APPARATUS, METHOD, AND COMPUTER READABLE MEDIUM

Provided is an apparatus including: an acquisition unit which acquires measurement data indicating a state of a target; a supply unit which supplies the measurement data acquired by the acquisition unit to a plurality of classification models respectively learned by learning data, which includes measurement data in a period in which the state of the target is normal, for periods different from each other, the plurality of classification models classifying measurement data as either normal or abnormal in response to the measurement data being input; and a determination unit which determines the state of the target as either normal or abnormal based on a plurality of classification results output from the plurality of classification models.

The contents of the following patent application(s) are incorporated herein by reference: NO. 2023-106925 filed in JP on Jun. 29, 2023

BACKGROUND

1. Technical Field

The present invention relates to an apparatus, a method, and a computer readable medium.

2. Related Art

Patent Document 1 describes “ . . . the presence or absence of abnormality of the equipment 10 may be detected by using a threshold value” (paragraph 0055), for example.

Prior Art Document

Patent Document

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the present invention will be described. However, the following embodiments are not for limiting the invention according to the claims. In addition, not all of the combinations of features described in the embodiments are imperative to the solving means of the invention.

FIG.1shows a system1according to the present embodiment. The system1includes equipment2and an apparatus3.

The equipment2may be an example of a target, and the state thereof may be monitored by the apparatus3. The equipment2may be an installation, an apparatus, or the like equipped with one or more instruments (not shown). For example, the equipment2may be a plant or may be a complex apparatus in which a plurality of instruments are combined. Examples of the plant include: in addition to an industrial plant such as a chemical plant and a biotechnology plant, a plant for managing and controlling a well site such as a gas field or an oil field and its surrounding area; a plant for managing and controlling power generation such as hydroelectric, thermal, or nuclear power generation; a plant for managing and controlling energy harvesting from solar power, wind power, or the like; a plant for managing and controlling water and sewerage, dams, or the like; and others. The instrument equipped in the equipment2is a tool, machinery, or an apparatus, and may be a so-called field instrument. For example, the instrument may be a sensor instrument such as a pressure gauge, a flow meter, or a temperature sensor, a valve instrument such as a flow control valve or an on/off valve, or an actuator instrument such as a fan or a motor.

The equipment2may be provided with one or more sensors20. Each sensor20may measure a physical quantity indicating the state of the equipment2. The physical quantity to be measured may be, for example, a pressure, a temperature, a pH, a speed, a flow rate, or the like, or may be a yield of a product by the equipment2, a proportion of impurities mixed in the product, or the like. Each sensor20may be of different types from each other, or at least some (two or more) of the sensors20may be of the same type. Each sensor20may supply a measurement value of a physical quantity or the like to the apparatus3. Communication between the sensor20and the apparatus3may be performed by, for example, a wireless communication protocol of International Society of Automation (ISA), and may be performed by ISA100, Highway Addressable Remote Transducer (HART) (registered trademark), BRAIN (registered trademark), FOUNDATION Fieldbus, PROFIBUS, or the like as an example.

The apparatus3includes an acquisition unit31, a storage unit32, a supply unit33, a determination unit34, a decision unit35, an output unit36, a setting unit37, a learning processing unit38, and a selection unit39.

The acquisition unit31acquires measurement data indicating the state of the equipment2. The acquisition unit31may acquire measurement data including one type of measurement value measured by a single sensor20, or may acquire measurement data including a plurality of types of measurement values measured at the same (or substantially the same) timing by a plurality of sensors20. A timestamp indicating a measurement timing by the sensor20or an acquisition timing by the acquisition unit31may be added to the measurement data. The acquisition unit31may supply the acquired measurement data to the storage unit32and cause the measurement data to be stored in a measurement data file321described later. The acquisition unit31may supply the acquired measurement data to the supply unit33.

The storage unit32stores various types of information. For example, the storage unit32may store the measurement data file321and a plurality of classification models322.

The measurement data file321stores the measurement data supplied from the acquisition unit31. Note that in each piece of measurement data in the measurement data file321, the timestamp of the measurement data may indicate which period among a plurality of periods different for each reference time width (one week as an example in the present embodiment) the measurement data belongs to. For example, a timestamp of Jan. 1, 2023 may indicate that the measurement data belongs to the first week period of 2023, and a timestamp of Jan. 8, 2023 may indicate that the measurement data belongs to the second week period of 2023.

Each classification model322classifies the measurement data as either normal or abnormal in response to the input of the measurement data. Each classification model322may output a classification result binarized with a value indicating normality and a value indicating abnormality. Each classification model322may output the classification result to the determination unit34.

