JP7292908B2 - Abnormality detection device, abnormality detection method, and program - Google Patents

Description

TECHNICAL FIELD Embodiments of the present invention relate to an abnormality detection device, an abnormality detection method, and a program.

Conventionally, techniques for determining abnormalities such as cooling abnormalities of heat-generating components in electronic equipment are known. For example, a technology is disclosed that determines a decrease in cooling performance for a heat-generating component, such as a processor, by using a usage rate that indicates how much of the processing time of the heat-generating component such as a processor is occupied.

However, even if the usage rate of a heat-generating component such as a processor is the same, the amount of heat generated differs depending on the software. In addition, it is not easy in terms of cost to grasp the heat generation state of the heat generating component by measuring the current value and voltage value of the heat generating component. In addition, it has been difficult to individually measure the current value or voltage value flowing through each of a plurality of components mounted in an electronic device. For this reason, it has been difficult to easily determine an abnormality in an electronic device with the conventional technology.

JP 2014-52689 A JP 2017-54498 A JP 2016-51213 A

SUMMARY OF THE INVENTION It is an object of the present invention to provide an abnormality detection device, an abnormality detection method, and a program that can easily determine an abnormality in an electronic device.

An abnormality detection device according to an embodiment is provided in an electronic device and includes a heat-generating component that is driven by electric power and changes the amount of heat generated, a plurality of environment sensors that detect the environment of the heat-generating component and are arranged in different positions, and a plurality of a first calculation unit that calculates a first estimated value indicating the amount of heat generated by the heat-generating component according to the first detection value of each of the environment sensors; and the first estimated value is equal to or greater than a first threshold In the case of, a determination unit that determines that the heat-generating component is abnormal. The environment sensor includes a sensor that detects the number of rotations or the amount of power of a component other than the heat-generating component provided in the electronic device, and the first calculation unit detects A value detected by the environmental sensor for the power amount or rotation speed of the component and a detected value for the temperature of the heat-generating component by the environmental sensor are acquired as the first detected values, and according to the first detected value to calculate the first estimated value.

The schematic diagram of an abnormality detection apparatus. 1 is a block diagram of an information processing device; FIG. Schematic diagram of calculation of the first estimated value. 4 is a flowchart showing the flow of information processing; The figure which shows the relationship between a usage rate and a calorific value. The figure which shows the 1st estimated value and the measurement result of an actual calorific value. 1 is a block diagram of an information processing device; FIG. Schematic diagram of calculation of the third estimated value. 4 is a flowchart showing the flow of information processing; FIG. 2 is a block diagram showing a hardware configuration example of a control unit;

Details of the present embodiment will be described below with reference to the accompanying drawings.

FIG. 1 is a schematic diagram showing an example of the abnormality detection device 1. As shown in FIG.

The abnormality detection device 1 includes an electronic device 10 and an information processing device 20 . The electronic device 10 and the information processing device 20 are connected so as to be able to exchange data or signals.

The electronic device 10 is a device that includes one or more components 14 and is driven by supplied power. The electronic device 10 is applied, for example, to various devices in which one or more electronic components are mounted in one rack. Specifically, the electronic device 10 is applied to various electronic devices such as a transmitter for digital broadcasting, a data relay device, a computer, and a server.

The electronic device 10 includes a housing 12 housing various components 14 and the like.

The housing 12 is a main body portion of the electronic device 10, and is an exterior housing various parts and devices. In the present embodiment, a case where the housing 12 is a box-shaped member with a hollow inside will be described as an example.

A part of the housing 12 is provided with an intake port 13A for taking in air from the outside of the housing 12 into the inside of the housing 12 . Further, a part of the housing 12 is provided with an exhaust port 13B for discharging the air inside the housing 12 to the outside of the housing 12 .

Disposed within housing 12 are one or more components 14 and a plurality of sensors 16 . In the present embodiment, a configuration in which a plurality of sensors 16 and a plurality of sensors 16 are arranged in housing 12 will be described as an example.

The component 14 is at least one of an electronic component and a member having various functions.

The electronic component is, for example, a component 14 that is driven according to supplied power and that changes the amount of heat generated by driving. "Drive" includes both electrical drive and mechanical drive. Electrical driving includes, for example, processing by a processor such as a CPU (Central Processing Unit). Mechanical drive includes, for example, motor drive.

The electronic components are, for example, the heat-generating component 14A and the air blower 14C.

The heat-generating component 14A is provided in the electronic device 10, is driven according to the supplied electric power, and changes the amount of heat generated. Specifically, the heat-generating component 14A exhibits a non-linear relationship between the amount of heat generated by the heat-generating component 14A and the power consumption of the electronic device 10 . That is, the heat-generating component 14A is a component 14 for which the amount of heat generated by the heat-generating component 14A cannot be derived from the power consumption of the electronic device 10 using a linear function.

