Patent Description:
A company suffers from loss when a machine in a factory has an abnormality and is stopped for a long time. Therefore, at the time of cyclic inspection, a skilled person detects an initial abnormality of the machine on the basis of his/her five senses. However, in recent years, since there are not enough skilled persons, mechanization and labor saving are desired. In order to detect an abnormality of a target machine at an early stage, it is necessary to collect and analyze data by attaching various types of sensors to the target machine. However, it is difficult to attach all of the necessary sensors to the target machine due to a physical restriction or a cost of the target machine. Therefore, a technique for estimating, from certain sensor data, other sensor data by using a regression model is proposed (<CIT> (PTL <NUM>)).

In the method described in Patent Literature <NUM>, all variations in the sensor data input in the regression model are described as variations to be estimated. Therefore, in PTL <NUM>, in a case where a variation unrelated to the sensor data to be estimated is added to the input sensor data, the unrelated variation is reflected on a value of the sensor data to be estimated.

<CIT> discloses a method for monitoring an asset system, including: using a plurality of auto-associative neural networks to determine estimates of actual values sensed by at least one sensor in at least one of the plurality of operating regimes; determining a residual between the estimated sensed values and the actual values sensed by the at least one sensor from each of the plurality of auto-associative neural networks; and combining the residuals for performing a fault diagnostic.

<CIT> discloses an anomaly detection system, including: an arithmetic device that executes processing of learning a predictive model that predicts a behavior of a monitoring target device based on operational data on the device, processing of adjusting an anomaly score such that the anomaly score for operational data under normal operation falls within a predetermined range, the anomaly score being based on a deviation of the operational data acquired from the monitoring target device from a prediction result obtained by the predictive model, processing of detecting an anomaly or a sign of an anomaly based on the adjusted anomaly score.

Therefore, an object of the present disclosure is to provide an abnormality detection system and an abnormality detection method capable of performing more stable abnormality detection. The technical improvement is achieved by the solution provided in accordance with the subjectmatter of the independent claims.

An abnormality detection system detects an abnormality of a target machine by a computer according to claim <NUM>. The computer includes a communication unit configured to acquire first data from a first sensor attached to the target machine and second data from a second sensor attached to the target machine, an arithmetic unit, and a memory unit. The arithmetic unit includes an encoding unit trained to generate latent expressions including a predetermined latent expression that estimates the second data on the basis of the first data, a decoding unit trained to restore the first data from the latent expressions, and an abnormality detection unit configured to detect the abnormality of the target machine on the basis of a restoration error between the first data and the first data restored by the decoding unit.

According to the invention, since the second data output from the second sensor is estimated using the predetermined latent expression which is a part of the latent expression, when the first data varies due to factors other than the second sensor, it is possible to describe the variation using another latent expression other than the predetermined latent expression and more robustly estimate the second data than in the related art.

Another aspect of the invention is a method according to claim <NUM> for detecting an abnormality of a target machine by a computer.

Hereinafter, an embodiment of the invention will be described with reference to the drawings. In this disclosure, a state of a machine to be subjected to abnormality detection (target machine) is determined by a minimum number of sensors. A target machine <NUM> includes various industrial machines such as a press machine, an injection molding machine, a heating furnace, an NC machine tool, a 3D printer, an electric discharge machine, a welding device, a machining center, a polishing device, an industrial sewing machine, and an industrial robot. The target machine <NUM> is not limited to the industrial machines, and may also include, for example, electric machines such as an air conditioner, a freezer, and a blower.

One target machine <NUM> may be configured with a plurality of machines such as a fan, a pump, a slider, a valve, and a motor. For example, physical quantities such as temperature, pressure, speed, voltage, current, load weight, load torque, vibration, and operation sound are measured by sensors. An abnormality detection system <NUM> determines a state and presence/absence of an abnormality of the target machine <NUM> on the basis of data output by the sensors.

The abnormality detection system <NUM> according to this disclosure estimates, on the basis of first data D0 output from a first sensor <NUM>, second data D1 output from a second sensor <NUM>, which is different from the first sensor <NUM>.

The first sensor <NUM> is attached to the target machine <NUM> whose abnormality is to be detected, and outputs the first data D0 at the time of training a machine learning model and at the time of operating a machine learning model. The second sensor <NUM> may be used only at the time of training the machine learning model to output the second data D1, and may be used also at the time of operating the machine learning model to output the second data D1.

In one embodiment, when the sensors attachable to the target machine <NUM> are limited, a first sensor <NUM> capable of estimating all sensors required for monitoring the state of the target machine <NUM> is selected. The first sensor <NUM> may be one or more. In the following description, a case where one first sensor <NUM> is used will be mainly described.

The abnormality detection system <NUM> according to the embodiment described above estimates the second data D1 output from the second sensor <NUM>, which is necessary for monitoring the state of the target machine <NUM> but is not installed in the target machine <NUM>, on the basis of the first data D0 output from the first sensor <NUM>. The abnormality detection system <NUM> monitors the state of the target machine <NUM>, detects an abnormality, and identifies a cause of the abnormality on the basis of the first data D0 and the estimated second data D1. The second sensor <NUM> whose output is estimated with the first data D0 measured by the first sensor <NUM> may be one or more.

According to this disclosure, since the machine learning model capable of estimating the second data D1 of the second sensor <NUM> which is not installed from the first data D0 of the first sensor <NUM> which is installed is generated at the time of training, it is possible to monitor the state and detect the abnormality even when the target machine <NUM> cannot have a plurality of sensors installed in at the time of operation.