Each classification model322may have been learned by learning data for each of periods different from each other, the learning data including measurement data in a period in which the state of the equipment2is normal. The measurement data in the period in which the state of the equipment2is normal may be measurement data including a measurement value measured in the period in which the state of the equipment2is normal. The period in which the state of the equipment2is normal may be a period in which it is determined by at least one of an operator or the determination unit34described later that the state of the equipment2is normal. The learning data for each of periods may include measurement data measured at two or more timings (as an example, measurement data in chronological order). In the present embodiment, as an example, the learning data for each of periods may include measurement data within periods of the same time width (also referred to as a reference time width). As an example, the reference time width may be one week. The periods different from each other may be periods in which at least one of the start or the end is different, and in the present embodiment, as an example, may be periods not including a common time point.

In response to to-be-classified measurement data satisfying a condition derived from the feature of the measurement data included in the learning data, each classification model322may classify the to-be-classified measurement data as normal, and in response to to-be-classified measurement data not satisfying the condition, it may classify the to-be-classified measurement data as abnormal. The feature of the measurement data included in the learning data may be a value indicating the distribution of each type of measurement value, and may be, as an example, at least one of a maximum value, a minimum value, an average value, a median value, a mode value, a standard deviation, or a variance, or may be a value indicating a waveform of a temporal measurement value. The condition derived from the feature of the measurement data may be, as an example, that the measurement value of the to-be-classified measurement data falls within a range of the minimum value to the maximum value, or may be that the to-be-classified measurement value falls within a range of (average value−3σ) to (average value+3σ) (where σ represents a standard deviation).

Each classification model322may be learned by unsupervised learning using only learning data including measurement data classified in advance as normal measurement data, and may be generated by the same learning algorithm. In the present embodiment, as an example, each classification model322may be generated by a learning algorithm of one-class support vector machine (SVM). Each classification model322may be learned by using separate learning data.

At least one of the classification models322stored in the storage unit32may be generated by learning processing by the learning processing unit38described later. At least one of the classification models322stored in the storage unit32may be a classification model322(also referred to as a to-be-used classification model322) used for determination by the determination unit34. Although details will be described later, the to-be-used classification model322may be selected by the selection unit39.

The supply unit33supplies, to one or more classification models322, the measurement data acquired by the acquisition unit31. The supply unit33may supply the measurement data to each to-be-used classification model322. Accordingly, the classification result may be output to the determination unit34from each to-be-used classification model322to which the measurement data has been supplied.

The determination unit34determines the state of the equipment2as either normal or abnormal based on a plurality of classification results output from a plurality of classification models322. The determination unit34may make a determination by acquiring the classification result from each to-be-used classification model322.

The determination unit34may determine the state of the equipment2as either normal

or abnormal by taking a logical product of the plurality of classification results. In the present embodiment, as an example, the determination unit34may take a logical product of a plurality of classification results by setting, as true, a classification result indicating that the measurement data is abnormal and, as false, a classification result indicating that the measurement data is normal, and in other words, the determination34unit may consider the state of the equipment2as abnormal in response to the measurement data being classified as abnormal in all the classification results and consider the state of the equipment2as normal in response to the measurement data being classified as normal in at least one classification result. The determination unit34may supply the determination result to the decision unit35.

The decision unit35decides the severity of the abnormal state of the equipment2based on the number of times of consecutive determination that the state of the equipment2is abnormal (also referred to as the number of times of consecutive abnormality determination). The consecutive determination as abnormality may mean that the determination result indicating abnormality continues without interposing the determination result indicating normality.

The decision unit35may decide the severity to be higher as the number of times of consecutive abnormality determination is larger. For example, when the number of times of consecutive abnormality determination is “x”, a first reference number of times is “a” (where a is a natural number of 2 or more), and a second reference number of times is “b” (where b is a natural number of a<b), the decision unit35may decide the severity as a low level when x<a, decide the severity as a medium level (also referred to as a caution level) when a≤×<b, and decide the severity as a high level (also referred to as a warning level) when b≤x. As an example, the first reference number of times a may be 5, and the second reference number of times b may be 10.

The decision unit35may decide the severity each time the determination result is supplied from the determination unit34. The decision unit35may supply, to the output unit36and the setting unit37, the decided severity together with the determination result by the determination unit34.

The output unit36outputs the determination result by the determination unit34and the decision result by the decision unit35. The output unit36may cause a display apparatus (not shown) to display the determination and decision results.

The setting unit37sets, as measurement data to be included in new learning data (also referred to as measurement data for learning), measurement data in a period in which the severity decided by the decision unit35is lower than a reference severity, among the measurement data in each period acquired by the acquisition unit31and supplied from the supply unit33to the to-be-used classification model322. The measurement data in the period in which the severity decided by the decision unit35is lower than the reference severity among the measurement data in each period supplied to the to-be-used classification model322may be measurement data in a period in which the severity decided by the decision unit35is maintained lower than the reference severity among the measurement data for each period. The description that the severity is lower than the reference severity may mean that the equipment2is normal, or may mean that the equipment2is abnormal but the severity of the abnormality is less than the reference severity. In the present embodiment, as an example, the reference severity may be at the high level, that is, the warning level.

