Peripheral device, monitoring system, monitoring method, and program

Abstract

To provide a peripheral apparatus, a monitoring system, a monitoring method, and a program more suitable for monitoring a device to be monitored.SOLUTION: A peripheral apparatus 10 includes: a power receiving unit 11 for receiving power from a device to be monitored; an environment sensor 12 for acquiring environmental information around the device; a transmitting unit 13 for transmitting the environment information; and a rechargeable battery 14 that is charged by receiving power from the device via the power receiving unit 11. The environmental sensor 12 and the transmitting unit 13 are operated by electric power discharged from the rechargeable battery 14 when no power is supplied through the power receiving unit 11.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a peripheral device, a monitoring system, a monitoring method, and a program.

Background Art

[0002] Techniques for detecting abnormalities in computers for industrial applications and the like have been developed.

[0003] For example, Patent Document 1 describes a technique for generating a prediction model for a plurality of similar devices and generating a prediction model dedicated to a newly installed device based on this prediction model.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] An object of the present disclosure is to provide a peripheral device, a monitoring system, a monitoring method, and a program that are more suitable for monitoring a device to be monitored.

Means for Solving the Problems

[0006] A peripheral device according to an aspect of the present embodiment includes a power receiving unit that receives power supply from a device to be monitored, an environment sensor that acquires environmental information around the device, a transmission unit that transmits the environmental information, and a rechargeable battery that is charged by receiving power supply from the device via the power receiving unit. The environment sensor and the transmission unit operate by discharge power from the rechargeable battery when power supply via the power receiving unit becomes impossible.

[0007] One aspect of the monitoring system according to the present embodiment includes a peripheral device provided corresponding to a device to be monitored, and a monitoring server that communicates with the peripheral device. The peripheral device includes a power receiving and supplying unit that receives power supply from the device, an environment sensor that acquires environment information around the device, a transmitting unit that transmits the environment information, and a rechargeable battery that is charged by receiving power supply from the device via the power receiving and supplying unit. The environment sensor and the transmitting unit operate by the discharge power from the rechargeable battery when power supply via the power receiving and supplying unit becomes unavailable. The monitoring server includes a receiving unit that receives the environment information from the peripheral device, an operation information acquisition unit that acquires the operation status of the device as operation information, and a status determination unit that determines the status of the device using the operation information and the environment information.

[0008] One aspect of the monitoring method according to the present embodiment includes a charging step of charging a rechargeable battery by receiving power supply from a device to be monitored, an environment information acquisition step of acquiring environment information around the device using the discharge power from the rechargeable battery when power supply from the device becomes unavailable, and a transmitting step of transmitting the environment information using the discharge power from the rechargeable battery, which are executed by a peripheral device.

[0009] One aspect of the program according to the present embodiment causes a peripheral device to execute, as a monitoring method, a charging step of charging a rechargeable battery by receiving power supply from a device to be monitored, an environment information acquisition step of acquiring environment information around the device using the discharge power from the rechargeable battery when power supply from the device becomes unavailable, and a transmitting step of transmitting the environment information using the discharge power from the rechargeable battery.

Advantages of the Invention

[0010] According to the present disclosure, it is possible to provide a peripheral device, a monitoring system, a monitoring method, and a program that are more suitable for monitoring a device to be monitored.

Brief Description of the Drawings

[0011]

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Embodiments for Carrying Out the Invention

[0012] Embodiment 1 (1A) Hereinafter, Embodiment 1 of the present disclosure will be described with reference to the drawings. In this (1A), peripheral devices provided corresponding to a device to be monitored such as a computer will be described.

[0013] FIG. 1 is a block diagram showing an example of a peripheral device. The peripheral device 10 includes a power receiving and supplying unit 11, an environmental sensor 12, a transmitting unit 13, and a rechargeable battery 14. Each part of the peripheral device 10 is controlled by a control unit (controller) (not shown). Hereinafter, each component will be described.

[0014] The power supply and reception unit 11 is a part that receives power supply from a device to be monitored (hereinafter referred to as the target device). The power supply and reception unit 11 is, for example, a connector physically connected to the peripheral device 10. By inserting and removing the power supply and reception unit 11 from the connector of one target device (not shown), the peripheral device 10 can be attached to and detached from the target device. Note that examples of the connector include terminals such as a USB port and a LAN (Local Area Network) port. When the connector is a USB port, the peripheral device is a device externally connected to the target device by a peripheral bus for connecting peripheral devices, such as a USB (Universal Serial Bus) peripheral. Also, the rechargeable battery 14 described later can be charged by power supply from the target device via the power supply and reception unit 11. For example, when the power supply and reception unit 11 is a LAN port, the target device supplies power to the peripheral device 10 by PoE (Power over Ethernet).

[0015] However, the power supply and reception unit 11 may receive power wirelessly from the target device without physically connecting the peripheral device 10 and the target device. In this case, the power supply and reception unit 11 can receive power supply by any method such as an electromagnetic induction method, a magnetic field resonance method, a radio wave reception method, and an electric field coupling method.

[0016] The environment sensor 12 is a sensor that acquires environmental information around the target device. The environmental information includes, for example, at least one detected value such as temperature, humidity, acceleration, vibration (e.g., impact), sound (e.g., sound pressure), atmospheric pressure, wind, illuminance, ultraviolet light, and the content of a certain substance in the air such as VOC (Volatile Organic Compounds), but is not limited thereto. These factors may affect the operation of the target device in at least either the short term or the long term.

[0017] The transmitting unit 13 is a transmitter that transmits the environmental information acquired by the environmental sensor 12. The destination of the environmental information may be, for example, a monitoring server that determines the state (e.g., operating state) of the target device using the environmental information. In this example, the transmitting unit 13 transmits the environmental information via the wireless R1, but wired transmission may also be used. The timing at which the transmitting unit 13 transmits the environmental information may be periodic, or transmission may be performed when the detected environmental information has characteristics. The environmental sensor 12 and the transmitting unit 13 can usually receive power supply via the power receiving and supplying unit 11.

[0018] The rechargeable battery 14 is charged by receiving power supply from the target device via the power receiving and supplying unit 11. The rechargeable battery 14 can supply power to at least the environmental sensor 12 and the transmitting unit 13. In particular, when the environmental sensor 12 and the transmitting unit 13 can no longer receive power supply via the power receiving and supplying unit 11, they can operate with the discharge power from the rechargeable battery 14. When the environmental sensor 12 and the transmitting unit 13 can no longer receive power supply via the power receiving and supplying unit 11, for example, it is when the target device has an abnormal operation such as an abnormal termination.

[0019] FIG. 2 is a flowchart showing an example of a typical process of the peripheral device 10, and the process of the peripheral device 10 is explained by this flowchart. First, the peripheral device 10 charges the rechargeable battery 14 by receiving power supply via the power receiving and supplying unit 11 with the target device (step S11).

[0020] Then, when power supply can no longer be received, the environmental sensor 12 acquires environmental information around the target device using the discharge power from the rechargeable battery 14 (step S12). The transmitting unit 13 transmits the environmental information using the discharge power from the rechargeable battery 14 (step S13). The peripheral device 10 can operate as described above.

[0021] In this way, even when the power supply is stopped due to the abnormal termination (e.g., system shutdown) of the target device, the peripheral device 10 can acquire and transmit environmental information by means of the rechargeable battery 14. That is, the peripheral device 10 can accurately acquire information before and after the abnormal termination of the target device, namely, information for predicting the abnormality of the target device. Also, the peripheral device 10 can receive power supply from the target device by means of the power receiving and supplying unit 11. Therefore, it is not necessary to pre-incorporate dedicated hardware or device drivers into a computer or the like that does not have a state monitoring function, and this function can be added by subsequently installing the peripheral device 10.

[0022] Also, in this example, one peripheral device 10 corresponds to one target device to be provided. However, a plurality of target devices may be provided, and one peripheral device 10 may be provided corresponding to those plurality of target devices. In that case, the power receiving and supplying unit 11 receives power supply from at least any one of the plurality of target devices. The environment sensor 12 acquires environmental information around the plurality of target devices, and the transmitting unit 13 transmits environmental information regarding the plurality of target devices.

[0023] Note that the peripheral device 10 may further include a storage unit (storage) for storing environmental information. When the transmitting unit 13 receives power supply via the power receiving and supplying unit 11, it transmits the environmental information stored in the storage unit at regular intervals, while when it can no longer receive power supply via the power receiving and supplying unit 11, it may transmit the environmental information stored in the storage unit regardless of the regular timing. Also, as another example, the peripheral device 10 may further include a determination unit for determining whether or not the detection value of the environmental information detected by the environment sensor 12 is equal to or greater than a predetermined threshold value. When the determination unit determines that the detection value is less than the predetermined threshold value, the transmitting unit 13 transmits the environmental information stored in the storage unit at regular intervals, while when the determination unit determines that the detection value is equal to or greater than the predetermined threshold value, it may transmit the environmental information stored in the storage unit regardless of the regular timing.

[0024] The phrase "transmit regardless of the regular timing" mentioned above means, for example, that when the peripheral device 10 can no longer receive power supply, or when the detected value is equal to or greater than a predetermined threshold value, the environmental information is transmitted even if the original regular timing for transmission has not arrived. Also, it may be to shorten the transmission timing of the transmission unit, that is, to increase the frequency of transmitting the environmental information. Thereby, when an abnormality occurs in the target device or there is a sign of an abnormality, information for predicting the abnormality of the target device can be surely acquired.

[0025] When the transmission unit 13 transmits the environmental information to the monitoring server, and then when the peripheral device 10 receives an instruction from the monitoring server to increase the frequency of transmitting the environmental information, the peripheral device 10 may control the transmission unit 13 to increase the frequency of transmitting the environmental information. Thereby, the peripheral device 10 can surely acquire information for predicting the abnormality of the target device according to the instruction from the monitoring server.

[0026] (1B) Next, in (1B), a monitoring system including the peripheral device 10 and the monitoring server will be described.

[0027] FIG. 3 is a block diagram showing an example of the monitoring system. The monitoring system S1 includes the peripheral device 10 and the monitoring server 20. Since the description of the peripheral device 10 is as shown in (1A), the description is omitted. The monitoring server includes a receiving unit (receiver) 21, an operation information acquisition unit 22, and a state determination unit 23. Hereinafter, each of these components will be described.