The heat-generating component 14A is, for example, a processor such as a CPU or a GPU (Graphics Processing Unit). Note that the heat-generating component 24 is not limited to a CPU or GPU as long as it generates heat by being driven in accordance with supplied power and changes the amount of heat generated. For example, the heat generating component 14A may be a motor, an electronic circuit, or the like.

In this embodiment, a configuration in which a plurality of heat generating components 14A (heat generating component 14A1, heat generating component 14A2, and heat generating component 14A3) are provided in housing 12 will be described as an example. The number of heat-generating components 14A provided in the housing 12 is not limited to three, and may be one, two, or four or more.

The blower 14C is driven according to the supplied electric power, so that the drive portion (for example, the rotary drive motor) generates heat, and the amount of heat generated changes according to the drive. That is, it can be said that the blower 14C is an example of the heat-generating component 14A.

In the present embodiment, the blower 14C is provided at the end of the housing 12 on the intake port 13A side. The blower 14C is arranged at a position capable of blowing the air F into the housing 12 from the air inlet 13A. A filter 17 is provided on the upstream side of the air blower 14C in the direction in which the wind F flows. Therefore, by driving the blower 14C, the air outside the housing 12 is sent into the housing 12 through the filter 17, and the wind F is fed into the housing 12. FIG. The heat generated inside the housing 12 is removed by the wind F sent into the housing 12 .

Air blower 14C may be called a fan or a blower, for example.

The blower 14</b>C may be arranged at a position capable of discharging the air inside the housing 12 to the outside of the housing 12 . That is, the blower 14C may be arranged at the end of the housing 12 on the side of the exhaust port 13B. Moreover, even if the blower 14C is arranged at both a position where the air F can be sent into the housing 12 and a position where the air inside the housing 12 can be discharged to the outside of the housing 12. good.

A member having various functions, which is an example of the component 14, may be a non-driving member. A member having various functions is, for example, a member having a cooling function. A member having a cooling function is, for example, a member made of a metal material with high thermal conductivity. Specifically, the member having a cooling function is the heat sink 14B, a heat radiation component, and the like.

The heat-generating components 14A arranged in the housing 12 are cooled by a member having a cooling function such as a heat sink 14B, or the air F sent by the blower 14C.

In the present embodiment, as shown in FIG. 1, one or more heat generating components 14A are arranged on each of the substrates 18A and 18B in the housing 12. As shown in FIG.

The board 18 is an example of an electronic circuit board, and is sometimes called a motherboard or a main board.

In the present embodiment, a heat-generating component 14A1 and a heat-generating component 14A2 are arranged as the heat-generating component 14A on the substrate 18A. A heat sink 14B is arranged on the heat-generating component 14A1 or on the substrate 18A. The heat sink 14B functions as a heat radiating member that radiates heat generated by the heat generating component 14A.

A heat-generating component 14A3 is arranged on the substrate 18B.

Note that the electronic device 10 may have a configuration in which another substrate 18 is further arranged inside the housing 12 . Further, the electronic device 10 may have a configuration in which the housing 12 further includes a plurality of heat-generating components 14A and other components 14 .

A plurality of sensors 16 (sensors 16a to 16h) are arranged inside the housing 12 . These multiple sensors 16 are arranged at different positions.

The sensor 16 is a sensor capable of measuring varying physical quantities within the housing 12 . For example, the sensor 16 detects physical quantities such as temperature, humidity, pressure, flow rate, acceleration, current, voltage, number of revolutions, etc., and outputs detection results as detection values. Physical quantities and detected values are represented by numerical values indicating, for example, temperature, humidity, pressure, flow rate, acceleration, current, voltage, number of revolutions, and the like. In addition, below, the detection value of the sensor 16 may be called the detection value X, and it demonstrates.

The sensor 16 includes, for example, a temperature sensor, a humidity sensor, a pressure sensor, a flow rate sensor of the wind F, a current sensor, a voltage sensor, a rotation speed sensor that detects the rotation speed of the fan 14C, and an acceleration sensor that detects the rotation acceleration of the fan 14C. , etc.

It is assumed that the electronic device 10 is not provided with the sensor 16 capable of detecting the current, voltage, and usage rate (operating rate, load rate) of each of the heat generating components 14A.

The sensor 16 may be arranged at a position capable of measuring various physical quantities inside the housing 12 .

In the present embodiment, it is assumed that the sensors 16a to 16c and the sensors 16e to 16h are temperature sensors for detecting temperature. The sensor 16a is arranged at a position capable of detecting the temperature of the heat generating component 14A1. Specifically, the sensor 16a is arranged in contact with the heat-generating component 14A1.