Note that, in the period in which the severity is lower than the reference severity, even when the state of the equipment2is determined as abnormal, the abnormal state occurs only temporarily and sporadically, and thus, it is considered that the measurement data is classified as abnormal due to noise, drift, or the like of the measurement value although the equipment2is originally normal. The setting unit37may set, as the measurement data for learning, the measurement data in the period in which the equipment2is originally normal as described above.

The setting unit37may add a label indicating the measurement data for learning (also referred to as a learning label) to the measurement data in each period in which the severity is lower than the reference severity among the measurement data in each period in the measurement data file321. In the present embodiment, as an example, the setting unit37may determine whether the decided severity is below the high level, each time the decision result of the severity is supplied from the decision unit35, and add the learning label to measurement data in a period in which the severity is maintained below the high level. In response to adding the learning label to the measurement data, the setting unit37may supply a notification to that fact to the learning processing unit38.

In each period in which the state of the equipment2is normal, the learning processing unit38generates a new classification model322to be included in the plurality of classification models322stored in the storage unit32by learning processing using the learning data including the measurement data in the period acquired by the acquisition unit31. The description of generating the new classification model322in each period in which the state of the equipment2is normal may mean generating the new classification model322for each period in which the state of the equipment2is normal.

In response to receiving, from the setting unit37, the notification that the learning label is added to the measurement data, the learning processing unit38may generate the new classification model322by using the learning data including the measurement data in the most recent period among the measurement data to which the learning label is added in the measurement data file321. Therefore, when the learning labels are added to measurement data of a plurality of consecutive periods, the new classification model322may be generated each time each period elapses.

The learning processing unit38may generate the classification model322by unsupervised learning using only learning data including measurement data classified in advance by adding the learning label, and in the present embodiment, as an example, the classification model322may be generated by the learning algorithm of one-class SVM. In response to generating the classification model322, the learning processing unit38may supply, to the selection unit39, a notification indicating the fact.

The selection unit39selects the to-be-used classification model322from the classification models322stored in the storage unit32. When two or more classification models322are stored in the storage unit32, the selection unit39may select a plurality of to-be-used classification models322, from the two or more classification models322. When a reference number (as an example, 4) or more classification models322are stored in the storage unit32, the selection unit39may select, as the to-be-used classification model322, the reference number of classification models322.

The selection unit39may select, as at least one of the to-be-used classification models322, at least one classification model322learned by learning data including measurement data in the most recent period. The selection unit39may select each of the to-be-used classification models322in response to receiving, from the learning processing unit38, a notification indicating that the classification model322is newly generated. The selection unit39may supply the measurement data from the supply unit33to the to-be-used classification model322by supplying, to the supply unit33, the identification information of the to-be-used classification model322.

According to the apparatus3described above, the state of the equipment2is determined as normal or abnormal, based on a plurality of classification results of normality or abnormality for the measurement data, the classification results being output when the measurement data is supplied to the classification model322. Therefore, unlike a case where an index value indicating a degree of normality or abnormality with respect to the measurement data is acquired, and determination is performed by comparing the index value with a threshold value, it is possible to eliminate the trouble of an operator setting the threshold value by trial and error and to prevent the determination result from being different depending on the magnitude of the threshold value. In addition, since a plurality of classification models322are used which are learned by the learning data, which includes the measurement data in the period in which the state of the equipment2is normal, for each of periods different from each other, it is possible to acquire a highly accurate classification result as compared with a case where a single classification model is used which is learned by the learning data including respective pieces of measurement data in a plurality of periods in which the state of the equipment2is normal. That is, even when the state of the equipment2is normal, the tendency of the measurement value may change, for example, in a case where the measurement value drifts, in a case where the state of the equipment2changes, in a case where the operation method of the equipment2changes, or the like. In addition, even when the state of the equipment2is normal, the measurement value may include various noises. As described above, in a case where the tendency of the measurement value changes or in a case where the measurement value includes various noises, when the single classification model is used which is learned by the learning data including respective pieces of measurement data in the plurality of periods, the to-be-classified measurement data can be classified as normal measurement data when the to-be-classified measurement data is included as a whole in a normal range in the plurality of periods even in a case where the to-be-classified measurement data deviates from a normal range in an individual period. On the other hand, when the plurality of classification models322are used which are learned by the learning data for each of periods different from each other, the measurement data can be correctly classified as abnormal measurement data by each classification model322when the to-be-classified measurement data deviates from the normal range in each period. Then, by performing determination by using a plurality of such classification results, it is possible to acquire a highly accurate determination result.