[0028] The receiving unit 21 receives environmental information from one or a plurality of peripheral devices 10 to be monitored. That is, the monitoring server 20 is the transmission destination where the transmission unit 13 of the peripheral device 10 transmits the environmental information. In this example, it is wirelessly connected to the peripheral device 10. However, the monitoring server 20 may acquire the environmental information by performing wired communication with the peripheral device 10.

[0029] The operation information acquisition unit 22 acquires, as operation information, the operation status of one or more target devices for which the reception unit 21 has received environmental information. The operation status is information as an indicator indicating whether the target device is operating normally, and examples thereof include at least one detection value such as the temperature of the target device, the voltage value inside the device, and the fan rotation speed of the device, but are not limited thereto.

[0030] The operation information acquisition unit 22 acquires operation information regarding one or more target devices wirelessly or by wire. The operation information acquisition unit 22 may acquire this operation information from, for example, the target device, or may acquire this operation information from another device such as the peripheral device 10. When acquiring this operation information from the peripheral device 10, the peripheral device 10 is further provided with an operation information unit that acquires operation information via the power receiving and supplying unit 11.

[0031] The state determination unit 23 determines the state of one or more target devices using the environmental information received by the reception unit 21 and the operation information acquired by the operation information acquisition unit 22. The state determination unit 23 may, for example, determine whether the target device is currently operating normally (whether an abnormality has occurred) using the operation information and the environmental information. In another example, the state determination unit 23 may predict whether there is a sign of an abnormality (that is, whether an abnormality is likely to occur in the future during operation) using the operation information and the environmental information. The state determination unit 23 may make this prediction, for example, by generating a model using the environmental information and the operation information as teacher data by AI (Artificial Intelligence) such as machine learning. Details of this prediction will be described later. When the state determination unit 23 determines the states of a plurality of target devices, the state of each target device can be determined individually.

[0032] Figure 4 is a sequence diagram showing an example of typical processing of the monitoring system S1. The processing of the monitoring system S1 is explained by this sequence diagram. First, when the environmental sensor 12 of the peripheral device 10 cannot receive power supply, it acquires environmental information around the device using the discharge power from the rechargeable battery 14 (step S21). Then, the transmission unit 13 transmits the acquired environmental information to the monitoring server 20 using the discharge power from the rechargeable battery 14 (step S22). Note that before step S21, the rechargeable battery 14 is charged by receiving power supply from the power receiving and supplying unit 11. This is as described in (1A).

[0033] The receiving unit 21 of the monitoring server receives the environmental information transmitted in step S22 (step S23). Also, the operation information acquisition unit 22 acquires the operation status of the target device as operation information (step S24). Note that step S24 may be executed before or after step S23, or at the same timing as step S23. The state determination unit 23 determines the state of the target device using the environmental information and operation information acquired in steps D23 and S24 respectively (step S25).

[0034] As shown in (1A), the peripheral device 10 can accurately acquire information before and after the abnormal termination of the target device, that is, information for predicting the abnormality of the target device. Therefore, the monitoring server 20 can more accurately determine the state of the target device based on the information for predicting the abnormality of the target device.

[0035] (1C) Hereinafter, in (1C) to (1E), examples of devices that perform prediction or determination regarding the state of the target device will be described. This device may be applied as the monitoring server 20 in (1B), or may be applied as other devices.

[0036] Figure 5 is a block diagram showing an example of a prediction device. The prediction device 30 includes an acquisition unit 31, a prediction model generation unit 32, and a prediction unit 33. Hereinafter, each of these components will be described.

[0037] The acquisition unit 31 acquires the operating status of one target device as operation information and also acquires the environmental information around the target device. The operation information and the environmental information are as described in (1A). The acquisition unit 31 may acquire the operating status from the target device or from another device (for example, peripheral devices). Further, the acquisition unit 31 may acquire the environmental status from peripheral devices provided corresponding to the target device. Note that the operation information and the environmental information acquired by the acquisition unit 31 may be stored in the storage unit inside the prediction device 30, or may be stored in a storage unit such as a DB (database) outside the prediction device 30.

[0038] The prediction model generation unit 32 sets, as a set of variables, at least an explanatory variable including environmental information in a predetermined time period and at least an objective variable including operation information at a timing immediately after the predetermined time period, for the data acquired by the acquisition unit 31. Then, the prediction model generation unit 32 generates a prediction model of the operating status using, as teacher data, a plurality of sets of variables defined by shifting the time slots in the set of variables in order. The prediction model generation unit 32 can extract the operation information and the environmental information stored in the storage unit and generate this prediction model.

[0039] The prediction unit 33 predicts the operating status of the target device using the generated prediction model and the environmental information newly acquired by the acquisition unit 31.

[0040] Each unit of the prediction device 30 may execute the above processing for each of a plurality of target devices. Hereinafter, details of processing examples of the prediction model generation unit 32 and the prediction unit 33 will be described.

[0041] Figure 6 shows a CNN (Convolutional Neural Network) model section 321, which is an example of the configuration of the model generated by the prediction model generation section 32. The CNN model section 321 outputs output data V2 based on input data V1. As will be described later, the input data V1, which is an explanatory variable, includes five types of environmental information, and its input size is 5 * N. Here, N is the range of time slots and indicates a predetermined time range (the range of past data used for learning). Also, since the output data V2, which is the target variable, includes five types of environmental information V2a and one type of operation information V2b, the output size of the output data V2 is 6. The environmental information V2a is the predicted value of the environmental information at the next time (immediately after) in the predetermined time range, and the operation information V2b is the predicted value of the operation information at the next time (immediately after) in the predetermined time range. Also, the size of each data is not limited to the case described above.

[0042] The CNN model section 321 has at least a convolutional layer 321a and a fully connected layer 321b for calculation. Each section will be described below.

[0043] The convolutional layer 321a is a layer that extracts features from the input data V1. The convolutional layer 321a has convolutional filters and may be provided with a ReLU (Rectified Linear Unit) layer as needed. Also, the convolutional layer 321a may be provided with a pooling layer at the subsequent stage (the part connected to the fully connected layer 321b). In this way, the convolutional layer 321a can execute any known process necessary for the convolutional process.

[0044] The fully-connected layer 321b executes classification processing or regression processing based on the feature quantities extracted by the convolutional layer 321a. For example, when the fully-connected layer 321b predicts the operation information V23b, a threshold value regarding the operation status may be set, and by determining the magnitude relationship between the threshold value and the predicted operation information V23b, it may be determined whether the operation state is normal or abnormal. Also, when the fully-connected layer 321b predicts the environmental information V22a, regression processing may be executed. The fully-connected layer 321b may further include, in addition to the fully-connected layer itself, one or more of at least any of a ReLU layer, a dropout layer, a batch normalization layer, a layer normalization layer, etc., as necessary. Also, in the intermediate layer of the fully-connected layer 321b, one or more output layers may be provided for the purpose of adding an auxiliary loss. The fully-connected layer 321b outputs the environmental information V2a and the operation information V2b as output data V2 via an output layer (not shown).

[0045] In each of the convolutional layer 321a and the fully-connected layer 321b, the number of layers of the model, the activation function, the optimization method, the number of epochs, etc. may be hyperparameters that can be freely determined by the user.

[0046] Before the CNN model unit 321 actually predicts the operation status of the target device, machine learning needs to be executed. Hereinafter, the processing during the learning and operation (i.e., during prediction execution) of the CNN model unit 321 will be described.

[0047] (During learning) Figures 7A and 7B show the data provided to the CNN model unit 321 as teacher data. This teacher data includes environmental information E1, which contains temperature, humidity, illuminance, volume, and vibration, for each of times T1 to T8, as well as the operating status of the target device. At each time in Figure 7A, the temperature information is represented by TH1 to TH8, the humidity information by HU1 to HU8, the illuminance information by IL1 to IL8, the volume information by VO1 to VO8, and the vibration information by VI1 to VI8. Also, the operating status is represented by a numerical value between 0 and 1, indicating that the closer the operating status is to 1, the more normal the operation of the target device, and the closer it is to 0, the more abnormal the operation of the target device. In Figures 7A and 7B, as a simplified example, when the target device is operating normally, 1 is set as the operating status, and when an abnormality occurs in the target device, 0 is set as the operating status.

[0048] First, the situation regarding Figure 7A will be described. In this example, the CNN model unit 321 learns a model that predicts output data V21, which includes environmental information V21a and operating information V21b, using input data V11, which is an explanatory variable. Specifically, the CNN model unit 321 learns a model that predicts the detected value of environmental information V21a and the value of operating information V21b at the next time T5 from the detected values of environmental information for the past N (= 4) time slots up to times T1 to T4. At this time, the prediction model generation unit 32 extracts data within the ranges of input data V11, environmental information V21a, and operating information V21b from the data shown in the table of Figure 7A as a set of variables and uses it as the teacher data for the CNN model unit 321. The CNN model unit 321 uses this teacher data and, for example, uses the sum of squared errors as a loss function for the predicted target variable and the target variable in the actual teacher data. As the regularization term for each parameter of the data, an L1 norm or an L2 norm may be used. For example, the L1 norm is preferable for use in feature selection in the model. However, the loss function and the regularization term are not limited to those exemplified above. For example, as the loss function, other types of loss functions such as absolute value error and Huber loss may be used.

[0049] Next, the situation regarding FIG. 7B will be described. In this example, the CNN model unit 321 learns a model that predicts output data V22 including environmental information V22a and operation information V22b using input data V12 which is an explanatory variable. The input data V12 and the output data V22 are shifted by one time slot from the input data V11 and the output data V21. Specifically, the CNN model unit 321 learns a model that predicts the detected value of the environmental information V22a and the value of the operation information V22b at the next time T6 from the detected values of the environmental information in the past N (= 4) time slots from time T2 to T5. At this time, the prediction model generation unit 32 extracts data within the ranges of the input data V12, the environmental information V22a, and the operation information V22b from the data shown in the table of FIG. 7B as a set of variables and uses them as the teacher data for the CNN model unit 321. The CNN model unit 321 uses this teacher data and applies a loss function to the predicted target variable and the target variable in the actual teacher data. The details of this process are as described in the explanation regarding FIG. 7A.