The sensor 16b is arranged on the substrate 18A and is arranged at a position that is not in contact with the heat-generating components 14A1 and 14A2. The sensor 16g is arranged in contact with the heat generating component 14A2. The sensor 16h is installed on the substrate 18A and arranged at a non-contact position with respect to the heat-generating component 14A1 and the heat-generating component 14A2. The sensors 16c, 16e, and 16f are arranged in a non-contact manner with respect to the component 14 inside the housing 12, and are arranged at positions different from each other.

The sensor 16d is the sensor 16 that detects the rotation speed of the fan 14C. Note that the sensor 16c only needs to be able to measure a phenomenon related to the driving of the blower 14C. Therefore, the sensor 16c may be a sensor that detects a physical quantity such as the amount of electric power (current value, voltage value) supplied to the blower 14C.

The information processing device 20 is a device that determines whether the electronic device 10 is abnormal. The information processing device 20 and each of the plurality of sensors 16 provided on the housing 12 are connected so as to be able to exchange data or signals. The information processing device 20 may be further connected to various electronic devices other than the sensor 16 mounted on the housing 12 so as to be able to exchange data or signals. For example, the information processing device 20 may be connected to the sensor 16, at least one of the components 14, and the blower 14C so as to be able to exchange data or signals.

Next, an example of the functional configuration of the information processing device 20 will be described.

FIG. 2 is a block diagram showing an example of the functional configuration of the information processing device 20. As shown in FIG. For the sake of explanation, FIG. 2 also shows the information processing device 20 and the sensor 16 for exchanging data or signals with the information processing device 20 in the electronic device 10 .

The information processing device 20 and the plurality of sensors 16 are connected so as to be able to exchange data or signals. As described above, the information processing device 20 is connected to both the plurality of sensors 16 and various electronic devices other than the sensors 16 mounted on the housing 12 so as to be able to exchange data or signals. may

The information processing device 20 includes a control section 30 , a storage section 32 and an output section 34 . The control unit 30, the storage unit 32, and the output unit 34 are connected so as to be able to exchange data or signals.

The storage unit 32 stores various data. The storage unit 32 is, for example, a storage medium such as a known HDD (hard disk drive).

The output unit 34 outputs various information. In this embodiment, the output unit 34 outputs error information. Details of the error information will be described later.

The output unit 34 has at least one of a display function for displaying various information, a sound output function for outputting sound, and a communication function for communicating data with an external device. An external device is a device provided outside the abnormality detection device 1 . The information processing device 20 and the external device may be communicable via a network or the like. For example, the output unit 34 is configured by combining at least one of a known display device, a known speaker, and a known communication device.

Next, the controller 30 will be described.

The control unit 30 includes an acquisition unit 30A, a first calculation unit 30B, a determination unit 30C, and an output control unit 30D.

The acquisition unit 30A, the first calculation unit 30B, the determination unit 30C, and the output control unit 30D are implemented by one or more processors, for example. For example, each unit described above may be implemented by causing a processor such as a CPU to execute a program, that is, by software. Each of the above units may be implemented by a processor such as a dedicated IC, that is, by hardware. Each of the above units may be implemented using both software and hardware. When multiple processors are used, each processor may implement one of the units, or may implement two or more of the units.

Acquisition unit 30A acquires detection value X of sensor 16 . In the present embodiment, the acquisition unit 30A acquires the first detection value Xa of the environment sensor 16A as the detection value X of the sensor 16A.

Here, in the present embodiment, the information processing device 20 determines whether the specific heat-generating component 14A provided in the electronic device 10 is abnormal. The heat-generating component 14A to be subjected to abnormality determination may be determined in advance. Further, the heat-generating component 14A to be subjected to abnormality determination may be changeable by a user's instruction input or the like.

In the present embodiment, an example will be described in which the abnormality of the heat-generating component 14A1 among the plurality of heat-generating components 14A is determined. Also, a case where the heat-generating component 14A1 is a processor such as a CPU or GPU will be described as an example.

When the controller 30 determines the abnormality of the heat-generating component 14A1, the acquisition unit 30A detects the environment of the heat-generating component 14A1, which is the target of abnormality determination, among the plurality of sensors 16 provided in the electronic device 10. It is used as sensor 16A.

The environmental sensors 16A are at least some of the sensors 16 of the plurality of sensors 16 provided in the electronic device 10 and are arranged at different positions within the housing 12 .

The environment sensor 16A is a sensor that detects the environment of the heat generating component 14A1. The "environment" detected by the environment sensor 16A does not include the current value of the heat-generating component 14A1, the voltage value of the heat-generating component 14A1, and the usage rate of the heat-generating component 14A1. That is, the environment sensor 16A is a sensor that detects the environment other than the current value of the heat generating component 14A1, the voltage value of the heat generating component 14A1, and the usage rate of the heat generating component 14A1.