In addition, since the logical product of the plurality of classification results by the plurality of classification models322is taken and the state of the equipment2is determined as normal or abnormal, it is possible to acquire a highly accurate determination result as compared with a case where the determination is performed with a logical sum.

In addition, in each period in which the state of the equipment2is normal, a new classification model322is generated by the learning processing using the learning data including the measurement data in the period. Therefore, since the classification model322according to the change in the tendency of the measurement data can be sequentially generated and used for determination, a highly accurate determination result can be acquired.

In addition, a plurality of to-be-used classification models322are selected from two or more classification models322stored in the storage unit32. Therefore, an appropriate classification model322can be appropriately selected and used for determination.

In addition, since one classification model322learned by the learning data including the measurement data in the most recent period is selected as at least one of the to-be-used classification models322, the classification model322according to the most recent state of the equipment2can be used for determination.

In addition, since the severity of the abnormal state of the equipment2is decided based on the number of times of consecutive determination that the state of the equipment2is abnormal, the severity of the state abnormality can be acquired.

In addition, among the measurement data in each period supplied to the plurality of classification models322, measurement data in a period in which the severity is lower than the reference severity is set as measurement data to be included in new learning data. Therefore, since the measurement data in the period in which the equipment2is originally normal can be set for learning, the classification model322is generated by using new learning data including such measurement data, thereby acquiring the classification model322which can correctly classify the originally normal measurement data as normal.

<2. Operation of Apparatus3>

FIG.2shows the operation of the apparatus3. The apparatus3supports the monitoring of the equipment2by performing processing of steps S1to S55. Note that, in the present embodiment, as an example, description will be given on the assumption that the classification model322is not stored in the storage unit32at the starting time point of the present operation.

In step S1, the acquisition unit31acquires measurement data in a period in which the state of the equipment2is normal (as an example, a period of the reference time width). In the present step, the period in which the state of the equipment2is normal may be a period in which the state of the equipment2is determined as normal by the operator, and as an example, when it is determined by the operator that the state of the equipment2is normal over the period of the reference time width, the period may be the relevant period, and when it is determined by the operator that the state of the equipment2is normal over a period longer than the reference time width, the period may be the period of the reference time width included in the relevant period. The acquired measurement data may include a measurement value of a period in which the state of the equipment2is determined as normal by the operator. The acquired measurement data may be for learning the classification model322, and a learning label may be added in advance. The acquisition unit31may acquire, as measurement data, measurement values from one or more sensors20. The acquisition unit31may perform so-called preprocessing on the acquired measurement data. As an example, the acquisition unit31may calculate a moving average of measurement values from a plurality of pieces of acquired most recent measurement data and use the moving average as the measurement value of the acquired measurement data, or may remove an outlier detected by statistical analysis using past measurement data among the measurement values of the acquired measurement data.

In step S3, the learning processing unit38generates a new classification model322by learning processing using learning data including the measurement data acquired in step S1. The learning processing unit38may store the generated new classification model322in the storage unit32.

In step S5, the selection unit39selects, as the to-be-used classification model322, the classification model322generated in step S3. Accordingly, the measurement data may be supplied to the selected classification model322in step S15described later.

In step S11, the supply unit33starts clocking for the reference time width. Accordingly, a new period may be started.

In step S13, the acquisition unit31acquires measurement data indicating the state of the equipment2. The acquisition unit31may acquire, as the measurement data, measurement values from one or more sensors20. The acquisition unit31may perform preprocessing on the acquired measurement data in a similar manner to that in step S1described above.

In step S15, the supply unit33supplies the measurement data acquired in step S13to at least one classification model322. Accordingly, a classification result obtained by classifying the measurement data as normal or abnormal may be output from each classification model322to which the measurement data is supplied.

The supply unit33may supply the measurement data to each to-be-used classification model322selected by the selection unit39. As an example, when only a single classification model322is selected as a use target in step S5described above, the supply unit33may supply the measurement data to the single classification model322. When a plurality of classification models322is selected as the use target in step S55described later, the supply unit33may supply the measurement data to each of the selected classification models322. When step S15is performed for the second time or later, the processing of step S15may be executed at reference intervals (as an example, 30 minutes).

In step S17, the determination unit34acquires the classification result of the measurement data from each to-be-used classification model322.

In step S19, the determination unit34determines the state of the equipment2as either normal or abnormal based on each classification result acquired in step S17.

When only a single classification result is acquired from the single classification model322in step S17described above, the determination unit34may determine the state of the equipment2as normal or abnormal based on the single classification result. As an example, the determination unit34may determine that the state of the equipment2is normal, based on the classification result that the measurement data is normal, and may determine that the state of the equipment2is abnormal, based on the classification result that the measurement data is abnormal.