In another embodiment of this disclosure, when physical quantities in accordance with the state of the target machine <NUM> are measured by the sensors, a set of hyper parameters to be used when calculating a difference between an observation value and a restoration value is selected according to a relation between the physical quantities and a type of the target machine. The set of the hyper parameters can be prepared in advance, for example, in accordance with physical properties and a required robustness of the target machine <NUM>. Accordingly, the abnormality of the target machine <NUM> can be detected as appropriate in accordance with the physical properties and the required robustness of the target machine <NUM>.

A first embodiment will be described with reference to <FIG>. <FIG> is a block diagram of the abnormality detection system <NUM> according to the present embodiment. In the present embodiment, the state of the target machine <NUM> such as a motor, a fan, a pump, a valve, and a slider is measured to monitor the state of target machine <NUM> and calculate an abnormality degree by a minimum number of the first sensors <NUM>. A power source of the target machine <NUM> may be any electricity, hydraulic pressure, and pneumatic pressure.

The abnormality detection system <NUM> includes, for example, the first sensor <NUM>, the second sensor <NUM>, an encoding unit <NUM>, a decoding unit <NUM>, a training unit <NUM>, an abnormality detection unit <NUM>, a state grasping unit <NUM>, a training data database DB1, and a training model database DB2.

The abnormality detection system <NUM> estimates the second data D1 output from the second sensor <NUM> which is not installed at the time of operating the target machine <NUM> (at the time of operating the machine learning model) from the first data D0 output from the first sensor <NUM> which can be installed at the time of operation. Therefore, the encoding unit <NUM> acquires latent expressions for estimating the second data D1 from the first data D0 at the time of training the machine learning model.

The decoding unit <NUM> restores the first data D0 output from the first sensor <NUM> using all the latent expressions obtained by the encoding unit <NUM>.

At the time of training the machine learning model (sometimes referred to as a model training time or a training mode), the training is performed by acquiring the first data D0 and the second data D1 from the machine <NUM> attached with all of the first and second sensors <NUM> and <NUM> necessary for grasping the state of the target machine <NUM>. At the time of training, the encoding unit <NUM> is trained so as to obtain, from the first data D0 of the first sensor <NUM> that can be installed at the time of operation, a latent expression that represents the second data D1 of the second sensor <NUM> which is not installed at the time of operation.

At the time of operation, the first data D0 from the first sensor <NUM> which is installed in the target machine <NUM> is used. The abnormality detection system <NUM> estimates the second data D1 of the second sensor <NUM> which is not installed in the target machine <NUM> by using the encoding unit <NUM>.

The state grasping unit <NUM> of the abnormality detection system <NUM> grasps the state of the target machine <NUM> from the first data D0 of the first sensor which is installed in the target machine <NUM> and the second data (estimation value) of the second sensor <NUM> which is not installed in the target machine <NUM>.

The state grasping unit <NUM> outputs an observation value or an estimation value of the first sensor <NUM> or the second sensor <NUM> as data D5. At the time of training, the state grasping unit <NUM> outputs the observation value of the first sensor <NUM> and the observation value of the second sensor <NUM>. As will be described in embodiments to be described later, the state grasping unit <NUM> can also output the estimation value of the second sensor <NUM> at the time of training. The state grasping unit <NUM> outputs the observation value of the first sensor <NUM> and the estimation value of the second sensor <NUM> as data D5 at the time of operation.

Here, the observation value of the first sensor <NUM> is the first data D0 actually measured by the first sensor <NUM>. The estimation value of the second sensor <NUM> is the second data D1 estimated from the first data.

The abnormality detection unit <NUM> of the abnormality detection system <NUM> calculates and outputs an abnormality degree D6 indicating whether an abnormality occurs in the target machine <NUM> on the basis of a difference between the first data D0 observed by the first sensor <NUM> and first data D0 restored by the encoding unit <NUM> and the decoding unit <NUM> constituting the machine learning model. The abnormality degree D6 can be used in other systems such as a production management system (not shown).

The training unit <NUM> learns parameters used in the encoding unit <NUM> and parameters used in the decoding unit <NUM> on the basis of training data stored in the training data database DB1, the first data D0 of the first sensor <NUM>, and the second data D1 of the second sensor <NUM>. Further, the training unit <NUM> updates the parameters used in the encoding unit <NUM> and the parameters used in the decoding unit <NUM> and stores the updated parameters in the training model database DB2 such that a predetermined loss function is minimized.

<FIG> illustrates a hardware configuration diagram of the abnormality detection system <NUM>. The abnormality detection system <NUM> includes, for example, an abnormality detection apparatus <NUM>, the first sensor <NUM>, and the second sensor <NUM>.

The abnormality detection apparatus <NUM> is implemented by a computer. The computer can be used as the abnormality detection apparatus <NUM> by causing the computer to execute a predetermined computer program. <FIG> illustrates an example of implementing the abnormality detection apparatus <NUM> from one computer, but instead, one or a plurality of abnormality detection apparatuses <NUM> may be formed from a plurality of computers. The computer may be a virtual computer.

The abnormality detection apparatus <NUM> is connected to the first and second sensors <NUM> and <NUM> via a communication network CN. Examples of the first and second sensors <NUM> and <NUM> include a microphone, a vibration sensor, a temperature sensor, a current sensor, a voltage sensor, a weight sensor, and a torque sensor. Here, a case in which the first sensor <NUM> is a microphone (sound sensor) and the second sensor <NUM> is a sensor that measures a physical quantity other than a sound will be described as an example.

The abnormality detection apparatus <NUM> includes, for example, an arithmetic unit <NUM>, a main memory device <NUM>, an auxiliary memory device <NUM>, an input unit <NUM>, an output unit <NUM>, and a communication unit <NUM>.

The arithmetic unit <NUM> includes one or a plurality of microprocessors, and implements a predetermined function as the abnormality detection apparatus <NUM> by reading the predetermined computer program stored in the auxiliary memory device <NUM> into the main memory device <NUM>. The predetermined function is, for example, the encoding unit <NUM>, the decoding unit <NUM>, the training unit <NUM>, the abnormality detection unit <NUM>, and the state grasping unit <NUM>.