Specifically, the environment sensor 16A is a sensor 16 that detects the temperature of the heat-generating component 14A1 that is subject to abnormality determination. That is, the environment sensor 16A is a temperature sensor. The environment sensor 16A may be arranged at a position where the temperature to be detected changes according to fluctuations in the amount of heat generated by the heat-generating component 14A1. For this reason, the environment sensor 16A may be arranged in contact with the heat-generating component 14A1 to be subjected to abnormality determination, or may be arranged in a non-contact manner.

In the case of the configuration shown in FIG. 1, if the object of abnormality determination is the heat-generating component 14A1, the environmental sensors 16A that detect the temperature of the heat-generating component 14A1 are, for example, sensors 16a, 16b, 16c, 16g, and 16h. be.

The "environment" detected by the environment sensor 16A may include an environment that directly or indirectly affects the environment of the heat-generating component 14A1 to be determined as being abnormal. Specifically, the environment sensor 16A may include a sensor 16 that detects a varying physical quantity of a component 14 other than the heat-generating component 14A1 to be determined as an abnormality. As described above, physical quantities include temperature, humidity, pressure, flow rate, acceleration, current, voltage, rotation speed, and the like.

That is, the environment sensor 16A is a sensor 16 that can detect the varying physical quantity of the component 14 other than the heat-generating component 14A1 to be determined as an abnormality, and is arranged in the housing 12. A sensor 16 capable of detecting the physical quantity that can affect the detection result of the amount of heat generated may be included.

In this case, the environment sensor 16A may include, for example, a sensor 16d that detects the rotation speed or power consumption of the fan 14C.

That is, the environment sensor 16A is arranged in the housing 12, and the physical quantity of the components 14 other than the heat-generating component 14A1 subject to abnormality determination and the temperature of the heat-generating component 14A1 subject to abnormality determination. is detected.

In the present embodiment, an example will be described in which the heat-generating component 14A1 is subject to abnormality determination, and the environment sensors 16A that detect the environment of the heat-generating component 14A1 are the sensors 16a to 16d, the sensor 16g, and the sensor 16h. do.

Therefore, in the present embodiment, acquisition unit 30A receives detection values X from each of sensors 16a to 16d, sensor 16g, and sensor 16h, which are environmental sensors 16A, among the detection values X received from each of the plurality of sensors 16. The detected value X is acquired as the first detected value Xa.

The first calculator 30B calculates a first estimated value Ia indicating the amount of heat generated by the heat-generating component 14A1 to be determined as an abnormality, according to the first detection value Xa of each of the plurality of environment sensors 16A.

FIG. 3 is a schematic diagram of calculation of the first estimated value Ia. As shown in FIG. 3, the first calculator 30B uses a plurality of first detection values Xa (first detection value Xa to first detection value Xn) to indicate the amount of heat generated by the heat generating component 14A1. A first estimated value Ia is calculated.

The first estimated value Ia is an estimated value of the amount of heat generated by the heat generating component 14A1. The calorific value is the amount of energy (the amount of heat generated) per unit, and is expressed in units such as W and W/kg. Note that the calorific value may be a dimensionless quantity or a value representing a qualitative change from a reference, so it may be expressed without a unit.

The first calculator 30B preliminarily derives the correlation between the plurality of first detection values Xa and the first estimated value Ia indicating the amount of heat generated by the heat generating component 14A1. The correlation between the plurality of first detected values Xa and the first estimated value Ia is represented by, for example, Equation (1) below.

Ia=p0+p1*Xa1+p2*Xa2+...+pn*Xan Formula (1)

In formula (1), Xa represents the first detected value Xa. The numbers (1 to n) adjacent to each Xa are the identifiers of the environmental sensors 16A. p0 to pn indicate coefficients. Ia indicates the first estimated value Ia of the amount of heat generated by the heat-generating component 14A1 to be determined as an abnormality.

The first calculator 30B calculates the correlation between the plurality of first detected values Xa and the first estimated values Ia for each component 14 (heat-generating component 14A1 in the present embodiment) subject to abnormality determination. It can be derived in advance. Specifically, for example, the first calculator 30B may derive in advance the values of the counts p0 to Pn in the above equation (1) for each component 14 to be subjected to abnormality determination. This correlation may be derived through experiments, simulations, machine learning, or the like. For machine learning, known machine learning methods such as convolutional neural networks (CNN), hierarchical Bayesian modeling, DNN (Deep Neural Network), data assimilation techniques such as particle filters, and the like may be used.

Equation (1) is given as an example of the function indicating the correlation for calculating the first estimated value Ia of the heat-generating component 14A1 to be determined as an abnormality. However, the correlation formula for calculating the first estimated value Ia may be either a one-dimensional polynomial or a multidimensional polynomial. Further, the correlation formula for calculating the first estimated value Ia may be a highly nonlinear formula.