When a plurality of classification results are acquired from the plurality of classification models322in step S17described above, the determination unit34may determine the state of the equipment2as either normal or abnormal based on the plurality of classification results. The determination unit34may determine the state of the equipment2as either normal or abnormal by taking the logical product of the plurality of classification results. If it is determined in step S19that the equipment2is normal (step S19; normal), the processing may proceed to step S41. If it is determined in step S19that the equipment2is abnormal (step S19; abnormal), the processing may proceed to step S21.

In step S21, the decision unit35increments a number of times of consecutive abnormality determination x. When step S21is performed first, the number of times of consecutive abnormality determination may be incremented from an initial value of 0 to 1.

In step S23, the decision unit35determines whether the number of times of consecutive abnormality determination x is less than the first reference number of times a, that is, x<a. If it is determined that x<a (step S23; Yes), the processing may proceed to step S25. If it is determined that x<a is not satisfied, that is, a≤x is satisfied (step S23; No), the processing may proceed to step S27.

In step S25, the decision unit35decides the severity of the abnormal state of the equipment2as the low level. When the processing of step S25is completed, the processing may proceed to step S45described later.

In step S27, the decision unit35determines whether the number of times of consecutive abnormality determination x is equal to or larger than the first reference number of times a and less than the second reference number of times b, that is, a≤×<b. If it is determined that a≤×<b (step S27; Yes), the processing may proceed to step S29. If it is determined that a≤×<b is not satisfied, that is, b≤x is satisfied (step S27; No), the processing may proceed to step S31.

In step S29, the decision unit35decides the severity of the abnormal state of the equipment2as the medium level, that is, the caution level. When the processing of step S29is completed, the processing may proceed to step S45described later.

In step S31, the decision unit35decides the severity of the abnormal state of the equipment2as the high level, that is, the warning level. When the processing of step S31is completed, the processing may proceed to step S45described later.

In step S41, the decision unit35resets the number of times of consecutive abnormality determination x to 0. In step S43, the decision unit35decides the severity of the abnormal state of the equipment2to be 0.

In step S45, the output unit36outputs the determination result by the determination unit34in step S19and the decision result by the decision unit35in step S25, S29, S31, or S43. The output unit36may cause a display apparatus (not shown) to display the determination and decision results.

In step S47, the supply unit33determines whether the clocking for the reference time width has been completed, that is, whether a period of the reference time width (one week as an example in the present embodiment) has elapsed from step S11described above. If it is determined that the period of the reference time width has elapsed (step S47; Yes), the processing may proceed to step S49. If it is determined that the period of the reference time width has not elapsed (step S47; No), the processing may proceed to step S13.

In step S49, the setting unit37determines whether in the most recent period, the severity in the period is maintained below the reference severity (the high level as an example in the present embodiment). If it is determined that the severity is not maintained below the high level (step S49; No), the processing may proceed to step S11described above. If it is determined that the severity is maintained below the high level (step S49; Yes), the processing may proceed to step S51.

In step S51, the setting unit37sets the measurement data in the most recent period as the measurement data to be included in the new learning data. In the present embodiment, as an example, the setting unit37may set, as the measurement data to be included in the new learning data, the measurement data in the period in which the determination that the state of the equipment2is normal is maintained in step S19. In addition to this, when the severity is decided as the low level, the medium level, or 0 in steps S25, S29, and S43, the setting unit37may set, as the measurement data to be included in the new learning data, the measurement data in the period in which the severity is maintained below the high level. The setting unit37may add a learning label to the measurement data in the most recent period stored in the measurement data file321.

In step S53, the learning processing unit38generates a new classification model322by learning processing using learning data including the measurement data in the most recent period to which the learning label is added. The learning processing unit38may additionally store the generated new classification model322in the storage unit32.

In step S55, the selection unit39selects the to-be-used classification model322from the plurality of classification models322stored in the storage unit32. Accordingly, the measurement data may be supplied to each selected to-be-used classification model322in step S15described above.

When only a number, which is equal to or less than the reference number, of

classification models322are stored in the storage unit32, the selection unit39may select each classification model322as the to-be-used classification model322. When a number, which is larger than the reference number, of classification models322are stored in the storage unit32, the selection unit39may select, as the to-be-used classification models322, the reference number of the classification models322from these classification models322. For example, the selection unit39may select, as the to-be-used classification model322, at least one latest classification model322generated in step S53among the classification models322in the storage unit32. Accordingly, the measurement data may be classified by all the classification models322until the number of classification models322in the storage unit32reaches the reference number, and after the number of classification models322in the storage unit32exceeds the reference number, the measurement data may be classified by the selected reference number of classification models322. The selection unit39may sequentially delete the previously generated classification model322according to the capacity of the storage unit32. When the processing of step S55is completed, the processing may proceed to step S11described above.