The input unit <NUM> can include, for example, a keyboard, a touch panel, or a pointing device, and receives input from a user using the abnormality detection apparatus <NUM>. The output unit <NUM> can include, for example, a monitor display, a speaker, or a printer, and provides information to the user.

The communication unit <NUM> communicates with the first and second sensors <NUM> and <NUM> via the communication network CN. The communication unit <NUM> can also communicate with another computer (not shown).

A memory medium MM is, for example, a flash memory or a hard disk, and transfers a computer program or data to the abnormality detection apparatus <NUM> for memory, and reads and stores the computer program or data from the abnormality detection apparatus <NUM>. The memory medium MM may be directly connected to the abnormality detection apparatus <NUM>, or may be connected to the abnormality detection apparatus <NUM> via the communication network CN.

Configurations of the first and second sensors <NUM> and <NUM> will be described. The first sensor <NUM> will be described as an example. The second sensor <NUM> can be implemented in the same manner. The first sensor <NUM> includes, for example, a sensor unit <NUM>, a control unit <NUM>, a memory unit <NUM>, and a communication unit <NUM>.

The sensor unit <NUM> is a microphone that detects a sound of the target machine <NUM>. Therefore, in the following, the sensor unit <NUM> may be referred to as a microphone <NUM>. Data of the sound detected by the sensor unit <NUM> is stored in the memory unit <NUM>. The control unit <NUM> controlling the sensor <NUM> transmits the sound data stored in the memory unit <NUM> toward the abnormality detection apparatus <NUM>.

<FIG> illustrates a method of training the machine learning model. As illustrated in an upper side of <FIG>, the first data D0 output from the first sensor <NUM> is input to the encoding unit <NUM>. The encoding unit <NUM> calculates feature data of the input first data D0 and outputs encoded data D2. The encoded data D2 includes latent expressions (can also be referred to as latent variables) LV12(<NUM>) to LV12(<NUM>) and LV0 for restoring the first data from the input first data D0.

Here, four latent expressions are illustrated here for the sake of description, while more latent expressions are actually included. Among the encoded data D2, the predetermined latent expressions LV12(<NUM>) to LV12(<NUM>) are used to estimate the output of the second sensor <NUM> (the second data D1). A first predetermined latent expression LV12(<NUM>) is used, for example, to estimate output of a second sensor <NUM>(<NUM>) such as a temperature sensor. A second predetermined latent expression LV12(<NUM>) is used, for example, to estimate output of a second sensor <NUM>(<NUM>) such as a voltage sensor. A third predetermined latent expression LV12(<NUM>) is used, for example, to estimate output of a second sensor <NUM>(<NUM>) such as a load weight sensor. The temperature sensor, the voltage sensor, and the load weight sensor are examples for explanation.

The other latent expression LV0 is not used to estimate the output of the second sensor <NUM>, and is only used to restore the output of the first sensor <NUM>. That is, only predetermined data LV12(<NUM>) to LV12(<NUM>) as a part of the encoded data D2 output from the encoding unit <NUM> is used to estimate the output of the second sensor <NUM>, and remaining data LV0 is used to estimate the output of the first sensor <NUM> together with the predetermined data LV12(<NUM>) to LV12(<NUM>).

As illustrated in the center of <FIG>, at the time of training the machine learning model, not only the first sensor <NUM> but also the second sensors <NUM>(<NUM>) to <NUM>(<NUM>) are attached to the target machine <NUM> and output data RV12(<NUM>) to RV12(<NUM>) as observation values.

The training unit <NUM> compares the data estimated from the predetermined latent expressions LV12(<NUM>) to LV12(<NUM>) with the observation data RV12(<NUM>) to RV12(<NUM>) measured by the actual second sensors <NUM>(<NUM>) to <NUM>(<NUM>), and calculates an estimation error which is a difference therebetween.

Meanwhile, the decoding unit <NUM> restores the first data D0 of the first sensor <NUM> from the encoded data D2 output from the encoding unit <NUM>, and outputs restored first data D0A. Here, for the sake of understanding, a description will be given by changing to the code "D0" of the observed first data and the code "D0A" of the restored first data.

The training unit <NUM> compares the first data D0 output from the first sensor <NUM> and the first data D0A restored by the decoding unit <NUM>, calculates a restoration error (reconstruction error) which is the difference therebetween, and adjusts the parameters of the encoding unit <NUM> and the parameters of the decoding unit <NUM> such that a total loss of the estimation error and the restoration error is minimized.

<FIG> is an explanatory diagram illustrating an effect of the present embodiment. (<NUM>) of <FIG> illustrates a comparative example. In the comparative example, the second data D1 output from the second sensor <NUM> is estimated using all of the latent expressions LV12(<NUM>) to LV12(<NUM>) obtained from the first data D0 output from the first sensor <NUM>. Therefore, a variation added to the first data D0, even if unrelated to the second data D1, is reflected in the estimation of the second data D1.

For example, a case will be described in which the first sensor <NUM> measures an operation sound of the target machine <NUM> and an operation speed of the target machine is estimated from the operation sound. An operation sound of another apparatus near to the target machine <NUM> is not a measurement target of the first sensor <NUM>, and is thus a noise. When the first sensor <NUM> detects the operation sound of the other apparatus included in the operation sound of the target machine <NUM>, a noise component thereof is reflected in an estimation value of the operation speed.