Then, the first calculation unit 30B substitutes each of the plurality of first detection values Xa into a previously derived function indicating the correlation (for example, the above formula (1)), thereby calculating the heat generation of the abnormality determination target. A first estimated value Ia indicating the amount of heat generated by the component 14A1 may be calculated.

The first calculator 30B preferably calculates the first estimated value Ia according to three or more first detected values Xa. That is, in the case of equation (1), it is preferable to calculate the first estimated value Ia using the above equation (1) including terms for substituting at least three or more first detected values Xa.

The determination unit 30C determines whether or not the first estimated value Ia indicating the amount of heat generated by the heat-generating component 14A1 subject to abnormality determination, which is calculated by the first calculation unit 30B, is greater than or equal to the first threshold. The first threshold value may be a threshold value for determining that the heat-generating component 14A1, which is an abnormality determination target, is abnormal. The determination unit 30C may predetermine the first threshold value for each heat-generating component 14A (component 14) subject to abnormality determination. Note that the first threshold value may be changed as appropriate according to a user's change instruction or the like.

For example, the determining unit 30C may predetermine the threshold value of the amount of heat generation for determining abnormality of the cooling function of the electronic device 10 with respect to the heat generating component 14A1 as the first threshold value.

If the determination unit 30C determines that the first estimated value Ia is equal to or greater than the first threshold value, the determination unit 30C determines that the heat generating component 14A1 is abnormal. The heat-generating component 14A1 being abnormal indicates at least one of the heat-generating abnormality of the heat-generating component 14A1 itself and the abnormality of at least a part of the cooling function for the heat-generating component 14A1.

The output control unit 30D outputs the error information to the output unit 34. FIG.

The error information includes information indicating that an abnormality has occurred in the amount of heat generated by the heat-generating component 14A1, which is the object of abnormality determination, as determined by the determination unit 30C. The error information may further include maintenance information indicating a maintenance work procedure for the heat-generating component 14A1 subject to abnormality determination, items to be noted during maintenance work, contact information for maintenance work service, and the like. In this case, the heat-generating component 14A1 to be subjected to abnormality determination and the maintenance information may be associated in advance and stored in the storage unit 32 . Then, the output control unit 30D may output error information including the maintenance information to the output unit 34 by reading the maintenance information corresponding to the heat-generating component 14A1 to be determined as an abnormality.

The output unit 34 outputs error information. If the output unit 34 has a display function, the output unit 34 displays error information. Moreover, when the output unit 34 has a sound output function, the output unit 34 displays a sound indicating error information.

Therefore, error information can be easily provided to the user. By checking the output error information, the user can understand the abnormality of the heat-generating component 14A1, which is the target of abnormality determination, indicated in the error information, and perform maintenance of the electronic device 10 in accordance with the maintenance information indicated in the error information. work can be done. That is, the information processing device 20 can appropriately provide the user with information necessary for maintenance work of the electronic device 10 .

In addition, by performing maintenance work based on the error information, it is possible to reduce the frequency of occurrence of abnormalities in the electronic device 10, thereby reducing labor costs for maintenance workers and the cost of replacing unnecessary parts 14. be able to.

On the other hand, when the output unit 34 has a communication function, the output unit 34 transmits error information to an external device via a network or the like.

The external device that receives the error information uses the error information to remotely monitor the electronic device 10 that sent the error information, process the error information, analyze the error information, and send the error information to another external device. You may perform various processes, such as transferring to. For example, the external device can analyze the usage environment and usage method of the electronic device 10 by combining and analyzing the installation environment of the electronic device 10 and the error information. Therefore, by outputting the error information, the information processing apparatus 20 can provide information that can be used to improve the design criteria of the electronic device 10 . In addition, the information processing apparatus 20 can provide information that can be used to improve the usage environment of the electronic device 10 and to optimize the usage method.

Also, the external device to which the error information is sent, or another device to which the error information is transferred from the external device may be the server device of the service provider in charge of the maintenance work. In this case, the information processing device 20 can improve the efficiency of maintenance work.

Next, the flow of information processing executed by the information processing device 20 will be described.

FIG. 4 is a flowchart showing an example of the flow of information processing executed by the information processing device 20. As shown in FIG. Note that the order of each of the plurality of steps can be changed as appropriate, and is not limited to the example shown in FIG. Also, at least some of the steps may be executed in parallel.

In addition, FIG. 4 will be described on the assumption that the heat-generating component 14A to be subjected to abnormality determination is determined in advance.

First, the acquisition unit 30A acquires the first detection value Xa from each of the plurality of environment sensors 16A that detect the environment of the heat-generating component 14A1 to be determined as an abnormality (step S100).