FIG.3shows a relationship among the determination result, the number of times of consecutive abnormality determination x, and the severity of the abnormality. Note that in the present drawing, a horizontal axis represents time, and a vertical broken line represents a period break.

As shown in the present drawing, the number of times of consecutive abnormality determination x is incremented in response to continuation of the determination result indicating that the state of the equipment2is abnormal, and is reset according to the determination result indicating that the state of the equipment2is normal. In addition, the severity is 0 when the number of times of consecutive abnormality determination x is 0, the severity is at the low level when the number of times of consecutive abnormality determination x is 1 to 4, the severity is at the medium level (that is, the caution level) when the number of times of consecutive abnormality determination x is 5 to 9, and the severity is at the high level (that is, the warning level) when the number of times of consecutive abnormality determination x is 10 or more.

Note that, in the above-described embodiment, the description has been given assuming that the selection unit39selects, as at least one of the to-be-used classification models322, at least one classification model322learned by the learning data including the measurement data in the most recent period, but, in addition to or instead of this, at least one classification model322designated by the operator among two or more classification models322stored in the storage unit32may be selected as at least one of the to-be-used classification models322. Accordingly, an arbitrary classification model322can be selected by the operator and used for determination. Therefore, when the equipment2is currently normal, the classification model322in which the current measurement data is determined as normal can be selected and used for determination. In addition, a classification model exhibiting high classification accuracy in a test using normal measurement data and abnormal measurement data can be selected in advance and used for determination. The selection unit39may fix the classification model322as the to-be-used classification model322in response to the classification model322being designated by the operator, and may automatically select the classification model322in the processing of step S55.

In addition, the description has been given assuming that the determination unit34determines the state of the equipment2as either normal or abnormal by taking the logical product of the plurality of classification results from the plurality of to-be-used classification models322, but a majority decision of the plurality of classification results may be made to determine the state of the equipment2as either normal or abnormal. In addition to this, the determination unit34may determine the state of the equipment2as either normal or abnormal by adding a larger weight to the classification result of the classification model322learned by learning data including measurement data in a more recent period among the plurality of to-be-used classification models322, and making a weighted majority decision of the plurality of classification results. Adding a weight to the classification result and making a weighted majority decision may be increasing the number of each classification result by the weight and making a majority decision. As an example, a classification result by the classification model322learned by learning data in the most recent period is set as a first classification result, a classification result by the classification model322learned by learning data in the second most recent period is set as a second classification result, and a classification result by the classification model322learned by learning data in the third most recent period is set as a third classification result. In addition, weights for the first to third classification results are defined as w1 to w3 (where w1>w2>w3), respectively. In this case, the determination unit34may make a majority decision with the first classification result considered as the classification results of 1×w1 counts, the second classification result considered as the classification results of 1×w2counts, and the third classification result considered as the classification results of 1×w3 counts. Accordingly, a newer classification model322, that is, the classification model322according to the state of the equipment2closer to the latest has a larger influence on the determination result, so that the determination result according to the transition of the state of the equipment2can be acquired.

In addition, the description has been given assuming that the determination unit34performs determination based on the plurality of classification results output from the plurality of classification models322generated by a common learning algorithm, but the determination may be performed based on a classification result output from a classification model generated by another known learning algorithm in addition to the plurality of classification results output from the plurality of classification models322.

In addition, the description has been given assuming that the learning processing unit38generates the classification model322in response to receiving, from the setting unit37, the notification that the learning label is added to the measurement data, but the classification model322may be generated according to a predetermined schedule, or the classification model322may be generated according to an instruction of the operator. The learning processing unit38may generate the classification model322by using learning data unused for the learning processing among the learning data for each period in which the state of the equipment2is normal.

In addition, the description has been given assuming that the apparatus3includes the storage unit32, the decision unit35, the output unit36, the setting unit37, the learning processing unit38, and the selection unit39, but the apparatus3may not include any of these. When the apparatus3does not include the storage unit32, the measurement data file321and the classification model322may be stored in a storage apparatus externally connected to the apparatus3. When the apparatus3does not include the decision unit35, the output unit36may output the determination result by the determination unit34without outputting the decision result by the decision unit35, and the setting unit37may set, as the measurement data to be included in the new learning data, measurement data in a period in which the equipment2is determined as normal by the determination unit34. When the apparatus3does not include the output unit36, the determination unit34and the decision unit35may store the determination or decision result in association with the corresponding measurement data in the measurement data file321. When the apparatus3does not include the setting unit37, the learning processing unit38may generate the classification model322by using measurement data within the period selected by the operator among the measurement data for each period. When the apparatus3does not include the learning processing unit38, the determination unit34may perform determination by using only the classification model322stored in the storage unit32by default. When the apparatus3does not include the selection unit39, the determination unit34may perform determination by using each classification model322in the storage unit32.