In contrast, in the method of the present embodiment illustrated in (<NUM>) of <FIG>, only a part of the latent expressions generated by the encoding unit <NUM> is used to estimate the second data D1 of the second sensor <NUM>, and the other latent expression LV0 is not used to estimate the second data. Therefore, when the first data D0 input to the encoding unit <NUM> varies due to a cause unrelated to the second data D1 to be estimated, the variation is aggregated into the other latent expression LV0 which is not used for the estimation. Therefore, it is possible to confirm that the variation is unrelated to the estimation of the second data D1.

<FIG> is a flow chart of a training process S1. The abnormality detection system <NUM> acquires the first data D0 from the first sensor <NUM> (S101), and stores the acquired first data D0 in the auxiliary memory device <NUM> (S102).

The abnormality detection system <NUM> causes the main memory device <NUM> to read the first data D0 stored in the auxiliary memory device <NUM> (S103), and causes the encoding unit <NUM> to encode the first data D0 (S104). The abnormality detection system <NUM> estimates the second data D1 of the second sensor <NUM> from the predetermined latent expression of the latent expressions generated by the encoding unit <NUM> (S105).

The abnormality detection system <NUM> causes the decoding unit <NUM> to restore the first data D0 using all of the latent expressions generated by the encoding unit <NUM> (S106). The abnormality detection system <NUM> calculates, as a loss, the restoration error between the first data D0 input into the encoding unit <NUM> and the first data D0A restored by the decoding unit <NUM> and the estimation error between the second data observed by the second sensor <NUM> and the second data estimated from the predetermined latent expression (S107).

The abnormality detection system <NUM> repeatedly learns parameters of the machine learning model such that a value of the calculated loss is minimized (S109 to S111). These parameters of the machine learning model are stored in the training model database DB2 (S112).

That is, the abnormality detection system <NUM> determines whether a predetermined convergence condition is satisfied or an iteration count C1 of the present process exceeds an upper limit value ThC (S108). When the convergence condition is not satisfied and the iteration count C1 is equal to or less than the upper limit value ThC, the training unit <NUM> updates the parameters of the machine learning model (S109), calculates the convergence condition (S110), increments the iteration count C1 by <NUM>, and returns to step S108.

When the predetermined convergence condition is satisfied (S108: YES), the abnormality detection system <NUM> stores the parameters of the machine learning model to the training model database DB2 (S112).

<FIG> is a flow chart of a process S2 for detecting the abnormality of the target machine <NUM>. The abnormality detection process S2 corresponds to a process at the time of operation in which the machine learning model is operated. At the time of operation according to the present embodiment, only the first sensor <NUM> is provided in the target machine <NUM>, and the second sensor <NUM> is not provided in the target machine.

The abnormality detection system <NUM> reads the parameters of the machine learning model from the training model database DB2 and sets the read parameters to the encoding unit <NUM> and the decoding unit <NUM> (S201). The abnormality detection system <NUM> acquires the first data D0 from the first sensor <NUM> (S202) and stores the acquired first data D0 in the auxiliary memory device <NUM> (S203).

The abnormality detection system <NUM> causes the main memory device <NUM> to read the first data D0 stored in the auxiliary memory device <NUM> (S204), and causes the encoding unit <NUM> to encode the first data D0 to generate the latent expressions (S205).

The abnormality detection system <NUM> estimates the second data D1 of the second sensor <NUM> from the predetermined latent expression of the latent expressions generated by the encoding unit <NUM> (S206).

Meanwhile, the decoding unit <NUM> restores the first data D0 (D0A in <FIG>) using all of the latent expressions generated by the encoding unit <NUM> (S207).

In step S208, the abnormality detection system <NUM> treats the restoration error of the first data D0 as the abnormality degree, and determines whether the target machine <NUM> is in an abnormal state or a normal state from a magnitude of the abnormality degree. Further, the abnormality detection system <NUM> grasps the state of the target machine <NUM> from the first data which is the observation value and the second data D1 which is the estimation value, and estimates a cause of the case where it is determined that the target machine <NUM> is in the abnormal state.

For example, when it is determined from the operation sound of the target machine <NUM> that the target machine <NUM> is in the abnormal state, the abnormality detection system <NUM> can estimate the cause of the abnormality of the target machine <NUM> from the estimation value of the second data D1 output from the second sensor <NUM>, such as an operation speed, a temperature, or a voltage. For example, in the case where it is determined from the operation sound that the target machine <NUM> is abnormal, when a temperature of the target machine <NUM> is higher than a predetermined temperature, the abnormality detection system <NUM> can estimate that the abnormality of the target machine <NUM> is caused by the temperature.

According to the present embodiment constituted in this manner, the second data D1 of the second sensor <NUM> which is not used at the time of operation can be estimated on the basis of the first data D0 of the first sensor <NUM> which is used at the time of operation. Therefore, even when the target machine <NUM> cannot be attached with a plurality of sensors due to limitation of sensor attachment or the like, it is possible to detect the abnormality of the target machine <NUM> from the first and second data D0 and D1 of the plurality of sensors and to monitor the state thereof.

In the present embodiment, since it is only necessary to attach a part of the first sensors <NUM> of a sensor group necessary for monitoring the target machine <NUM> to the target machine <NUM>, it is possible to reduce a purchase cost and an attachment cost of the sensors. As a result, it is possible to reduce a cost of the abnormality detection system <NUM>.

In the present embodiment, since the second data D1 of the second sensor <NUM> is estimated from the predetermined latent expression which is a part of the latent expressions of the machine learning model, even when a noise unrelated to the second sensor <NUM> is included in the first data D0 of the first sensor <NUM>, an influence of the noise on the estimation of the second data D1 can be reduced, and reliability is improved.

A second embodiment will be described with reference to <FIG>. In the following embodiments including the present embodiment, differences from the first embodiment will be mainly described. In the present embodiment, a sensor to be selected as the first sensor <NUM> is selected from a plurality of first sensor candidates by using a physical causal database DB3 that defines a causal relation of the physical quantities.