[0050] Hereinafter, the prediction model generation unit 32 makes settings to shift each time slot of the input data V11 and the output data V21 by one time slot in order. The CNN model unit 321 performs the same processing as described above using each of the input data V11 and the output data V21 obtained by the shift as teacher data. By executing this process over the entire time slot range of the teacher data, the prediction model generation unit 32 updates the weights of the model in the CNN model unit 321 and improves the accuracy of the prediction model.

[0051] (During operation) FIG. 7C shows the data given to the CNN model unit 321 during operation. This teacher data includes environmental information E1 including temperature, humidity, illuminance, volume, and vibration for each of times T11 to T14, and the operating status of the target device. Note that time T14 is the current time, and time T15 is a future time to be predicted. At each time in FIG. 7C, the temperature information is represented by TH11 to TH14, the humidity information is represented by HU11 to HU14, the illuminance information is represented by IL11 to IL14, the volume information is represented by VO11 to VO14, and the vibration information is represented by VI11 to VI14. Also, the operating status is represented by a numerical value between 0 and 1, similar to FIGS. 7A and 7B. The prediction unit 33 extracts the input data V13 from the data shown in the table of FIG. 7C and uses it as the input data for the CNN model unit 321. Here, the input data V13 is the detected values of the environmental information in N (= 4) time slots from time T11 to T14.

[0052] FIG. 8 is a flowchart showing an example of a typical process of the prediction device 30. First, the acquisition unit 31 of the prediction device 30 acquires the operating status of the target device as operation information and the environmental information around the target device as teacher data (step S31). Next, the prediction model generation unit 32 generates a prediction model using the teacher data (step S32). For example, the prediction model generation unit 32 generates a prediction model by training the above-described CNN model unit 321. This is the learning step of the prediction model so far. Then, the prediction unit 33 predicts the future operating status of the target device by inputting the environmental information (for example, including the current environmental information) newly acquired by the acquisition unit 31 into the generated prediction model (step S33).

[0053] In the above example, by inputting the input data V13, which is an explanatory variable, into the CNN model unit 321, the CNN model unit 321 outputs output data V23 (the prediction target) including environmental information V23a and operation information V23b. The environmental information V23a is the detected value of the environmental information at the next time T15, and the operation information V23b is the value of the vibration status at time T15. The prediction device 30 predicts the future environmental information and operation information as described above.

[0054] Note that the prediction device 30 can execute, for example, the following processing according to the prediction result. For example, when the operation information V23b is 0, the prediction device 30 may notify the user of a warning that "an abnormality is occurring in the target device" via a notification unit capable of image display, voice, or other notifications. Also, when the operation information V23b is not 0 but is less than a predetermined threshold X1 (X1 is 0 or more and less than 1), the prediction device 30 may notify the user of a warning that "there is a sign of abnormality in the target device" via a notification unit capable of image display, voice, or other notifications.

[0055] In addition, after predicting the environmental information V23a, the prediction device 30 compares the predicted value of the environmental information V23a with the detected value of the actual environmental information V23a at the timing when the acquisition unit 31 acquires the detected value of the actual environmental information V23a, and may calculate the difference for each. As an example, when at least one of the difference values of the five types of environmental information is greater than a predetermined threshold X2, it is assumed that a rapid change has occurred in the environment. In that case, the prediction device 30 may notify the user of a warning that "there is a sign of abnormality in the target device" via the notification unit.

[0056] As described above, the prediction device 30 sets a plurality of sets of variables defined by shifting the time slots one by one in order for the explanatory variables and the objective variables, and uses these plurality of sets of variables as teacher data to generate a prediction model (CNN model unit 321) of the operation status. Then, using the generated prediction model and the environmental information V13 newly acquired by the acquisition unit 31, the operation status V23b of the target device is predicted. By this processing, the prediction device 30 can accurately predict the operation status V23b of the target device.

[0057] In order for the prediction device 30 to accurately generate the prediction model of the operating status, it is preferable to accurately obtain information on "under what circumstances the target device has gone down". Therefore, when the prediction device 30 is applied to the monitoring server 20 shown in (1B), the prediction device 30 can obtain the environmental information when the target device has gone down from the peripheral device 10, so that the prediction device 30 can accurately generate the prediction model. Therefore, the prediction device 30 can be operated more effectively.

[0058] In particular, when the target device is a computer provided in the factory, it is expected that the target device will repeatedly execute some processing along with product manufacturing and the like. Along with this, it is predicted that the fluctuation of the value of the environmental information will become periodic. CNN is suitable for detecting such periodic fluctuations in data. The prediction device 30 can monitor such periodic processing and, as described above, can notify the factory worker of the abnormality when an abnormality of the target device is detected. The periodicity of the data is, for example, in units of several tens of seconds to several hours, and the prediction device 30 may detect a sign of an abnormality of the target device by using the latest data including such periodicity as teacher data.

[0059] In addition, the prediction device 30 may include environmental information at a timing immediately after a predetermined time period as a target variable to be predicted. Thereby, since the prediction device 30 can determine the possibility that an abnormality has occurred in the environment as described above, the possibility of detecting an abnormality of the target device can be increased. However, the prediction device 30 may include operation information at a timing immediately after a predetermined time period as a target variable to be predicted, but may not include environmental information.

[0060] (1D) FIG. 9 is a block diagram showing an example of the determination device. The determination device 40 monitors a plurality of target devices and includes a receiving unit (transmitter) 41, an operation information acquisition unit 42, a storage unit (storage) 43, a state determination unit 44, and an instruction output unit 45. Hereinafter, each of these components will be described.

[0061] The receiving unit 41 receives environmental information around a plurality of devices from a plurality of peripheral devices provided corresponding to each of the plurality of target devices. Note that the peripheral device may be the peripheral device 10 shown in (1A), or may be a known environmental sensor without a rechargeable battery. Further, the peripheral device may transmit the environmental information wirelessly or by wire.

[0062] Each peripheral device transmits identification information for identifying itself together with the environmental information. The determination device 40 determines from which peripheral device the environmental information has been transmitted (that is, for which target device the environmental information has been transmitted) by comparing with the identification information of each peripheral device stored in advance inside.

[0063] The operation information acquisition unit 42 acquires the operation status of each of the plurality of devices as operation information wirelessly or by wire. The operation information acquisition unit 22 may acquire this operation information from, for example, a target device, or may acquire this operation information from another device such as the peripheral device 10.

[0064] The storage unit 43 stores, in association with each other, a first device in a similar environment among the plurality of target devices and a second device different from the first device. Note that "similar environment" may mean that, for example, in the environments around the first device and the second device, among the temperature, humidity, acceleration, vibration, sound, atmospheric pressure, wind, illuminance, ultraviolet light, content of a certain substance in the air such as VOC, the difference regarding at least one detection value detected by the peripheral device is equal to or less than a predetermined threshold value.

[0065] As another example of "similar environment", when a plurality of target devices are provided at a location, it may mean that among the plurality of target devices, the first device and the second device are closest to each other. As still another example, when a plurality of target devices are provided in a factory, it may mean that the first device and the second device perform the same process treatment on the line. Even in such a case, it is assumed that the peripheral environments of the first device and the second device are similar.

[0066] The state determination unit 44 determines the presence or absence of an abnormality in the first device using the operation information acquired by the operation information acquisition unit 42 and the environmental information acquired by the reception unit 41. For example, the state determination unit 44 may determine the presence or absence of an abnormality by determining whether the target device is currently operating normally using the operation information and the environmental information. In another example, the state determination unit 44 predicts whether there is a sign of an abnormality (that is, whether an abnormality is likely to occur in the future during operation) using the operation information and the environmental information, and may determine that there is an abnormality when there is a sign of an abnormality. For example, as shown in (1C), the state determination unit 23 may make this prediction by generating a model using the environmental information and the operation information as teacher data.

[0067] When the state determination unit 44 determines that the first device has an abnormality, the instruction output unit 45 outputs at least one of the following instructions. That is, it outputs an instruction to increase the frequency of transmitting environmental information to the peripheral device provided corresponding to the second device, and an instruction to stop the processing of the second device. Note that the stop of the processing may mean that the second device does not execute the processing unless instructed by the operator, or may mean that the second device stops the processing for a predetermined period and resumes the processing after the predetermined period has elapsed. The instruction output unit 45 refers to the storage unit 43 and recognizes that the second device is the target that requires attention other than the first device by grasping that the second device is associated with the first device determined by the state determination unit 44.

[0068] Note that the instruction output unit 45 may execute at least one of outputting an instruction to increase the frequency of transmitting environmental information to the peripheral device provided corresponding to the first device and outputting an instruction to stop the processing of the first device in parallel with the above processing. Also, the destinations of these devices or peripheral devices that are the output destinations of the instructions may be stored in the storage unit 43, for example. The instruction output unit 45 can output an instruction by referring to this information.

[0069] FIG. 10 is a flowchart showing an example of typical processing of the determination device 40. First, a reception unit 41 of the determination device 40 receives environmental information of a plurality of device peripheries from a plurality of peripheral devices (step S41). Further, an operation information acquisition unit 42 acquires the operation status of each of the plurality of devices as operation information (step S42). Note that step S42 may be executed before or after step S41, or may be executed at the same timing as step S41.

[0070] A state determination unit 44 determines the presence or absence of an abnormality in the first device using the operation information acquired by the operation information acquisition unit 42 and the environmental information acquired by the reception unit 41 (step S43). If there is an abnormality in the first device (Yes in step S43), an instruction output unit 45 outputs the instruction described above to the second device (step S44). On the other hand, if there is no abnormality in the first device (No in step S43), the instruction output unit 45 does not output an instruction to the second device.

[0071] As described above, the first device and the second device are in a similar environment. Therefore, if an abnormality is determined in the first device, it is conceivable that an abnormality has occurred or may occur in the future in the second device, which is in a similar environment. Therefore, the instruction output unit 45 can execute processing corresponding to that possibility. For example, by the instruction output unit 45 outputting an instruction to increase the frequency of transmitting environmental information to a peripheral device provided corresponding to the second device, the peripheral device increases the frequency of transmitting environmental information. Thereby, the determination device 40 can more reliably acquire data related to an abnormality around the second device. Further, by the instruction output unit 45 outputting an instruction to stop the processing to the second device, it is possible to suppress a defect in the processing executed by the second device when an abnormality has occurred in the second device, or to suppress an actual occurrence of an abnormality when a sign of an abnormality has occurred in the second device.