The first calculator 30B calculates a first estimated value Ia indicating the amount of heat generated by the heat-generating component 14A1 to be determined as an abnormality, according to the plurality of first detection values Xa acquired in step S100 (step S102). .

The determination unit 30C determines whether or not the first estimated value Ia indicating the amount of heat generated by the heat generating component 14A1 calculated in step S102 is greater than or equal to the first threshold (step S104). When it is determined that the first estimated value Ia is less than the first threshold value (step S104: No), this routine ends. On the other hand, if it is determined that the first estimated value Ia is greater than or equal to the first threshold (step S104: Yes), the process proceeds to step S106.

At step S106, the output control unit 30D outputs the error information to the output unit 34 (step S106). The output control unit 30D outputs to the output unit 34 error information including information indicating that an abnormality has occurred in the amount of heat generated by the heat-generating component 14A1 that is subject to abnormality determination. Then, the routine ends.

As described above, the abnormality detection device 1 of the present embodiment includes the heat-generating component 14A, the environment sensor 16A, the first calculation section 30B, and the determination section 30C. The heat-generating component 14A is a component 14 that is provided in the electronic device 10, driven by electric power, and whose heat generation amount changes. The environment sensor 16A detects the environment of the heat generating component 14A. The plurality of environment sensors 16A are arranged at different positions. The first calculator 30B calculates a first estimated value Ia indicating the amount of heat generated by the heat generating component 14A according to the first detected value Xa of each of the plurality of environment sensors 16A. The determination unit 30C determines that the heat-generating component 14A is abnormal when the first estimated value Ia is greater than or equal to the first threshold.

Here, the amount of heat generated by the heat-generating component 14A such as a processor changes with time. Conventionally, even if the usage rate of the heat-generating component 14A such as a processor is the same value, the amount of heat generated differs depending on the software. That is, the correlation between the usage rate and the calorific value is not strong.

FIG. 5 is a diagram showing the relationship between the usage rate of a CPU, which is an example of the heat-generating component 14A, and the amount of heat generated by the CPU. FIG. 5 shows the results of an experimental investigation of the relationship between the usage rate of the heat-generating component 14A1, which is a CPU, and the amount of heat generated. Several kinds of software were used for the experiment. The calorific value was measured using a dedicated sensor. As shown in FIG. 5, even if the usage rate of the CPU is approximately the same value, it can be seen that the derived calorific value varies. Therefore, the amount of heat generated by the processor derived from the usage rate of the heat-generating component 14A varies.

Further, it is not easy in terms of cost to grasp the heat generation state of the heat generating component 14A by measuring the current value and voltage value of the heat generating component 14A.

The power consumption, current value, and supply voltage of the entire electronic device 10 including the heat-generating component 14A can be measured by a sensor 16 that detects current or voltage. However, it has been difficult to individually measure the current value or voltage value flowing through each of the plurality of components 14 mounted on the electronic device 10 for each component 14 .

Further, in the electronic device 10 equipped with a plurality of heat-generating components 14A whose heat generation amount changes with time, it is difficult to derive the heat generation amount of each of the plurality of heat-generating components 14A from the power consumption of the electronic device 10 . This is because the number of unknowns is two or more, and only one function for deriving the calorific value of each of the plurality of heat-generating components 14A can be established.

Therefore, conventionally, it has been difficult to easily determine whether the electronic device 10 is abnormal.

On the other hand, in the abnormality detection device 1 of the present embodiment, the first value indicating the amount of heat generated by the heat-generating component 14A is estimated according to the plurality of first detection values Xa of the environment sensor 16A that detects the environment of the heat-generating component 14A. Using the estimated value Ia, it is determined whether or not the heat-generating component 14A is abnormal.

As described above, the abnormality detection device 1 of the present embodiment uses only the first detection value Xa that can be obtained while the electronic device 10 is being driven to determine whether the heat-generating component 14A is abnormal. Therefore, in the abnormality detection device 1 of the present embodiment, the heat-generating component 14A can be operated without suspending the driving of the electronic device 10 or applying a load that hinders the processing by the program being executed in the electronic device 10. Abnormalities can be determined.

Therefore, the abnormality detection device 1 of the present embodiment can easily determine the abnormality of the electronic device 10 .

Also, the heat-generating component 14A is, for example, a processor such as a CPU or GPU. Therefore, in addition to the effects described above, the abnormality detection device 1 of the present embodiment can easily determine an abnormality of the heat-generating component 14A, which is a processor such as a CPU or GPU.

FIG. 6 shows the measurement results of the first estimated value Ia indicating the amount of heat generated by the CPU, which is an example of the heat-generating component 14A1, and the actual amount of heat generated by the CPU, derived by the information processing apparatus 20 of the present embodiment. It is a figure which shows. As shown in FIG. 6, a linear relationship was shown between the first estimated value Ia and the calorific value.