<FIG> is a block diagram of an abnormality detection system 1A according to the present embodiment. The abnormality detection system 1A includes all of configurations of the abnormality detection system <NUM> illustrated in <FIG> and is added with a first sensor selection unit <NUM> and the physical causal database DB3.

The first sensor selection unit <NUM> receives a sensor list L0 defining sensors necessary for grasping the state of the target machine <NUM>, and then selects the first sensor <NUM> from the sensor group listed in the sensor list L0 with reference to the physical causal database DB3. A sensor that is not selected as the first sensor <NUM> in the sensor group listed in the sensor list L0 is the second sensor.

<FIG> illustrates a method for selecting the first sensor <NUM>. The first sensor selection unit <NUM> collates the sensor group listed in the sensor list L0 with the physical causal database DB3, and selects the first sensor <NUM> and the second sensor <NUM> that can most efficiently grasp the state of the target machine <NUM>. To most efficiently grasp the state of the target machine <NUM> means to grasp the state of the target machine <NUM> by the smallest number of sensors.

An example will be described. In the sensor list L0, a sensor for detecting a motor rotation number, a temperature sensor, a vibration sensor, a sensor for detecting a motor acceleration, and a sensor for detecting the operation sound are described as types of the sensors necessary for grasping the state of the target machine <NUM>. That is, physical quantities necessary for grasping the state of the target machine <NUM> are defined in the sensor list L0.

The physical causal database DB3 illustrates causal relations between a plurality of the physical quantities. For example, a motor rotation number of a machine may affect the operation sound but is not affected by the operation sound. It is possible to select the first sensor <NUM> that can efficiently grasp the state of the target machine <NUM> using such physical causal relations. The physical causal relations may be manually set in advance by an administrator or the like of the abnormality detection system 1A, or may be semi-automatically or automatically created by simulation software or the like.

The present embodiment constituted in this manner also achieves the same operational effect as that of the first embodiment. Further, in the present embodiment, it is possible to determine a sensor having a minimum configuration on the basis of the sensor list L0 illustrating the sensor group necessary for grasping the state of the target machine <NUM> (measurement values are necessary physical quantities). This improves convenience of the user using the abnormality detection system 1A.

A modification of the present embodiment will be described with reference to <FIG> and <FIG>. In the present modification, the first sensor <NUM> is selected from the sensor group listed in the sensor list L0 without using the physical causal database DB3. The same as the above, the sensor that is not selected as the first sensor <NUM> is the second sensor <NUM>.

<FIG> is a block diagram of an abnormality detection system 1A1 according to the present modification. The abnormality detection system 1A1 includes the first sensor selection unit <NUM> but does not include the physical causal database DB3.

<FIG> illustrates a method for selecting the first sensor <NUM> by the first sensor selection unit <NUM>. The first sensor selection unit <NUM> selects one sensor from the sensor group listed in the sensor list L0 as a first sensor candidate 11A and sets a sensor that is not selected as a second sensor candidate 12A. Then, the abnormality detection system 1A1 calculates a restoration error between an observation value D0 and a restoration value D3 of the first sensor candidate 11A and an estimation error between an observation value of the second sensor candidate 12A and an estimation value estimated from the predetermined latent expression generated by the encoding unit <NUM>.

The abnormality detection system 1A1 calculates a loss at the time of training while changing the first sensor candidate 11A, and selects a first sensor candidate 11A having the minimum loss as the first sensor <NUM>.

The present modification constituted in this manner also achieves the same operational effect as that of the present embodiment. Further, in the present modification, since the physical causal database DB3 is not used, it is not necessary to prepare the physical causal database DB3 in advance. Therefore, the present modification improves convenience of a user using the abnormality detection system 1A1.

A third embodiment will be described with reference to <FIG> and <FIG>. An abnormality detection system 1B according to the present embodiment calculates the abnormality degree using the estimation error of the second data D1 in addition to the restoration error of the first data D0 when detecting the abnormality of the target machine <NUM>.

<FIG> is a block diagram of an abnormality detection system 1B according to the present embodiment. <FIG> illustrates a method for detecting the abnormality. In the present embodiment, not only at the time of training but also at the time of operating the machine learning model, the first sensor <NUM> and the second sensor <NUM> are provided in the target machine <NUM>, and data of each is output as an observation value.

As illustrated in an upper side of <FIG>, at the time of training the machine learning model, the abnormality detection system 1B determines the parameters of the machine learning model, such that the second data of the second sensor <NUM> can be estimated on the basis of the predetermined latent expression of the latent expressions based on the first data D0 of the first sensor <NUM> (S301).

At the time of operating the machine learning model, the target machine <NUM> is provided with the first sensor <NUM> and the second sensor <NUM>. The first sensor <NUM> outputs the first data D0 as the observation value, and the second sensor <NUM> outputs the second data D1 as the observation value.

At the time of operating the machine learning model, the abnormality detection system 1B compares the second data D1 observed by the second sensor <NUM> and the second data D1 estimated from the first data D0 and calculates the estimation error (S302).

The abnormality detection system 1B calculates the restoration error between the first data D0 and the first data D0A restored from the first data D0 (S303), and determines the abnormality degree of the target machine <NUM> from the estimation error and the restoration error (S304).

The present embodiment constituted in this manner also achieves the same operational effect as that of the first embodiment. Further, in the present embodiment, the abnormality degree is calculated by including the estimation error in addition to the restoration error, and thus it is possible to detect the abnormality of the target machine <NUM> at higher accuracy than the method of calculating the abnormality degree by using only the restoration error. In the present embodiment, it is possible to detect the abnormality of the target machine <NUM> from the restoration error of the first data D0. Further, it is possible to estimate the cause of the abnormality from the first data D0 (observation value) and the second data D1 (observation value). Further, in the present embodiment, it is possible to detect the abnormality at high accuracy using the estimation error between the second data D1 (observation value) and the second data D1 (estimation value).