[0072] (1E) FIG. 11 is a block diagram showing an example of a determination device. The determination device 50 monitors a target device and includes an acquisition unit 51, a power spectrum calculation unit 52, a similarity calculation unit 53, and a determination unit 54. Each of these components will be described below.

[0073] The acquisition unit 51 acquires the state of one target device as state information regarding at least one of sound and vibration. The target device has components that emit relatively loud sounds, such as, for example, an HDD (Hard Disk Drive) or a fan, and the determination device 50 can be used to detect abnormalities in those components. The acquisition unit 51 may acquire the operating status from the target device, or may acquire it from another device, such as a peripheral device provided corresponding to the target device and having a sound pressure sensor, a vibration sensor, or the like. An example of such a peripheral device is the peripheral device 10 described in (1A).

[0074] The power spectrum calculation unit 52 calculates the power spectrum of the state information acquired by the acquisition unit 51 at each of a plurality of different timings of 3 or more. This timing may be at an arbitrary time interval. For example, the timing may be a relatively long-term interval such as several weeks, 1 to several months, or several years. As will be described later, the determination device 50 may determine not only abnormalities in the target device but also aging degradation. Thus, the timing may also be a short-term time interval such as several tens of seconds to several hours.

[0075] The similarity calculation unit 53 calculates at least the similarity between the power spectrum at the first timing and the power spectrum at the second timing, and the similarity between the power spectrum at the first timing and the power spectrum at the third timing. The first timing may be, for example, the most recent timing among a plurality of timings, but may also be other timings. Further, the similarity calculation unit 53 may calculate the similarity between the power spectrum at the first timing and the power spectra at all timings other than the first timing. The similarity calculation unit 53 may set the value of the similarity between the power spectra at the first timing (for example, set to 1), and use it for comparison with each similarity calculated as described above.

[0076] The determination unit 54 uses the plurality of similarities calculated by the similarity calculation unit 53 to determine the operating state (especially the deterioration state) of the target device. For example, the determination unit 54 may determine that a problem has occurred in the target device when the difference between the two is equal to or greater than a predetermined threshold when comparing different similarities (that is, similarities regarding power spectra at different timings). Note that the problem includes not only a failure event such that the device cannot execute a desired process, but also a case where the device can execute a desired process, but the sound or vibration in the target device is different from the normal case (for example, the initial state). Other examples of determination will be described later in (2B) of Embodiment 2.

[0077] Each unit of the determination device 50 may execute the above processing for each of a plurality of target devices.

[0078] FIG. 11 is a flowchart showing an example of typical processing of the determination device 50. First, the acquisition unit 51 of the determination device 50 acquires state information regarding at least one of sound and vibration (step S51). The power spectrum calculation unit 52 calculates the power spectrum of the state information at each of three or more different timings (step S52).

[0079] The similarity calculation unit 53 calculates a plurality of similarities between the power spectra (step S53). The determination unit 54 determines the operating status of the target device using the calculated plurality of similarities (step S54).

[0080] Through the above processing, the determination device 50 can accurately determine the state of the target device (for example, the state of components) based on at least one of the sound and vibration data.

[0081] (1A) to (1E) in the above-described Embodiment 1 can be used in appropriate combinations. In Embodiment 2, such specific examples will be described.

[0082] Embodiment 2 (2A) Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In (2A), an example combining (1B) to (1D) of the above-described Embodiment 1 will be described.

[0083] FIG. 13 is a block diagram showing an example of the monitoring system according to (2A). The monitoring system S2 is provided in a factory and includes a plurality of (three in this example) peripheral devices, namely, USB sensors 100 and one monitoring server 200. The USB sensors 100A, 100B, and 100C are USB peripherals and are connected to the respective target devices, namely, factory computers FC1, FC2, and FC3. Hereinafter, the USB sensors 100A to 100C will be collectively referred to as USB sensors 100, and the factory computers FC1 to FC3 will be collectively referred to as computers FC.

[0084] In FIG. 13, the solid-line arrow from the computer FC to the monitoring server 200 indicates that the computer FC outputs its own operation information to the monitoring server 200. The broken-line arrow from the USB sensor 100 to the monitoring server 200 indicates that the USB sensor 100 wirelessly transmits environmental information to the monitoring server 200.

[0085] FIG. 14 is a block diagram showing an example of the USB sensor according to (2A). The USB sensor 100 includes a connection unit 101, a controller 102, a non-volatile storage area 103, an illuminance sensor 104, a shock / vibration sensor 105, a temperature / humidity sensor 106, a wireless communication module 107, and a rechargeable battery 108. Each part of the USB sensor 100 is controlled by the controller 102. Hereinafter, each component will be described.

[0086] The connection unit 101 is a part corresponding to the power supply / reception unit 11 in (1A). By being inserted into the USB port of the computer FC which is the target device, it connects the computer FC and the peripheral device 10, and receives power supply from the computer FC.

[0087] The controller 102 is composed of a processor. By reading and executing the software module group stored in the non-volatile storage area 103, it causes each part of the USB sensor 100 to execute the processing described later. Also, the controller 102 supplies power to the illuminance sensor 104, the shock / vibration sensor 105, the temperature / humidity sensor 106, and the wireless communication module 107.

[0088] Note that when the controller 102 can receive power supply from the computer FC via the connection unit 101, as shown in FIG. 14, it operates when power is supplied from the computer FC. On the other hand, when it cannot receive power supply from the computer FC, it operates when power is supplied from the rechargeable battery 108.

[0089] In the non-volatile storage area 103, in addition to the software module group executed by the controller 102, the detection values detected by the illuminance sensor 104, the shock / vibration sensor 105, and the temperature / humidity sensor 106 are stored. The data of this detection value can be stored by the controller 102 and can also be read from the controller 102. The non-volatile storage area 103 may be composed of any type of non-volatile storage area, and an SSD (Solid State Drive) is an example thereof.

[0090] The illuminance sensor 104 is a sensor that detects the illuminance around the computer FC, which is the target device, and outputs the detected value to the controller 102. The shock and vibration sensor 105 is a sensor that detects the vibration (especially shock) around the computer FC, and outputs the detected value to the controller 102. The temperature and humidity sensor 106 is a sensor that detects the temperature and humidity around the computer FC, and outputs the detected value to the controller 102. Each of the above sensors corresponds to the environmental sensor 12 in (1A). The controller 102 stores the detected values (environmental information) output by each sensor in the non-volatile storage area 103.

[0091] The wireless communication module 107 corresponds to the transmission unit 13 in (1A). It acquires the environmental information stored in the non-volatile storage area 103 from the controller 102 and wirelessly transmits the environmental information to the monitoring server 200. The wireless communication module 107 can execute communication by, for example, a LPWA (Low Power Wide Area) communication method.

[0092] The controller 102 periodically causes the environmental information stored in the non-volatile storage area 103 to be transmitted to the monitoring server 200. After transmitting the environmental information, the controller 102 stores the environmental information acquired by each sensor in the non-volatile storage area 103 during the period until the next transmission of the environmental information, and causes the stored environmental information to be transmitted when the environmental information is transmitted next. The period for transmitting the environmental information can adopt any period such as 1 minute, 10 minutes, etc. Also, at the time of transmission, the controller 102 transmits the identification information of the USB sensor 100 together with the environmental information.

[0093] The rechargeable battery 108 corresponds to the rechargeable battery 14 of (1A). When the rechargeable battery 108 receives power supply from the computer FC via the connection part 101, as shown in FIG. 14, it is charged by the power supply from the computer FC. On the other hand, when it cannot receive power supply from the computer FC, it supplies the charged power to the controller 102. The controller 102 supplies this power to each of the sensors 104 to 106 and the wireless communication module 107. That is, the rechargeable battery 108 supplies power to the controller 102 so that the environmental information can be acquired by each sensor and transmitted wirelessly for a predetermined period even after the computer FC has an abnormal operation such as an abnormal termination. This predetermined period is a period sufficient for the monitoring server 200 to generate an accurate prediction model based on the environmental information, for example, a period of several minutes.

[0094] FIG. 15 shows the state when the computer FC3 abnormally terminates in the monitoring system S2 in FIG. 13. Since the computer FC3 has abnormally terminated, it cannot transmit its operation information to the monitoring server 200. However, the USB sensor 100C can acquire and transmit the environmental information around the computer FC3 for a predetermined period.

[0095] FIG. 16 is a block diagram showing the state of the USB sensor 100C in FIG. 15. For the above reasons, the USB sensor 100C cannot receive power supply from the computer FC3. However, the rechargeable battery 108 in the USB sensor 100C supplies power to the controller 102, enabling the acquisition and transmission of the environmental information of the USB sensor 100C.

[0096] Note that the controller 102 can also function as a failure determination unit. Specifically, the controller 102 may detect that the power supply from the computer FC has stopped or the power supply has become unstable, and in that case, determine that an abnormality has occurred in the computer FC. Further, the controller 102 may detect that the detected value of the environmental information detected by at least any one of the illuminance sensors 104 to the temperature / humidity sensors 106 has become equal to or greater than a predetermined threshold value, and in that case, determine that there is a sign of abnormality in the computer FC. For example, the case where the shock / vibration sensor 105 detects a shock equal to or greater than a predetermined threshold value (that is, an explosion is detected) corresponds to this. In the above cases, as described in (1A), the controller 102 can transmit the environmental information stored in the non-volatile storage area 103 regardless of the regular timing. For example, the controller 102 can transmit the environmental information at the time when the above-described events are detected.

[0097] Next, the details of the monitoring server 200 will be described. FIG. 17 is a block diagram showing an example of the monitoring server 200. The monitoring server 200 is connected to a database DB connected to the outside thereof, and can input and output information with the database DB. However, the database DB may be provided inside the monitoring server 200. The monitoring server 200 includes a receiving unit 201, a learning unit 202, a model storage unit 203, an inference unit 204, and a notification unit 205. Hereinafter, each of these components will be described.

[0098] The receiving unit 201 corresponds to the acquisition unit 31 in (1C), and receives the environmental information transmitted by the wireless R2 from the USB sensor 100 and the operation information transmitted from the computer FC. Since the USB sensor 100 transmits the environmental information periodically, the receiving unit 201 receives the new environmental information periodically.

