, but the measured values t2 and t3 can be handled in the same manner.

10 CPU
11 Main memory 12 Bus 13 External storage interface 14 Input interface 15 Display interface 16 Data input interface 22 Mouse 23 External storage device 24 Input device 25 Display device 26 Sensor 30 Prediction model 31 Prediction model creation means 32 Teacher Prediction data acquisition means 33 Standard error Acquisition means 34 Measurement prediction data acquisition means 35 Error acquisition means 36 State fluctuation detection means

Claims (12)

Using a plurality of sensors composed of a sensor that measures the explanatory variable and a sensor that measures the objective variable, the measurement is measured at the same time each time, and the measurement explanatory variable that is the obtained explanatory variable and the obtained objective variable are used. A state fluctuation detection device that detects state fluctuations using a certain measurement objective variable.
A prediction model created using teacher data composed of a teacher objective variable, which is an objective variable with a fixed value, and a teacher explanatory variable, which is an explanatory variable with a fixed value, and predicts the objective variable from the explanatory variables by machine learning. When,
A teacher prediction data acquisition means for obtaining teacher prediction data by inputting the teacher explanatory variable of the teacher data into the prediction model,
A standard error acquisition means for acquiring a standard error which is a difference between the teacher prediction data and the teacher objective variable of the teacher data, and
The measurement objective variable, and the measurement using the measurement data composed explanatory variable and the measurement predicted data acquisition means for obtaining a measurement prediction data by inputting the measured explanatory variables to the predictive model,
An error acquisition means for acquiring an error which is a difference between the measurement prediction data and the measurement objective variable,
A state change detecting apparatus comprising: a state change detecting means for obtaining a dissociation degree between the standard error and the error and detecting a state change based on the dissociation degree. The state change detection device according to claim 1, further comprising a predictive model creating means for creating the predictive model using the teacher data. The teacher data is data including a plurality of sets of data composed of a teacher objective variable which is an objective variable of a fixed value and a teacher explanatory variable which is an explanatory variable of a fixed value.
The measurement data is a set of a set of data composed of a measurement objective variable which is a measured objective variable and a measurement explanatory variable which is a measured explanatory variable obtained for each measurement time, and a plurality of sets are set according to the measurement time. This is the data
The standard error acquisition means obtains the average of the standard errors of each set of the plurality of sets of the teacher data and uses it as the standard error, and the error acquisition means obtains the average of the errors for each measurement and uses it as the error. The state change detection device according to claim 1 or 2. The state fluctuation detecting means detects the state change based on the magnification of how many times the amount of error obtained from each measurement data is compared with the reference error obtained from the teacher data, or the deviation value of the standard error. The state change detection device according to claim 3, wherein the state change is detected based on the ratio of σST and the deviation values σt1, σt2, and σt3 of the error for each measurement . The present invention according to any one of claims 1 to 4, wherein the state change detecting means has a dissociation degree threshold value, and determines that there is a state change when the obtained dissociation degree exceeds the dissociation degree threshold value. The described state change detector. The state fluctuation detecting means has a dissociation degree abnormality threshold value to be determined as abnormal, and claims 1 to 5 are characterized in that when the obtained dissociation degree exceeds the dissociation degree abnormality threshold value, it is determined that an abnormality has occurred. The state change detection device according to any one of the above items. Using a plurality of sensors composed of a sensor that measures the explanatory variable and a sensor that measures the objective variable, the measurement is measured at the same time each time, and the measurement explanatory variable that is the obtained explanatory variable and the obtained objective variable are used. A computer of a state change detector that detects state changes using a certain measurement objective variable ,
A prediction model created using teacher data composed of a teacher objective variable, which is an objective variable with a fixed value, and a teacher explanatory variable, which is an explanatory variable with a fixed value, and predicts the objective variable from the explanatory variables by machine learning. ,
A teacher prediction data acquisition means for obtaining teacher prediction data by inputting the teacher explanatory variable of the teacher data into the prediction model.
A standard error acquisition means for acquiring a standard error which is a difference between the teacher prediction data and the teacher objective variable of the teacher data .
A measurement prediction data acquisition means for obtaining measurement prediction data by inputting the measurement explanatory variable into the prediction model using the measurement data composed of the measurement objective variable and the measurement explanatory variable.
An error acquisition means for acquiring an error which is a difference between the measurement prediction data and the measurement objective variable,
A program for detecting state fluctuations, which obtains the degree of dissociation between the standard error and the error and functions as a state fluctuation detecting means for detecting state fluctuations based on the degree of dissociation. The computer,
The state change detection program according to claim 7, wherein the program functions as a predictive model creating means for creating the predictive model using the teacher data. The teacher data is data including a plurality of sets of data composed of a teacher objective variable which is an objective variable of a fixed value and a teacher explanatory variable which is an explanatory variable of a fixed value.
The measurement data is a set of a set of data composed of a measurement objective variable which is a measured objective variable and a measurement explanatory variable which is a measured explanatory variable obtained for each measurement time, and a plurality of sets are set according to the measurement time. This is the data
The computer is used as the standard error acquisition means to obtain the average of the standard errors of each set of the plurality of sets of teacher data to be the standard error, and the computer is used as the error acquisition means for each measurement. The state fluctuation detection program according to claim 7 or 8, wherein the average of the errors is calculated and made to function as an error. Using the computer as the state fluctuation detecting means, the state change is detected based on the magnification of how many times the error amount obtained from each measurement data is compared with the reference error obtained from the teacher data, or the standard error. The state change detection program according to claim 9, wherein the state change detection program is made to function to detect a state change based on the ratio of the deviation value σST of the above and the deviation values σt1, σt2, and σt3 of the error for each measurement . 7. To claim 7, wherein the computer is used as the state change detecting means to have a dissociation degree threshold value and to function so as to determine that there is a state change when the obtained dissociation degree exceeds the dissociation degree threshold value. The program for detecting state fluctuations according to any one of 10. The computer is characterized by having a dissociation degree abnormality threshold value to be determined as abnormal and functioning as the state fluctuation detecting means so as to determine that an abnormality has occurred when the obtained dissociation degree exceeds the dissociation degree abnormality threshold value. The program for detecting state fluctuations according to any one of claims 7 to 11.

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