In addition, the description has been given assuming that each piece of learning data includes the measurement data within the periods of the same reference time width, but the learning data may include measurement data within periods of time widths different from each other. In this case, the operator may arbitrarily set the start and the end of each period. In addition, in the processing of step S1described above, the acquisition unit31may acquire measurement data in a period of an arbitrary time width in which the state of the equipment2is normal, and as an example, may acquire measurement data in a period of an arbitrary time width in which the state of the equipment2is determined as normal by the operator.

In addition, the description has been given assuming that the setting unit37sets, as the measurement data for learning, the measurement data in the period in which the severity decided by the decision unit35is maintained lower than the reference severity (as an example, the high level), that is, excludes the period in which the severity is equal to or higher than the reference severity and sets, as the measurement data for learning, the measurement data in the remaining period, but the setting unit37may set the measurement data for learning by another mode. For example, when the number of times of consecutive abnormality determination is equal to or larger than the above-described second reference number of times “b” (that is, the reference number of times for deciding the severity as the high level), the setting unit37may exclude the measurement data, which has caused the abnormality determination for the “b”-th or later, among the measurement data acquired by the acquisition unit31and supplied from the supply unit33to the plurality of classification models322and set the remaining measurement data as the measurement data for learning, or may set, as the measurement data for learning, measurement data obtained by excluding each piece of measurement data which has caused consecutive abnormality determination until the severity decided by the decision unit35becomes at the high level (that is, each piece of measurement data which has caused the first abnormality determination to the “b”-th abnormality determination), and each piece of measurement data which is subsequent to the measurement data and has caused consecutive abnormality determination (that is, each piece of measurement data which has caused consecutive abnormality determination for the “b+1”-th time or later) among the measurement data acquired by the acquisition unit31and supplied from the supply unit33to the plurality of classification models322. In these cases, the measurement data for learning may include measurement data within periods of time widths different from each other.

In addition, the target to be monitored by the apparatus3has been described as the equipment2, but the target may be an instrument (the actuator instrument as an example) provided in the equipment2.

In addition, the description has been given assuming that the classification model322is not stored in the storage unit32at the starting time point of the operation ofFIG.2, but one or more classification models322may be stored in advance. In this case, the operation may start from step S11in a state where each classification model322in the storage unit32is selected as the to-be-used classification model322in advance by the selection unit39. In addition, in step S55, the selection unit39may select the classification model322generated by the learning processing unit38, or may select the classification model322stored in advance in the storage unit32.

In addition, in the operation ofFIG.2, the description has been given assuming that the new classification model322generated by the learning processing unit38is stored in the storage unit32, but a new classification model322generated by the learning processing similar to that of the learning processing unit38in an external apparatus may be stored in the storage unit32. In this case, the selection unit39may select any classification model322as the to-be-used classification model322in response to the classification model322being stored in the storage unit32.

In addition, various embodiments of the present invention may be described with reference to flowcharts and block diagrams whose blocks may represent (1) steps of processes in which operations are executed or (2) sections of apparatuses responsible for executing operations. Certain steps and sections may be implemented by dedicated circuitry, programmable circuitry supplied with computer-readable instructions stored on computer-readable media, and/or processors supplied with computer-readable instructions stored on computer-readable media.

Dedicated circuitry may include digital and/or analog hardware circuits, and may include integrated circuits (IC) and/or discrete circuits. The programmable circuitry may include a reconfigurable hardware circuit including logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, a memory element such as a flip-flop, a register, a field programmable gate array (FPGA) and a programmable logic array (PLA), and the like.

A computer readable medium may include any tangible device that can store instructions to be executed by a suitable device, and as a result, the computer readable medium having instructions stored thereon includes an article of manufacture including instructions which can be executed in order to create means for performing operations specified in the flowcharts or block diagrams. Examples of the computer readable medium may include an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, and the like. More specific examples of the computer readable medium may include a floppy (registered trademark) disk, a diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an electrically erasable programmable read-only memory (EEPROM), a static random access memory (SRAM), a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a Blu-ray (registered trademark) disc, a memory stick, an integrated circuit card, and the like.

The computer-readable instruction may include: an assembler instruction, an instruction-set-architecture (ISA) instruction; a machine instruction; a machine dependent instruction; a microcode; a firmware instruction; state-setting data; or either a source code or an object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk (registered trademark), JAVA (registered trademark), C++, or the like, and a conventional procedural programming language such as a “C” programming language or a similar programming language.