[0099] The receiving unit 201 determines which USB sensor 100 the environmental information has been transmitted from (i.e., which computer FC's environmental information has been transmitted) by comparing with the identification information of each USB sensor 100 stored inside the monitoring server 200. Also, the operation information transmitted from the computer FC is attached with identification information for the computer FC to identify itself. The receiving unit 201 determines which computer FC the environmental information has been transmitted from by comparing with the identification information of each computer FC stored inside the monitoring server 200. The receiving unit 201 associates the environmental information and operation information for a certain computer FC identified in this way with the identification information of the identified computer FC and stores them in the database DB as learning data.

[0100] The learning unit 202 corresponds to the prediction model generation unit 32 in (1C). For a certain computer FC, it reads the environmental information and operation information stored in the database DB using the identification information of the computer FC and generates teacher data. Then, using this teacher data, it generates a prediction model for each computer FC. The details of this process are as described in (1C). Note that the process of the learning unit 202 may be performed according to the instruction of an operator who operates the monitoring server 200, or the monitoring server 200 may execute it automatically. For example, for a certain computer FC, after a predetermined period has elapsed since the learning unit 202 generated or updated the previous prediction model, the learning unit 202 may update the prediction model using the data related to the computer FC stored in the database DB after the previous time.

[0101] The model storage unit 203 stores the prediction models of each computer FC generated by the learning unit 202 in association with the identification information of the computer FC. Note that when the learning unit 202 generates an updated version of the prediction model, the prediction model stored in the model storage unit 203 is updated to the one generated by the learning unit 202. Also, the model storage unit 203 may be provided outside instead of inside the monitoring server 200.

[0102] The inference unit 204 corresponds to the prediction unit 33 in (1C). When the reception unit 201 receives new environment information for a certain computer FC, the inference unit 204 acquires from the model storage unit 203 a prediction model associated with the identification information of that computer FC. The inference unit 204 inputs the newly acquired environment information as an explanatory variable into the prediction model to obtain predicted values of future environment information and operation information, which are the target variables. The details of this process are as described in (1C). Then, the inference unit 204 outputs to the notification unit 205 information indicating the presence or absence of an abnormality or sign of an abnormality regarding the computer FC to be inferred. The inference unit 204 executes this process for each corresponding computer FC at the timing when new environment information is received for each USB sensor 100. Further, the inference unit 204 may store the inference result in the database DB in association with data regarding the computer FC to be inferred that has been previously stored in the database DB.

[0103] Based on the information output from the inference unit 204, the notification unit 205 notifies the presence or absence of an abnormality or sign of an abnormality of the computer FC by at least one of display and sound. The notification unit 205 has, for example, a display, a speaker, etc. Further, the notification unit 205 may output an instruction to notify the computer FC for which an abnormality or sign of an abnormality has been determined that there is an abnormality or sign of an abnormality in that computer FC. Furthermore, if necessary, the notification unit 205 may output an instruction to stop the processing of that computer FC.

[0104] In addition, when the receiving unit 201 receives environmental information at irregular timings, the inference unit 204 may determine from which USB sensor 100 and what kind of information was transmitted, and notify the operator of this via the notification unit 205. For example, when the USB sensor 100 transmits environmental information because the impact / vibration sensor 105 has detected a strong impact equal to or greater than a predetermined threshold, the inference unit 204 determines from the environmental information that a strong impact has been detected. Also, when the USB sensor 100 transmits environmental information because the temperature / humidity sensor 106 has detected a high temperature equal to or greater than a predetermined threshold, the inference unit 204 determines from the environmental information that a high temperature has been detected. The inference unit 204 outputs information regarding this determination to the notification unit 205. Based on the information output from the inference unit 204, the notification unit 205 notifies information regarding this determination by at least either display or voice.

[0105] As described above, since the USB sensor 100 is inserted into the USB port of the computer FC, it is normally always powered by the computer FC. Therefore, the USB sensor 100 does not need to use power normally, and can store in the rechargeable battery 108 the power for operating when the computer FC shuts down or the like. Also, even when the computer FC shuts down (especially after the power supply has been interrupted), it can acquire and transmit environmental information.

[0106] The monitoring server 200 can determine whether there is any abnormality or sign of abnormality in the computer FC by aggregating and analyzing the information sent from each USB sensor 100. Also, when an abnormality or sign of abnormality is detected, the monitoring server 200 can execute, via the notification unit 205, the processing necessary for the maintenance of the computer FC.

[0107] Note that the model storage unit 203 may store a plurality of computers FC in a similar environment in an associated manner. The definition of a similar environment is as described in (1D). Hereinafter, two computers FC in a similar environment are referred to as C1 and C2. The inference unit 204 can reduce the learning time by using this information to divert the prediction model of C1 stored in the model storage unit 203 to C2 in a similar environment. That is, the inference unit 204 can execute the determination regarding the abnormality of C2 by inputting the newly acquired environmental information of C2 into the prediction model of C1. Of course, the process with C1 and C2 reversed is also possible.

[0108] Furthermore, in addition to the inference result 1 obtained by inputting the newly acquired environmental information of C1 into the prediction model of C1, the inference unit 204 may derive an inference result 2 obtained by inputting the newly acquired environmental information of C1 into the prediction model of C2. The inference unit 204 can determine whether or not an abnormality or a sign of an abnormality has occurred in C1 by referring to both. For example, even if no abnormality has occurred in the inference result 1, if an abnormality has occurred in the inference result 2, the inference unit 204 can determine that an abnormality has occurred in C1.

[0109] (2B) (2B) describes an example in which (1B) and (1E) are combined. Since the overall configuration of the monitoring system according to (2B) is as described with respect to FIG. 13 of (2A), the description is omitted.

[0110] FIG. 18 is a block diagram showing an example of a USB sensor in (2B). This USB sensor 100 includes a sound sensor 109 instead of the illuminance sensor 104 as compared with the USB sensor 100.

[0111] The sound sensor 109 is a sensor that detects the sound (e.g., sound pressure) around the computer FC which is the target device, and outputs the detection value to the controller 102. The controller 102 stores the detection value of the sound sensor 109 in the non-volatile storage area 103 as environmental information, similar to the detection values output by other sensors. The controller 102 periodically (at regular intervals) transmits this stored environmental information to the monitoring server 200 using the wireless communication module 107. Note that since the configuration of the USB sensor 100 in (2B) other than the sound sensor 109 is the same as that in (2A), the description thereof is omitted.

[0112] Next, the details of the monitoring server 200 in (2B) will be described. FIG. 19 is a block diagram showing an example of the monitoring server 200. The monitoring server 200 is connected to a database DB connected to its external, and is capable of inputting and outputting information with the database DB. However, the database DB may be provided inside the monitoring server 200. The monitoring server 200 includes a receiving unit 211, a power spectrum calculation unit 212, a similarity calculation unit 213, a determination unit 214, and a notification unit 215. Hereinafter, each of these components will be described.

[0113] The receiving unit 211 identifies which computer FC the received environmental information is about by performing the same processing as the receiving unit 201, and stores the environmental information in the database DB as learning data. The details of this processing are as described in (2A). Although not described in detail here, the receiving unit 211 may acquire the operation information from the computer FC and also store the data in the database DB as learning data.

[0114] FIG. 20 is a diagram showing an example of the sound data of the computer FC1 stored in the DB. In the DB, for the computer FC1, the current sound data D0, the sound data D1 one month ago, the sound data D2 two months ago, ··· the sound data DN N months ago are stored. These sound data D are received from the USB sensor 100A. The monitoring server 200 monitors the computer FC1 at one-month intervals using this data.

[0115] The power spectrum calculation unit 212 corresponds to the power spectrum calculation unit 52 in (1E), and Fourier-transforms each sound data D stored in the database DB to calculate the power spectrum (frequency characteristics) related to frequency respectively.

[0116] The similarity calculation unit 213 corresponds to the similarity calculation unit 53 in (1E). Specifically, the similarity calculation unit 213 selects the power spectrum P0 of the current sound data D0 (the power spectrum at the most recent timing) and the power spectrum P1 of the sound data D1 one month ago, and calculates the similarity R1 between the two. Similarly, the similarity calculation unit 213 selects the power spectrum P0 and the power spectrum P2 of the sound data D2 two months ago, and calculates the similarity R2 between the two. In this way, the similarity calculation unit 213 calculates the similarity R between the power spectrum P0 and the power spectrum of any one of the past sound data D for all the past sound data. Finally, the similarity calculation unit 213 selects the power spectrum P0 and the power spectrum PN of the sound data DN N months ago, and calculates the similarity RN between the two. Also, the similarity calculation unit 213 sets the similarity R0 between the power spectra P0 to 1 for the comparison of similarities.

[0117] At this time, the similarity calculation unit 213 calculates the similarity by calculating the cross-correlation between the power spectra P after normalizing the distribution of each power spectrum P so that the average is 0 and the variance is 1. However, the normalization method is not limited to this. The similarity calculation unit 213 outputs the similarity between the power spectra P at different timings as a sequence of size N to the determination unit 214. Also, the similarity calculation unit 213 outputs to the determination unit 214 that the similarity R0 between the power spectra P0 is 1.

[0118] The determination unit 214 corresponds to the determination unit 54 in (1E), and determines the operating status of the computer FC1 using the similarity calculated by the similarity calculation unit 213. Specifically, the determination unit 214 plots the calculated plurality of similarities R in chronological order from R0 to RN, performs linear fitting on the plotted graph, and determines whether the graph approximates a straight line. Whether the graph approximates a straight line can be determined, for example, by whether the difference in similarity over a certain one-month period is greater than a predetermined threshold from the slope of the graph in other periods. If the graph does not approximate a straight line, the determination unit 214 can identify the location of the inflection point in the graph and estimate the time when a problem occurred in the computer FC1 based on the location of the inflection point. In this way, the determination unit 214 determines that a problem has occurred in the computer FC1.

[0119] (i) For example, the determination unit 214 determines whether the difference DI between the similarity R0 and the similarity R1 (that is, the slope of the graph in the most recent one-month period) is greater than a predetermined threshold from the slope of the graph in other periods. If DI is greater than a predetermined threshold from the slope of the graph in other periods, since the slope (rate of change of similarity) of the graph has changed rapidly in the most recent one-month period, it is determined that the plotted graph does not approximate a straight line. Therefore, the determination unit 214 identifies that there is an inflection point in the graph in the most recent one-month period and estimates that a problem has occurred in the computer FC1 in the most recent one-month period. Note that the determination unit 214 can estimate that a problem has occurred not only in the most recent one-month period but also in other periods.