A fourth embodiment will be described with reference to <FIG> and <FIG>. In the present embodiment, when data on the target machine <NUM> is small, a training model created from data on a machine of the same type as the target machine <NUM> is used as an initial model, and the initial model is finely tuned with data obtained from the target machine <NUM>.

<FIG> is a block diagram of an abnormality detection system 1C. The abnormality detection system 1C according to the present embodiment includes a pre-trained model database DB4. The pre-trained model database DB4 stores a pre-trained model M0 (refer to <FIG>).

The pre-trained model M0 will be described with reference to <FIG>. The pre-trained model M0 is a training model generated for a machine of the same type, i.e., a machine that belongs to the same machine genre as the target machine <NUM> but has a different individual identification number.

The machine genre can be classified from information including a machine type, a vendor name, a driving method, and the like, for example, as in "hydraulic type press machine made by A" and "electric type die casting system made by B". Alternatively, the machine genre may be classified from the machine type and the driving method. Furthermore, for example, in the case of a machine that is highly standardized in the industry, the machine genre may be classified by only the machine type.

In the example of <FIG>, two machine genres MG1 and MG2 are illustrated. For example, the machine genre MG1 and the machine genre MG2 are products of the same vendor, but have driving methods different from each other, such as a hydraulic type and an electric type. It is assumed that a factory of a user who uses the abnormality detection system 1C is provided with machines belonging to the machine genres MG1 and MG2.

The machine genre MG1 includes two identification numbers IN1 and IN2. Meanwhile, the other machine genre MG2 includes two identification numbers IN3 and IN4. The target machine <NUM> is a machine having the identification number IN1 of the machine genre MG1.

The abnormality detection system <NUM> trains the training model in the training model database DB2 on the basis of the data measured by the first and second sensors <NUM> and <NUM> for the target machine <NUM> (MG1, IN1) (S401) as described above.

Here, in a case where the target machine <NUM> has just been introduced recently and a data amount measured for the target machine <NUM> is small, it takes time to generate a machine learning model at high accuracy. Therefore, the abnormality detection system <NUM> uses, as the pre-trained model M0, a machine learning model generated for a machine of the same type (MG1, IN2) that has an identification number IN different from that of the target machine <NUM> but belongs to the same machine genre MG1 (S402).

The abnormality detection system <NUM> acquires the pre-trained model M0 stored in the pre-trained model database DB4 and uses the pre-trained model M0 as an initial value of the machine learning model used to monitor the target machine <NUM> (S403).

Then, the abnormality detection system <NUM> finely tunes the machine learning model (= the initial value is the pre-trained model M0) on the basis of the data measured for the target machine <NUM> by the first sensor <NUM> and the second sensor <NUM> (S404).

The present embodiment constituted in this manner also achieves the same operational effect as that of the first embodiment. Further, in the present embodiment, when amounts of the first and second data D0 and D1 measured for the target machine <NUM> are small, the pre-trained model created for the machine of same type as the target machine <NUM> is used as the initial value of the machine learning model and is finely tuned by the data measured for the target machine <NUM>, and thus it is possible to create an appropriate machine learning model in a shorter time.

A fifth embodiment will be described with reference to <FIG> and <FIG>. In the present embodiment, when the physical quantities measured for the target machine <NUM> continuously change in accordance with the state of the target machine <NUM>, data between the observed data is interpolated.

<FIG> is a block diagram of an abnormality detection system 1D according to the present embodiment. The abnormality detection system 1D includes a data expansion unit <NUM> for expanding the first data D0 detected by the first sensor <NUM>. In the present embodiment, both the first sensor <NUM> and the second sensor <NUM> can be used at the time of training the machine learning model used to grasp the state of the target machine <NUM>. At the time of operating the machine learning model, the first sensor <NUM> may be used, and the second sensor <NUM> may or may not be used. That is, at the time of operation, the second data of the second sensor <NUM> may be estimated on the basis of the first data.

<FIG> illustrates a method for expanding the data. For example, a case will be described in which the first sensor <NUM> is a microphone for measuring the operation sound, and the operation sound of the target machine <NUM> changes in accordance with a continuously changing state such as the temperature, the operation speed, and the pressure of the target machine <NUM>.

In this case, it is difficult to measure all the operation sounds corresponding to the continuous change of the target machine <NUM>. Therefore, the abnormality detection system 1D generates expansion data (interpolation data) D0E by using a data expansion method such as mix-up on the basis of first data D0 of an operation sound measured in one state of the target machine <NUM> (an operation sound at an operation speed v100) and first data D0 of an operation sound measured in another state of the target machine <NUM> (an operation sound at an operation speed v300).

In this embodiment, unmeasured data such as data of an operation sound at an operation speed v101, data of an operation sound at an operation speed v102, data of an operation sound at an operation speed v103,. , and data of an operation sound at an operation speed v299 can be acquired in advance.

The present embodiment constituted in this manner also achieves the same operational effect as that of the first embodiment. Further, in the present embodiment, even when the first data D0 output from the first sensor <NUM> changes in accordance with a continuous state change of the target machine <NUM>, the first data D0 of the first sensor <NUM> can be expanded and acquired in advance, and the machine learning model can be created in an early stage.

A sixth embodiment will be described with reference to <FIG>. In the present embodiment, it is possible to improve the abnormality detection accuracy and to satisfy the required robustness by optimizing a loss function used in the machine learning model in accordance with characteristics of the target machine <NUM>.