Therefore, by using the first detection value Xa used for estimating the first estimated value Ia, the abnormality detection device 1 of the present embodiment can easily estimate the amount of heat generated by the heat-generating component 14A1, and can accurately estimate the amount of heat generated. It can be said that the abnormality of the heat-generating component 14A1 can be determined.

(Second embodiment)
In the present embodiment, a mode will be described in which abnormality in a specific region within housing 12 is determined using the first estimated value Ia and other estimated values. The specific area will be described later.

FIG. 7 is a block diagram showing an example of the functional configuration of the information processing device 21 of this embodiment. Note that, as shown in FIG. 1, the abnormality detection device 1A of the present embodiment is the same as the abnormality detection device 1 of the first embodiment, except that an information processing device 21 is provided instead of the information processing device 20. It is the same.

The information processing device 21 and the plurality of sensors 16 are connected so as to be able to exchange data or signals. As in the above embodiment, the information processing device 21 can exchange data or signals with both the sensors 16 and various electronic devices other than the sensors 16 mounted on the housing 12. may be connected.

The information processing device 21 includes a control section 31 , a storage section 32 and an output section 34 . The control unit 31, the storage unit 32, and the output unit 34 are connected so as to be able to exchange data or signals. The storage unit 32 and the output unit 34 are the same as in the first embodiment.

The control unit 31 includes an acquisition unit 30A, a first calculation unit 30B, a second calculation unit 31E, a third calculation unit 31F, a determination unit 31C, and an output control unit 31D. 30 A of acquisition parts and the 1st calculation part 30B are the same as that of 1st Embodiment.

The acquisition unit 30A, the first calculation unit 30B, the second calculation unit 31E, the third calculation unit 31F, the determination unit 31C, and the output control unit 31D are realized by, for example, one or more processors. For example, each unit described above may be implemented by causing a processor such as a CPU to execute a program, that is, by software. Each of the above units may be implemented by a processor such as a dedicated IC, that is, by hardware. Each of the above units may be implemented using both software and hardware. When multiple processors are used, each processor may implement one of the units, or may implement two or more of the units.

30 A of acquisition parts acquire several 1st detection values Xa similarly to 1st Embodiment. The first detected value Xa is the same as in the first embodiment. The first calculator 30B calculates a first estimated value Ia from a plurality of first detected values Xa.

FIG. 8 is a schematic diagram of calculation of the second estimated value Ic. The second estimated value Ic will be described later. As shown in FIG. 8, the first calculator 30B calculates a first estimated value Ia indicating the amount of heat generated by the heat-generating component 14A subject to abnormality determination, according to the plurality of first detected values Xa. The first calculator 30B calculates the first estimated value Ia in the same manner as in the first embodiment.

In the present embodiment, the first estimated value of the amount of heat generated by the heat-generating component 14A among the plurality of heat-generating components 14A mounted on the electronic device 10, which is a factor affecting the abnormality in the specific region of the electronic device 10. Calculate Ia. In the present embodiment, an example will be described in which the abnormality in the specific region is the temperature abnormality in the specific region. However, anomalies in specific regions are not limited to temperature anomalies. For example, anomalies in a specific area may be anomalies in humidity, pressure, or the like.

That is, in the present embodiment, the heat-generating component 14</b>A to be subjected to abnormality determination is the heat-generating component 14</b>A that affects the temperature abnormality in the specific region of the electronic device 10 .

A specific area within the housing 12 may be determined in advance as the specific area of the electronic device 10 . However, the specific area is preferably an area within the housing 12 of the electronic device 10 that cannot be directly measured by the sensor 16 . That is, the specific area is preferably an area within the housing 12 where the sensor 16 is not installed.

Information indicating the heat-generating component 14A that is a factor affecting the abnormality in the specific area may be stored in the storage unit 32 in advance for each identification information of the specific area. Then, when calculating the first estimated value Ia, the first calculation unit 30B may read from the storage unit 32 the information indicating the heat-generating component 14A corresponding to the identification information of the specific area subject to abnormality determination. Then, the first calculator 30B may calculate the first estimated value Ia of the read heat-generating component 14A using the first detected value Xa.

Further, in the present embodiment, description will be made on the assumption that there are a plurality of heat-generating components 14A to be subjected to abnormality determination. Therefore, the first calculator 30B uses the plurality of first detection values Xa to calculate the first estimated value Ia of the calorific value of each of the plurality of heat generating components 14A arranged at different positions. do. That is, the first calculator 30B calculates a plurality of first estimated values Ia from a plurality of first detected values Xa. It is assumed that the number of first detected values Xa and the number of first estimated values Ia are the same. This is because we need as many functions as there are unknowns.