[0120] When the graph is approximated by a straight line, the determination unit 214 determines whether the slope of the entire graph is greater than or equal to a predetermined threshold value. (ii) When the slope of the graph is greater than or equal to the predetermined threshold value, the determination unit 214 estimates that aging degradation has occurred in the computer FC1. This is because in this case, it can be interpreted that the sound data changes at a substantially constant rate over time. (iii) When the slope of the graph is less than the predetermined threshold value, the determination unit 214 estimates that no abnormality has occurred in the computer FC1. This is because in this case, it can be interpreted that the sound data has hardly changed between the present and the past.

[0121] FIG. 21 is an example of a graph in which the similarity degrees R0 to RN are plotted. The graphs of (i) to (iii) in FIG. 21 are examples of the shapes of the graphs corresponding to the above (i) to (iii). In this way, the determination unit 214 can accurately determine the state of the computer FC1.

[0122] The determination unit 214 outputs the above determination result to the notification unit 215. Note that when the determination unit 214 determines at least one of the occurrence of a malfunction in the computer FC1 and the occurrence of aging degradation in the computer FC1, it may generate notification content prompting the operator to inspect or replace the computer FC1 and output it to the notification unit 215 together with the determination result.

[0123] The notification unit 215 has the same configuration as the notification unit 205 in (2A), and notifies the operator of the determination result regarding the computer FC by at least one of display and voice. For example, when the determination unit 214 identifies the time when a malfunction occurred in the computer FC1 in the most recent one month, the notification unit 215 displays the content of "A rapid change has been observed in the drive sound of the computer FC1 in the most recent one month. Since there may be a malfunction, it is recommended to promptly inspect or replace the computer FC1." Also, when the determination unit 214 determines that aging deterioration has occurred in the computer FC1, the notification unit 215 displays the content of "No malfunction is recognized in the computer FC1, but there may be aging deterioration. It is recommended to inspect the computer FC1." When the determination unit 214 determines that no abnormality has occurred in the computer FC1, it displays the content of "No abnormality was recognized in the computer FC1."

[0124] The above processing is executed for each of the plurality of computers FC. Also, although sound data was used as an example, the same processing can be executed for data on vibration (for example, impact).

[0125] FIG. 22A is a flowchart showing an example of typical processing of the monitoring server 200. First, the reception unit 211 of the monitoring server 200 receives sound and vibration data, which are environmental information, from each USB sensor 100 (step S61). The received sound and vibration data are stored in the database DB.

[0126] The power spectrum calculation unit 212 performs Fourier transform on each sound data D stored in the database DB to calculate the power spectrum respectively (step S62). The similarity calculation unit 213 calculates the similarity R between the power spectra using each power spectrum (step S63).

[0127] The determination unit 214 plots the calculated multiple similarities R in chronological order, and determines whether the plotted graph approximates a straight line, that is, whether it is linear (step S64). If the graph is not linear (No in step S64), the determination unit 214 determines that a malfunction has occurred in the computer FC1 and the time when the malfunction occurred by identifying the location of the inflection point in the graph (step S65).

[0128] In the determination of step S64, if the graph is linear (Yes in step S64), the determination unit 214 determines whether the slope of the entire graph is equal to or greater than a predetermined threshold (step S66). If the slope of the graph is equal to or greater than the predetermined threshold (Yes in step S66), the determination unit 214 determines that aging deterioration has occurred in the computer FC1 (step S67). On the other hand, if the slope of the graph is less than the predetermined threshold (No in step S66), the determination unit 214 determines that no abnormality has occurred in the computer FC1 (step S68). Also, in each of steps S65, S67, and S68, the determination unit 214 outputs the notification content based on the determination result to the notification unit 215.

[0129] As described above, the monitoring server 200 can detect changes in the states of the HDD and the fan by examining fluctuations in the values of sound and vibration at relatively long intervals.

[0130] Also, in (2B), the processing of the monitoring server shown in (1C) may be further combined. Thereby, the monitoring server can determine not only the state changes of the computer FC over a long period but also the presence or absence of abnormalities in the computer FC over a short period.

[0131] Computer monitoring systems are known to be of types that operate in cooperation with an OS (Operating System) or those in which a separate power supply or OS is incorporated. Also, in order to detect abnormalities in a computer to be monitored, it is preferable to detect various types of information before and after an abnormality occurs in the computer. However, the former monitoring system requires the computer to be monitored to operate normally to a certain extent, and there is a possibility that information at the moment the computer shuts down cannot be obtained. The latter monitoring system uses a separate power supply or system after the computer shuts down to investigate failure information and back up data. Therefore, similar to the former, there is a possibility that information at the moment of system shutdown of the computer cannot be obtained. Also, in the case of the latter, since it is necessary to prepare dedicated hardware or a separate power supply system, basically, it is not a system that can be retrofitted to a computer. Therefore, it has not been possible to easily add a status monitoring function to a computer that does not have a status monitoring function.

[0132] On the other hand, the computer monitoring systems exemplified in (2A) and (2B) acquire computer information in systems that require high reliability, such as factory facilities. By utilizing the acquired information, the monitoring system can improve the availability of the computer by grasping the situation at the time of computer failure or detecting signs of computer failure. For example, since the monitoring system can accurately acquire information at the moment of system shutdown of the computer, it is possible to grasp in detail the information at the time of failure and determine signs of computer failure with high accuracy. Also, for a computer that operates continuously for 24 hours in a factory, leak-free monitoring can be realized.

[0133] Note that the present disclosure is not limited to the above-described embodiments, and can be appropriately changed without departing from the gist. For example, in (1E) and (2B), the timing for calculating the power spectrum does not have to be at the same interval.

[0134] (2B), the determination unit 214 may determine the magnitude relationship between the slope of the entire graph and two or more different threshold values when the graph approximates a straight line. For example, when the slope of the entire graph of the computer FC1 is equal to or greater than a large threshold value Th1, the determination unit 214 determines that the aging deterioration is progressing rapidly. The determination unit 214 generates, as notification content, a point that prompt inspection or replacement of the computer FC1 is considered necessary for the operator, and outputs it to the notification unit 215.

[0135] On the other hand, when the slope of the entire graph of the computer FC1 is less than the large threshold value Th1 but equal to or greater than a small threshold value Th2 (Th1 > Th2), the determination unit 214 determines that the aging deterioration is progressing slowly. The determination unit 214 generates, as notification content, a point that recommends regular inspection of the computer FC1 to the operator, and outputs it to the notification unit 215. Further, when the slope of the entire graph of the computer FC1 is less than the small threshold value Th2, the determination unit 214 determines that no aging deterioration has occurred. This is as described in step S68 of FIG. 22B. In this way, the determination unit 214 may determine different states of the computer FC1 based on the magnitude relationship with two or more different threshold values, and accordingly change the content notified to the operator by the notification unit 215.

[0136] (1D) or (2A), the information of a plurality of devices stored as being in a similar environment among the plurality of target devices (computers) can be updated by the operator and the determination device 40 (or the monitoring server 200). For example, the determination device 40 in (1D) may determine target devices in a similar environment using data on the environmental information and operation information of the target devices.

[0137] Specifically, the state determination unit 44 of the determination device 40 calculates the similarity of the time-series data of environmental information (including at least one of the various parameters described above, such as temperature, humidity, etc.) for a predetermined period regarding the target devices. Then, when the similarity is equal to or greater than a predetermined threshold, the state determination unit 44 determines that the two target devices are in a similar environment. When the similarity is less than the predetermined threshold, the state determination unit 44 determines that the two target devices are not in a similar environment.

[0138] The state determination unit 44 may further calculate the similarity of the time-series data of the operation information for a predetermined period and reflect the similarity in the determination. For example, when it is determined in the comparison of the similarity regarding the above environmental information that the two target devices are in a similar environment, if the similarity of the time-series data of the operation information in the two devices is less than a predetermined value, the state determination unit 44 may determine that the two target devices are not in a similar environment. If the similarity of the time-series data of the operation information in the two devices is equal to or greater than the predetermined value, the state determination unit 44 determines that the two target devices are not in a similar environment.

[0139] Then, the state determination unit 44 stores the information of the target devices determined to be in a similar environment in the storage unit 43 and does not store (or deletes the information from the storage unit 43) the information of the target devices determined not to be in a similar environment. Note that the state determination unit 44 applies known techniques such as distance calculation (e.g., Euclidean distance) and clustering (k-means method, hierarchical type, etc.) to calculate the similarity of the above environmental information and operation information.

[0140] Also, the controller 102 of the USB sensor 100 shown in (2A) or (2B) may charge the rechargeable battery 108 at a first charging speed until it reaches a certain charging value (e.g., about 80% of full charge), and then set the charging speed to a second charging speed slower than before, or stop charging. Thereby, the controller 102 can extend the life of the rechargeable battery 108.

[0141] When it is determined that there is an abnormality or a sign of abnormality in a certain computer FC as shown in Embodiments 1 and 2, the monitoring server 200 may send an instruction to change the charging method of the rechargeable battery 108 to the USB sensor 100 connected to the computer FC. For example, even when the rechargeable battery 108 is charged to a certain charging value, the monitoring server 200 may set the charging speed to the first charging speed without changing the charging speed (or set it to a charging speed faster than the second charging speed), so that the rechargeable battery 108 can achieve 100% charging earlier. The monitoring server 200 can execute such processing, for example, in the following cases. · When it is determined in step S33 of (1C) that there is an abnormality (or a sign of abnormality) · When it is determined in step S43 of (1D) that there is an abnormality · In (2A), when it is determined that an abnormality has occurred in the target device C1 by using the prediction model of the target device C2 similar to the target device C1 · When it is determined in step S65 of (2B) that a malfunction has occurred · When it is determined in step S67 of (2B) that aging deterioration has occurred, and the slope of the entire graph is greater than the above-mentioned threshold Th1 for determining aging deterioration and it is determined that the aging deterioration is progressing rapidly In these cases, it is considered that there is a relatively high possibility that an abnormal termination (for example, a system shutdown) will occur in the target device in the near future. Therefore, it is considered better to charge the rechargeable battery 108 closer to 100% so that the environmental information at the time of abnormal termination can be reliably obtained.

[0142] In the embodiments shown above, this disclosure has been described as a hardware configuration, but this disclosure is not limited thereto. This disclosure can also be realized by causing a processor in a computer to execute a computer program for the processing (steps) of the device (any one of the peripheral device, the monitoring server, the determination device, and the prediction device) described in the above embodiments.