Computer-readable instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatuses, or to programmable circuitry, locally or via a local area network (LAN), wide area network (WAN) such as the Internet, or the like, so that the computer-readable instructions are executed to create means for performing operations specified in the flowcharts or block diagrams. Examples of the processor include a computer processor, a processing unit, a microprocessor, a digital signal processor, a controller, a microcontroller, and the like.

FIG.4illustrates an example of a computer2200in which a plurality of aspects of the present invention may be entirely or partially embodied. A program installed in the computer2200may cause the computer2200to function as an operation associated with the apparatuses according to the embodiments of the present invention or as one or more sections of the apparatuses, or may cause the operation or the one or more sections to be executed, and/or may cause the computer2200to execute a process according to the embodiments of the present invention or a stage of the process. Such programs may be executed by a CPU2212to cause the computer2200to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described in the present specification.

The computer2200according to the present embodiment includes the CPU2212, an RAM2214, a graphic controller2216, and a display device2218, which are interconnected by a host controller2210. The computer2200also includes input/output units such as a communication interface2222, a hard disk drive2224, a DVD-ROM drive2226, and an IC card drive, which are connected to the host controller2210via an input/output controller2220. The computer also includes legacy input/output units such as an ROM2230and a keyboard2242, which are connected to the input/output controller2220via an input/output chip2240.

The CPU2212operates according to programs stored in the ROM2230and the RAM2214, thereby controlling each unit. The graphic controller2216acquires image data generated by the CPU2212in a frame buffer or the like provided in the RAM2214or in itself, such that the image data is displayed on the display device2218.

The communication interface2222communicates with other electronic devices via a network. The hard disk drive2224stores programs and data used by the CPU2212in the computer2200. The DVD-ROM drive2226reads a program or data from a DVD-ROM2201and provides the program or data to the hard disk drive2224via the RAM2214. The IC card drive reads the programs and the data from the IC card, and/or writes the programs and the data to the IC card.

The ROM2230stores therein boot programs and the like executed by the computer2200at the time of activation, and/or programs that depend on the hardware of the computer2200. The input/output chip2240may also connect various input/output units to the input/output controller2220via a parallel port, a serial port, a keyboard port, a mouse port, or the like.

The program is provided by a computer readable medium such as the DVD-ROM2201or the IC card. The program is read from a computer readable medium, installed in the hard disk drive2224, the RAM2214, or the ROM2230which are also examples of the computer readable medium, and executed by the CPU2212. The information processing written in these programs is read by the computer2200and provides cooperation between the programs and the above-described various types of hardware resources. The apparatus or method may be configured by implementing operations or processings of information according to use of the computer2200.

For example, in a case where communication is performed between the computer2200and an external device, the CPU2212may execute a communication program loaded in the RAM2214and instruct the communication interface2222to perform communication processing based on a processing written in the communication program. The communication interface2222, under control of the CPU2212, reads transmission data stored on a transmission buffering region provided in a recording medium such as the RAM2214, the hard disk drive2224, the DVD-ROM2201, or the IC card, and transmits the read transmission data to a network or writes reception data received from a network to a reception buffering region or the like provided on the recording medium.

In addition, the CPU2212may cause the RAM2214to read all or a necessary part of a file or database stored in an external recording medium such as the hard disk drive2224, the DVD-ROM drive2226(DVD-ROM2201), the IC card, or the like, and may execute various types of processing on data on the RAM2214. Then, the CPU2212writes the processed data back in the external recording medium.

Various types of information such as various types of programs, data, tables, and databases may be stored in a recording medium and subjected to information processing. The CPU2212may execute, on the data read from the RAM2214, various types of processing including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information retrieval/replacement, or the like described throughout the present disclosure and specified by instruction sequences of the programs, and writes the results back to the RAM2214. In addition, the CPU2212may retrieve information in a file, a database, or the like in the recording medium. For example, when a plurality of entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, is stored in the recording medium, the CPU2212may retrieve, out of the plurality of entries, an entry with the attribute value of the first attribute specified that meets a condition, read the attribute value of the second attribute stored in said entry, and thereby acquiring the attribute value of the second attribute associated with the first attribute meeting a predetermined condition.

The programs or software modules described above may be stored in a computer readable medium on or near the computer2200. In addition, a recording medium such as a hard disk or an RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer readable medium, thereby providing a program to the computer2200via the network.

While the present invention has been described by way of the embodiments, the technical scope of the present invention is not limited to the above described embodiments. It is apparent to persons skilled in the art that various alterations or improvements can be made to the above described embodiments. It is also apparent from description of the claims that the embodiments to which such alterations or improvements are made can be included in the technical scope of the present invention.

EXPLANATION OF REFERENCES