<FIG> is a block diagram of an abnormality detection system 1E at the time of training. The abnormality detection system 1E may include, for example, a sensor <NUM>, a feature data extraction unit <NUM>, a machine learning unit <NUM>, a loss calculation unit <NUM>, an abnormality determination unit <NUM>, a waveform determination unit <NUM>, a hyper parameter setting unit <NUM>, a training data database DB1E, a training model database DB2E, and a hyper parameter set database DB5.

Here, a sensor such as a microphone that measures the operation sound of the target machine <NUM> will be described as an example of the sensor <NUM>. When a vibration is measured instead of the operation sound, a vibration sensor may be used as the sensor <NUM>.

The feature data extraction unit <NUM> extracts feature data vector (hereinafter, referred to as feature data) D51 from data <NUM> measured by the sensor <NUM>, and stores the extracted feature data D51 in the training data database DB1E.

The machine learning unit <NUM> trains the machine learning model stored in the training model database DB2E using the feature data D51 extracted by the feature data extraction unit <NUM>.

The loss calculation unit <NUM> calculates a loss between an observation value of the sensor <NUM> and a restoration value restored by the machine learning model. It is possible to determine how abnormal the target machine <NUM> is on the basis of a magnitude of the loss, and thus the loss calculation unit <NUM> may be referred to as an abnormality degree calculation unit <NUM>.

The abnormality determination unit <NUM> determines whether the target machine <NUM> is abnormal on the basis of data D53 output from the loss calculation unit <NUM> (loss or abnormality degree).

The waveform determination unit <NUM> determines a waveform pattern registered in advance to which a waveform pattern of the operation sound corresponds, on the basis of the extracted feature data D51. The hyper parameter setting unit <NUM> refers to the hyper parameter set database DB5 to select one hyper parameter set on the basis of a determination result of the waveform determination unit <NUM>, and sets the selected hyper parameter set D52 in the loss calculation unit <NUM>.

<FIG> is a hardware configuration diagram when the abnormality detection system 1E is implemented using a computer <NUM>.

The computer <NUM> includes, for example, a control unit <NUM>, a memory unit <NUM>, a storage <NUM>, and an input/output device (I/O device) <NUM>, and is connected to the communication network CN. In addition, the computer <NUM> can be connected to the memory medium MM, and can transmit and receive a computer program or data between the memory medium MM and the memory <NUM> or the storage <NUM>.

A predetermined computer program stored in the storage <NUM> is read into the memory <NUM> and executed by the control unit (arithmetic unit) <NUM>, thereby implementing the feature data extraction unit <NUM>, the machine learning unit <NUM>, the loss calculation unit <NUM>, and the abnormality determination unit <NUM>. A part or all of the predetermined computer program can be stored in the memory medium MM.

<FIG> illustrates a method for setting the hyper parameter set in accordance with the characteristics and the required robustness of the target machine <NUM> in the loss calculation unit <NUM>.

The waveform determination unit <NUM> selects a waveform pattern and the like corresponding to the machine type on the basis of a table T1 illustrating a combination of the machine type and the waveform pattern and the like. The waveform pattern and the like indicate a tendency of a waveform pattern and a noise for each target machine <NUM>. For example, a fan outputs a stationary operation sound and hardly generates an abrupt sound (noise). A pump outputs a stationary operation sound but may also generate an abrupt sound sometimes. A slider generates different sounds during operation and during stop, and thus a stationary operation sound and a non-stationary operation sound coexist. A valve operates intermittently, and thus has a non-stationary operation sound. The waveform determination unit <NUM> may determine a machine type to which the target machine <NUM> corresponds from the feature data D51 of the operation sound of the target machine <NUM>, or by acquiring a signal indicating the type of the target machine <NUM>. For example, when the abnormality detection system 1E is installed at a factory, the type of the target machine <NUM> may be set manually.

The hyper parameter setting unit <NUM> refers to the hyper parameter set database DB5 on the basis of the determination result of the waveform determination unit <NUM> (the corresponding waveform pattern and the like), thereby identifying a hyper parameter set to be set in the loss calculation unit <NUM>.

The hyper parameter set database DB5 manages, for example, a set number, the waveform pattern and the like, the required robustness, the hyper parameter, the number of samples, and a weight of the loss function in association with one another.

The required robustness illustrates the required robustness for determining the abnormality of the target machine <NUM>. The required robustness includes, for example, "high", "low", "medium-low", and "medium-high". As illustrated in <FIG>, the hyper parameter includes, for example, a standard deviation δ and an average value µ of a probability distribution function used by the loss calculation unit <NUM>.

The hyper parameter setting unit <NUM> selects a hyper parameter set corresponding to the determined waveform pattern and the like, and sets the hyper parameter and the weight of the loss function included in the selected hyper parameter set in the loss calculation unit <NUM>.

As illustrated in <FIG>, in a case such as a fan in which the operation sound is stable and is hardly mixed with a noise, it is not necessary to increase the required robustness, and thus the required robustness is set low. Therefore, in this case, a hyper parameter set is selected such that a range determined as normal by the loss calculation unit <NUM> is narrowed (Set-<NUM>).

In contrast, in a case such as a valve that operates irregularly and outputs a non-stationary operation sound, the required robustness increases. This is because that since the operation sound of the valve is not a stable pattern, there is a high possibility that the valve is erroneously determined to be abnormal if the required robustness is lowered. In this case, a hyper parameter is selected such that the range determined as normal by the loss calculation unit <NUM> is widened (Set-<NUM>). A hyper parameter set used in a case where the target machine <NUM> is a pump or a slider is an intermediate value has a range determined to be normal by the loss calculation unit <NUM> that is between the case of a fan and the case of a valve.

<FIG> is a flow chart illustrating a process S11 at the time of training. The abnormality detection system 1E acquires the data D50 measured by the sensor <NUM> (S1101), and stores the acquired data D50 in the storage <NUM> (S1102). The abnormality detection system 1E reads the data D50 from the storage <NUM> to the memory <NUM> (S1103), and causes the feature data extraction unit <NUM> to extract the feature data D51 (S1104).