[0143] FIG. 23 is a block diagram showing a hardware configuration example of an information processing apparatus (signal processing apparatus) in which the processing of each of the embodiments described above is executed. Referring to FIG. 23, this information processing apparatus 90 includes a signal processing circuit 91, a processor 92, and a memory 93.

[0144] The signal processing circuit 91 is a circuit for processing signals in accordance with the control of the processor 92. Note that the signal processing circuit 91 may include a communication circuit that receives signals from a transmission device.

[0145] The processor 92 reads and executes software (computer program) from the memory 93 to perform the processing of the apparatus described in the above embodiments. As an example of the processor 92, one of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an FPGA (Field-Programmable Gate Array), a DSP (Demand-Side Platform), and an ASIC (Application Specific Integrated Circuit) may be used, or a plurality of them may be used in parallel.

[0146] The memory 93 is composed of a volatile memory, a non-volatile memory, or a combination thereof. The memory 93 is not limited to one, and a plurality of them may be provided. Note that the volatile memory may be, for example, a RAM (Random Access Memory) such as a DRAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory). The non-volatile memory may be, for example, a ROM (Random Only Memory) such as a PROM (Programmable Random Only Memory) or an EPROM (Erasable Programmable Read Only Memory), or an SSD (Solid State Drive).

[0147] Memory 93 is used to store one or more instructions. Here, the one or more instructions are stored in memory 93 as a group of software modules. The processor 92 can perform the processing described in the above embodiments by reading out and executing these groups of software modules from memory 93.

[0148] Note that in addition to the one provided outside the processor 92, the memory 93 may include one built into the processor 92. Also, the memory 93 may include storage located away from the processor that constitutes the processor 92. In this case, the processor 92 can access the memory 93 via an I / O (Input / Output) interface.

[0149] As described above, the one or more processors included in each device in the above embodiments execute one or more programs including a group of instructions for causing a computer to perform the algorithms described with reference to the drawings. By this processing, the signal processing methods described in each embodiment can be realized.

[0150] The program can be stored using various types of non-transitory computer readable media and supplied to a computer. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer readable media include magnetic recording media (such as flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (such as magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, semiconductor memories (such as mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)). Also, the program may be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. Transitory computer readable media can supply the program to the computer via wired communication channels such as electric wires and optical fibers, or wireless communication channels.

[0151] Some or all of the above embodiments can also be described as follows, but are not limited thereto. (Appendix 1) A power receiving unit that receives power supply from a device to be monitored, An environmental sensor that acquires environmental information around the device, A transmission unit that transmits the environmental information, A rechargeable battery that is charged by receiving power supply from the device via the power receiving unit, and When the environmental sensor and the transmission unit cannot receive power supply via the power receiving unit, they operate with the discharge power from the rechargeable battery. Peripheral device. (Appendix 2) Further comprising a storage unit that stores the environmental information, When the power supply unit is receiving power supply via the power receiving and supplying unit, the transmission unit transmits the environmental information stored in the storage unit at regular intervals. When it becomes impossible to receive power supply via the power receiving and supplying unit, the transmission unit transmits the environmental information stored in the storage unit regardless of the regular timing. The peripheral device according to Supplementary Note 1. (Supplementary Note 3) A storage unit that stores the environmental information; A determination unit that determines whether or not a detection value of the environmental information detected by the environmental sensor is equal to or greater than a predetermined threshold value; and further includes When the determination unit determines that the detection value is less than a predetermined threshold value, the transmission unit transmits the environmental information stored in the storage unit at regular intervals. When the determination unit determines that the detection value is equal to or greater than a predetermined threshold value, the transmission unit transmits the environmental information stored in the storage unit regardless of the regular timing. The peripheral device according to Supplementary Note 1. (Supplementary Note 4) The transmission unit transmits the environmental information to a monitoring server, When the peripheral device receives an instruction from the monitoring server to increase the frequency of transmitting the environmental information, the peripheral device controls the transmission unit to increase the frequency of transmitting the environmental information. The peripheral device according to any one of Supplementary Notes 1 to 3. (Supplementary Note 5) The peripheral device is a USB peripheral. The peripheral device according to any one of Supplementary Notes 1 to 4. (Supplementary Note 6) A peripheral device provided corresponding to a device to be monitored; A monitoring server that communicates with the peripheral device; and includes The peripheral device A power receiving and supplying unit that receives power supply from the device; An environmental sensor that acquires environmental information around the device; A transmission unit that transmits the environmental information; A rechargeable battery that is charged by receiving power supply from the device via the power receiving and supplying unit; and has When the environmental sensor and the transmission unit can no longer receive power supply via the power receiving and supplying unit, they operate with the discharge power from the rechargeable battery. The monitoring server has a receiving unit that receives the environmental information from the peripheral device, an operation information acquisition unit that acquires the operation status of the device as operation information, and a state determination unit that determines the state of the device using the operation information and the environmental information. A monitoring system. (Appendix 7) The state determination unit sets at least the explanatory variable including the environmental information in a predetermined time region and at least the objective variable including the operation information at the timing immediately after the predetermined time region as a set of variables, and uses, as teacher data, a plurality of sets of the variables defined by shifting the time slots in the set of variables in order, to generate a prediction model for the operation status, a prediction model generation unit, and a prediction unit that predicts the operation status using the prediction model and the newly acquired environmental information. The monitoring system according to Appendix 6. (Appendix 8) The monitoring system includes a plurality of peripheral devices corresponding to each of the plurality of devices to be monitored. The monitoring server has a storage unit that associates and stores a first device and a second device in a similar environment among the plurality of devices, and an instruction output unit that, when the state determination unit determines an abnormality for the first device, outputs at least one of an instruction to increase the frequency at which the transmission unit transmits the environmental information to the peripheral device corresponding to the second device and an instruction to stop the processing for the second device. The monitoring system according to Appendix 6. (Appendix 9) A charging step of charging the rechargeable battery by receiving power supply from the device to be monitored, An environmental information acquisition step of acquiring environmental information around the device using the discharge power from the rechargeable battery when power supply from the device becomes unavailable; A transmission step of transmitting the environmental information using the discharge power from the rechargeable battery; A monitoring method executed by a peripheral device. (Appendix 10) A charging step of charging a rechargeable battery by receiving power supply from a device to be monitored; An environmental information acquisition step of acquiring environmental information around the device using the discharge power from the rechargeable battery when power supply from the device becomes unavailable; A transmission step of transmitting the environmental information using the discharge power from the rechargeable battery; A program that causes a peripheral device to execute as a monitoring method. (Appendix 11) The target variable in the set of variables further includes the environmental information at a timing immediately after the predetermined time period; The prediction model generation unit generates a prediction model of the operating status and the environmental information using the sets of the plurality of variables as teacher data; The prediction unit predicts the operating status of the device using the prediction model and the newly acquired environmental information. The monitoring system according to Appendix 7. (Appendix 12) The environmental information includes at least one piece of operation data of sound and vibration indicating the operating status of the device; The state determination unit A power spectrum calculation unit that calculates power spectra of the operation data at three or more different timings respectively; A similarity calculation unit that calculates at least the similarity between the power spectrum at the first timing and the power spectrum at the second timing, and the similarity between the power spectrum at the first timing and the power spectrum at the third timing; A degradation state determination unit that determines the degradation state of the device using the plurality of similarities calculated by the similarity calculation unit. The monitoring system according to Supplementary Note 6. (Supplementary Note 13) Peripheral devices provided corresponding to the device to be monitored A charging step of charging a rechargeable battery by receiving power supply from the device; An environmental information acquisition step of acquiring environmental information around the device using the discharge power from the rechargeable battery when the power supply cannot be received; A transmission step of transmitting the environmental information to a monitoring server using the discharge power from the rechargeable battery, and The monitoring server A reception step of receiving the environmental information from the peripheral device; An operation information acquisition step of acquiring the operation status of the device as operation information; A state determination step of determining the state of the device using the operation information and the environmental information. Monitoring method. (Supplementary Note 14) An acquisition unit that acquires the operation status of the device to be monitored as operation information and acquires environmental information around the device; A prediction model generation unit that generates a prediction model of the operation status using, as teacher data, a plurality of sets of variables defined by setting, as a set of variables, at least explanatory variables including the environmental information in a predetermined time region and at least target variables including the operation information at a timing immediately after the predetermined time region, and shifting the time slots in the set of variables in order; A prediction unit that predicts the operation status of the device using the prediction model and the newly acquired environmental information. Prediction device. (Supplementary Note 15) The target variable in the set of variables further includes the environmental information at a timing immediately after the predetermined time region. The prediction model generation unit generates a prediction model of the operating status and the environmental information, using the set of the plurality of variables as teacher data. The prediction unit predicts the operating status of the device, using the prediction model and the newly acquired environmental information. The prediction device according to Supplementary Note 14. (Supplementary Note 16) A receiving unit that receives environmental information around the plurality of devices from a plurality of peripheral devices respectively provided corresponding to each of the plurality of devices to be monitored; An operation information acquisition unit that acquires the operating status of the plurality of devices as operation information; A storage unit that stores by associating a first device and a second device that are in a similar environment among the plurality of devices; A state determination unit that determines the presence or absence of an abnormality in the first device, using the operation information and the environmental information; When the state determination unit determines that the first device has an abnormality, outputting an instruction to increase the frequency of transmitting the environmental information to the peripheral device provided corresponding to the second device, and outputting an instruction to stop the process for the second device, and an instruction output unit that executes at least any one of them. Determination device. (Supplementary Note 17) An acquisition unit that acquires the operating status of the device to be monitored as operation information related to at least any one of sound and vibration; A power spectrum calculation unit that calculates the power spectrum of the operation information at each of a plurality of different timings of 3 or more; A similarity calculation unit that calculates at least the similarity between the power spectrum at the first timing and the power spectrum at the second timing, and the similarity between the power spectrum at the first timing and the power spectrum at the third timing; A determination unit that determines the operating status of the device, using the plurality of similarities calculated by the similarity calculation unit. Determination device. (Supplementary Note 18) The determination unit determines whether a graph obtained by plotting the plurality of similarity degrees in chronological order approximates a straight line. If the graph does not approximate a straight line, the determination unit identifies a location of an inflection point in the graph and estimates a time when a defect occurred in the device based on the location of the inflection point. The determination device according to Supplementary Note 17. (Supplementary Note 19) If the graph approximates a straight line, the determination unit determines whether an inclination of the entire graph is equal to or greater than a predetermined threshold value. If the inclination of the graph is equal to or greater than the predetermined threshold value, the determination unit estimates that aging deterioration has occurred in the device. The determination device according to Supplementary Note 18. (Supplementary Note 20) If the inclination of the graph is less than the predetermined threshold value, the determination unit estimates that no abnormality has occurred in the device. The determination device according to Supplementary Note 19. (Supplementary Note 21) An acquisition step of acquiring an operation status of a device to be monitored as operation information and acquiring environment information around the device; A prediction model generation step of generating a prediction model of the operation status, using, as teacher data, a set of variables including at least the explanatory variable including the environment information in a predetermined time period and a target variable including at least the operation information at a timing immediately after the predetermined time period, and a plurality of sets of the variables defined by sequentially shifting time slots in the set of variables; A prediction step of predicting the operation status of the device using the prediction model and the newly acquired environment information; A monitoring method executed by a prediction device. (Supplementary Note 22) A reception step of receiving environment information around a plurality of devices from a plurality of peripheral devices provided corresponding to each of the plurality of devices to be monitored; An operation information acquisition step of acquiring an operation status of the plurality of devices as operation information; A state determination step of determining whether or not there is an abnormality in a first device among the plurality of devices using the operation information and the environment information; When it is determined that the first device has an abnormality, output an instruction to increase the frequency of transmitting the environmental information to the peripheral device provided corresponding to the second device associated and stored as being in an environment similar to the first device, and output an instruction to stop the processing for the second device, and an instruction output step for executing at least any one of them; A monitoring method executed by a determination device. (Appendix 23) An acquisition step of acquiring the operating status of the device to be monitored as operating information related to at least any one of sound and vibration; A power spectrum calculation step of calculating the power spectrum of the operating information at each of a plurality of different timings of 3 or more; A similarity calculation step of calculating at least the similarity between the power spectrum at the first timing and the power spectrum at the second timing, and the similarity between the power spectrum at the first timing and the power spectrum at the third timing; A determination step of determining the operating status of the device using the calculated plurality of similarities; A monitoring method executed by a determination device. (Appendix 24) An acquisition step of acquiring the operating status of the device to be monitored as operating information and acquiring the environmental information around the device; A prediction model generation step of setting at least the explanatory variable including the environmental information in a predetermined time region and at least the objective variable including the operating information at the timing immediately after the predetermined time region as a set of variables, and generating a prediction model of the operating status using a plurality of sets of variables defined by shifting the time slots in the set of variables in order as teacher data; A prediction step of predicting the operating status of the device using the prediction model and the newly acquired environmental information; A program for causing a prediction device to execute as a monitoring method. (Appendix 25) A receiving step of receiving environmental information around the plurality of devices from a plurality of peripheral devices provided corresponding to each of the plurality of devices to be monitored; An operation information acquisition step of acquiring the operation status of the plurality of devices as operation information; A state determination step of determining the presence or absence of an abnormality in the first device among the plurality of devices by using the operation information and the environmental information; When it is determined that the first device has an abnormality, output an instruction to increase the frequency of transmitting the environmental information to the peripheral device provided corresponding to the second device associated and stored as being in an environment similar to the first device, and at least one of outputting an instruction to stop processing to the second device; an instruction output step of executing any one of A program for causing a determination device to execute as a monitoring method. (Supplementary Note 26) An acquisition step of acquiring the operation status of the device to be monitored as operation information related to at least one of sound and vibration; A power spectrum calculation step of calculating the power spectrum of the operation information at each of a plurality of different timings of 3 or more; A similarity calculation step of calculating at least the similarity between the power spectrum at the first timing and the power spectrum at the second timing, and the similarity between the power spectrum at the first timing and the power spectrum at the third timing; A determination step of determining the operation status of the device by using the calculated plurality of similarities; A program for causing a determination device to execute as a monitoring method.

[0152] As described above, the present disclosure has been described with reference to the embodiments, but the present disclosure is not limited thereto. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the disclosure.

Explanation of Signs

[0153] 10 Peripheral device 11 Power receiving and supplying unit 12 Environment sensor 13 Transmitting unit 14 Rechargeable battery 20 Monitoring server 21 Receiving unit 22 Operating information acquisition unit 23 State determination unit 30 Prediction device 31 Acquisition unit 32 Prediction model generation unit 321 CNN model unit 321a Convolutional layer 321b Fully connected layer 33 Prediction unit 40 Determination device 41 Receiving unit 42 Operating information acquisition unit 43 Memory unit 44 State determination unit 45 Instruction output unit 50 Determination device 51 Acquisition unit 52 Power spectrum calculation unit 53 Similarity calculation unit 54 Determination unit 100 USB sensor 101 Connection unit 102 Controller 103 Non-volatile storage area 104 Illuminance sensor 105 Impact and vibration sensor 106 Temperature and humidity sensor 107 Wireless communication module 108 Rechargeable battery 109 Sound sensor 200 Monitoring server 201 Receiving unit 202 Learning unit 203 Model storage unit 204 Inference unit 205 Notification unit S1, S2 Monitoring system 211 Receiving unit 212 Power spectrum calculation unit 213 Similarity calculation unit 214 Determination Unit 215 Notification Unit

Claims

1. A power receiving and supplying unit that receives power supply from a device to be monitored, An environmental sensor that acquires environmental information around the device, A transmitting unit that transmits the environmental information, A rechargeable battery that is charged by receiving power supply from the device via the power receiving and supplying unit, A storage unit that stores the environmental information, and When the environmental sensor and the transmitting unit cannot receive power supply via the power receiving and supplying unit, they operate with the discharge power from the rechargeable battery. When the transmitting unit is receiving power supply via the power receiving and supplying unit, it transmits the environmental information stored in the storage unit at regular intervals. When it can no longer receive power supply via the power receiving and supplying unit, it transmits the environmental information stored in the storage unit regardless of the regular intervals. Peripheral device.

2. A power receiving and supplying unit that receives power supply from a device to be monitored, An environmental sensor that acquires environmental information around the device, A transmitting unit that transmits the environmental information, A rechargeable battery that is charged by receiving power supply from the device via the power receiving and supplying unit, A storage unit that stores the environmental information, and A determination unit that determines whether or not a detection value of the environmental information detected by the environmental sensor is equal to or greater than a predetermined threshold value. When the environmental sensor and the transmitting unit cannot receive power supply via the power receiving and supplying unit, they operate with the discharge power from the rechargeable battery. When the determination unit determines that the detection value is less than the predetermined threshold value, the transmitting unit transmits the environmental information stored in the storage unit at regular intervals. When the determination unit determines that the detection value is equal to or greater than the predetermined threshold value, the transmitting unit transmits the environmental information stored in the storage unit regardless of the regular intervals. Peripheral device.

3. A power receiving and supplying unit that receives power supply from a device to be monitored, An environmental sensor that acquires environmental information around the device, A transmitting unit that transmits the environmental information, A rechargeable battery that is charged by receiving power supply from the device via the power receiving and supplying unit, and When the environmental sensor and the transmitting unit cannot receive power supply via the power receiving and supplying unit, they operate with the discharge power from the rechargeable battery. The transmitting unit transmits the environmental information to a monitoring server, and when it receives an instruction from the monitoring server to increase the frequency of transmitting the environmental information, it increases the frequency of transmitting the environmental information. Peripheral device.

4. The peripheral device is a USB peripheral. The peripheral device according to any one of claims 1 to 3.

5. A peripheral device provided corresponding to a device to be monitored, A monitoring server that executes communication with the peripheral device, comprising: The peripheral device, A power receiving unit that receives power supply from the device, An environmental sensor that acquires environmental information around the device, A transmission unit that transmits the environmental information, A rechargeable battery that is charged by receiving power supply from the device via the power receiving unit, and When the environmental sensor and the transmission unit cannot receive power supply via the power receiving unit, they operate with the discharge power from the rechargeable battery, The monitoring server, A receiving unit that receives the environmental information from the peripheral device, An operation information acquisition unit that acquires the operation status of the device as operation information, A state determination unit that determines the state of the device using the operation information and the environmental information. Monitoring system.

6. The state determination unit, At least the explanatory variable including the environmental information in a predetermined time region and at least the target variable including the operation information at the timing immediately after the predetermined time region are set as a set of variables, and a plurality of the sets of variables defined by shifting the time slots in the set of variables in order are used as teacher data to generate a prediction model for predicting the operation status, a prediction model generation unit, A prediction unit that predicts the operation status using the prediction model and the newly acquired environmental information. The monitoring system according to claim 5.

7. The monitoring system includes a plurality of the peripheral devices corresponding to each of the plurality of devices to be monitored, The monitoring server, A storage unit that associates and stores a first device and a second device in a similar environment among the plurality of devices, When the state determination unit determines an abnormality for the first device, output an instruction to increase the frequency at which the transmission unit transmits the environmental information to the peripheral device corresponding to the second device, and output an instruction to stop the process to the second device, and an instruction output unit that executes at least any one of them. The monitoring system according to claim 5.

8. A charging step of charging a rechargeable battery by receiving power supply from a device to be monitored. An environmental information acquisition step of acquiring environmental information around the device using the discharge power from the rechargeable battery when power supply from the device cannot be received; A storage step of storing the environmental information; A transmission step of transmitting the stored environmental information at regular timing when power supply is being received from the device, and transmitting the stored environmental information regardless of the regular timing using the discharge power from the rechargeable battery when power supply from the device cannot be received; A monitoring method executed by a peripheral device.

9. A charging step of charging a rechargeable battery by receiving power supply from a device to be monitored; An environmental information acquisition step of acquiring environmental information around the device using the discharge power from the rechargeable battery when power supply from the device cannot be received; A storage step of storing the environmental information; A determination step of determining whether or not a detected value of the environmental information is equal to or greater than a predetermined threshold; A transmission step of transmitting the stored environmental information at regular timing when it is determined that the detected value is less than the predetermined threshold, and transmitting the stored environmental information regardless of the regular timing when it is determined that the detected value is equal to or greater than the predetermined threshold; A program for causing a peripheral device to execute as a monitoring method.

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