The abnormality detection system 1E causes the waveform determination unit <NUM> to determine the waveform pattern and the like of the feature data D51 (S1105), and causes the hyper parameter setting unit <NUM> to select the hyper parameter set in accordance with the waveform pattern and the like (S1106).

The loss calculation unit <NUM> uses the hyper parameter set that is set by the hyper parameter setting unit <NUM> to calculate the loss of the machine learning model (S1108).

The abnormality detection system 1E repeatedly learns the parameters of the machine learning model such that a value of the calculated loss is minimized (S1109 to S1111). These parameters of the machine learning model are stored in the training model database DB2E (S1112).

That is, the abnormality detection system 1E determines whether the predetermined convergence condition is satisfied or the iteration count C1 of the present process exceeds the upper limit value ThC (S1108). When the convergence condition is not satisfied and the iteration count C1 is equal to or less than the upper limit value ThC, the machine learning unit <NUM> updates the parameters of the machine learning model (S1109), calculates the convergence condition (S1110), increments the iteration count C1 by <NUM>, and returns to step S1108.

When the predetermined convergence condition is satisfied (S1108: YES), the abnormality detection system 1E stores the parameters of the machine learning model to the training model database DB2E (S1112).

<FIG> is a flow chart of a process S21 for detecting the abnormality of the target machine <NUM>. The abnormality detection system 1E reads and sets the parameters of the machine learning model from the training model database DB2E (S2101).

The abnormality detection system 1E acquires the data D50 from the sensor <NUM> (S2102), and stores the acquired data D50 in the storage <NUM> (S2103).

The abnormality detection system 1E reads the data D50 stored in the storage <NUM> into the memory <NUM> (S2104), and causes the feature data extraction unit <NUM> to extract the feature data D51 (S2105).

The abnormality detection system 1E causes the waveform determination unit <NUM> to determine the waveform pattern and the like (S2106), and causes the hyper parameter setting unit <NUM> to set the hyper parameter set in accordance with the determination result in the loss calculation unit <NUM> (S2107).

The abnormality detection system 1E causes the loss calculation unit <NUM> to calculate the abnormality degree by a method according to properties of the target machine <NUM> (S2108). The abnormality detection system 1E causes the abnormality determination unit <NUM> to determine whether the calculated abnormality degree is larger than a predetermined threshold value (S2109).

When the calculated abnormality degree is larger than the predetermined threshold value (S2109: YES), the abnormality determination unit <NUM> determines that the target machine <NUM> is in the abnormal state (S2110). In contrast, when the calculated abnormality degree is equal to or less than the predetermined threshold value (S2109: NO), the abnormality determination unit <NUM> determines that the target machine <NUM> is in the normal state (S2111).

According to the present embodiment constituted in this manner, the hyper parameter set selected in accordance with the type of the target machine <NUM> can be set in the loss calculation unit <NUM>, and thus the abnormality of the target machine <NUM> can be detected more appropriately.

In the present embodiment, the robustness is determined according to a degree of change in the physical quantities measured for the target machine <NUM> (whether the physical quantities are stationary or non-stationary, whether noise is mixed, or the like), and thus it is possible to detect the abnormality according to the properties of the target machine <NUM>.

The present embodiment can be combined with the first to the fifth embodiments. For example, also in the present embodiment, the first sensor and the second sensor may be used as in the first embodiment. A value of the second sensor may be estimated using a part of the latent expressions obtained from the first sensor.

Claim 1:
An abnormality detection system (<NUM>) for detecting an abnormality of a target machine (<NUM>) by a computer, wherein
the computer includes a communication unit configured to acquire first data (D0) from a first sensor (<NUM>) attached to the target machine and second data (D1, RV12(<NUM>), RV12(<NUM>), RV12(<NUM>)) from a second sensor (<NUM>, <NUM>(<NUM>), <NUM>(<NUM>), <NUM>(<NUM>)) attached to the target machine, an arithmetic unit (<NUM>), and a memory unit,
whereby the arithmetic unit (<NUM>) includes
an encoding unit (<NUM>) trained to generate latent expressions (LV12(<NUM>), LV12(<NUM>), LV12(<NUM>), LV0) including a predetermined latent expression (LV12(<NUM>), LV12 (<NUM>), LV12(<NUM>)) for estimating the second data on the basis of the first data and a latent expression (LV0) able to be used for restoring the first data,
a decoding unit (<NUM>) trained to restore the first data (DOA) from the latent expressions (LV12(<NUM>), LV12(<NUM>), LV12(<NUM>), LV0), and
an abnormality detection unit (<NUM>) configured to detect the abnormality of the target machine on the basis of a restoration error between the first data(DO) and the first data (DOA) restored by the decoding unit, wherein
the arithmetic unit (<NUM>) further includes a training unit (<NUM>) configured to compare the first data (D0) output from the first sensor (<NUM>) and the first data (D0A) restored by the decoding unit (<NUM>), and calculate the restoration error which is the difference therebetween,
wherein the training unit (<NUM>) is further configured to compare the second data estimated from the predetermined latent expression (LV12(<NUM>), LV12(<NUM>), LV12(<NUM>)) with the second data (RV12(<NUM>), RV12(<NUM>), RV12(<NUM>)) measured by the second sensor (<NUM>(<NUM>), <NUM>(<NUM>), <NUM>(<NUM>)), and calculate an estimation error which is a difference therebetween,
wherein the training unit (<NUM>) is further configured to adjust parameters of the encoding unit (<NUM>) and parameters of the decoding unit (<NUM>) such that a total loss of the estimation error and the restoration error is minimized.