Note that the first calculation unit 30B calculates the plurality of first detection values Xa and the first estimated value Ia (first estimated value Ia1 to first An estimated value Ian) of 1 and the correlation are derived in advance. For example, the first calculator 30B preliminarily derives, for each heat-generating component 14A, a formula representing the correlation between the first estimated value Ia and the first detected value Xa represented by the above formula (1). Therefore, the correlation represented by the equation (1) differs in at least one value of the coefficients (p0 to pn) shown in the equation (1) for each corresponding heat-generating component 14A.

Note that the first calculator 30B may derive this correlation in advance by experiment, simulation, machine learning, or the like, as in the first embodiment. For machine learning, known machine learning methods such as CNN, hierarchical Bayesian modeling, DNN, and data assimilation techniques such as particle filters may be used.

Also, the first calculator 30B preferably calculates the first estimated value Ia according to three or more first detected values Xa. Further, it is assumed that the types of the first detection values Xa and the number of the first detection values Xa used for calculating the first estimated value Ia of the heat generation amount of each of the plurality of heat generating components 14A are the same. .

The second calculation unit 31E calculates an evaluation value Ib of a factor that affects the abnormality in the specific region to be determined as an abnormality determination target and the factor other than the heat-generating component 14A, according to the plurality of first detection values Xa. calculate.

Factors that affect the abnormality in the specific area to be determined as an abnormality and are factors other than the heat-generating component 14A include, for example, the temperature outside the housing 12, the contact state of the heat sink 14B, the clogging of the filter 17, Factors other than the heat-generating component 14A, such as insufficient rotation of the blower 14C, blocked state of the air inlet 13A, and blocked state of the air outlet 13B.

The evaluation value Ib indicates a higher value as the influence of the temperature abnormality in the specific area subject to abnormality determination is greater. In the present embodiment, the second calculator 31E calculates a plurality of evaluation values Ib (evaluation values Ib1 to Ibn) having different factors. That is, the second calculator 31E calculates multiple evaluation values Ib from multiple first detection values Xa. It is assumed that the number of first detection values Xa and the number of evaluation values Ib are the same. This is because we need as many functions as there are unknowns.

The second calculator 31E preliminarily derives the correlation between the plurality of first detection values Xa and the evaluation value Ib. For example, this correlation is represented by the following formula (2).

Ib=q0+q1*Xa1+q2*Xa2+...+qn*Xan Formula (2)

In formula (2), Xa represents the first detected value Xa. The numbers (1 to n) adjacent to each Xa are the identifiers of the environmental sensors 16A. q0 to qn indicate coefficients. n represents an integer of 2 or more. Ib indicates the evaluation value Ib of the factor.

The second calculator 31E may preliminarily derive the correlation between the factor evaluation value Ib and the plurality of first detection values Xa for each factor. Specifically, for example, the second calculator 31E may derive in advance the values of the counts q0 to qn of the above equation (2). This correlation may be derived through experiments, simulations, machine learning, or the like. For machine learning, known machine learning methods such as CNN, hierarchical Bayesian modeling, DNN, and data assimilation techniques such as particle filters may be used. In the above description, Equation (2) is given as an example of the function indicating the correlation for calculating from the first detected value Xa. However, the correlation formula for calculating the evaluation value Ib from the first detection value Xa may be either a one-dimensional polynomial or a multidimensional polynomial. Further, the correlation formula for calculating the evaluation value Ib from the first detection value Xa may be a highly nonlinear formula.

Then, the second calculation unit 31E substitutes each of the plurality of first detection values Xa into a function (for example, the above formula (2)) indicating the correlation for each factor derived in advance, so that for each factor Then, the factor evaluation value Ib may be calculated.

Note that the second calculation unit 31E calculates the plurality of first detection values Xa , the evaluation value Ib may be calculated for each factor.

The third calculator 31F calculates a second estimated value Ic of the temperature of the specific region based on the first estimated value Ia and the factor evaluation value Ib.

The third calculator 31F preliminarily derives the correlation between the plurality of first estimated values Ia and the plurality of evaluation values Ib, and the second estimated value Ic. For example, this correlation is represented by the following formula (3).

Ic=r0+r1Ia1+r2Ia2+...+rnIan+s1Ib1+s2Ib2+...+snIbn Formula (3)

In Equation (3), Ia indicates the first estimated value Ia. A number (1 to n) adjacent to each Ia is an identifier of the heat-generating component 14A. Ib indicates the evaluation value Ib of the factor. The numbers (1 to n) adjacent to each of Ib are the factor identifiers. r0-rn and s1-sn indicate coefficients. n represents an integer of 2 or more. Ic denotes a second estimate Ic of the temperature of the particular region.

The third calculator 31F may derive in advance the correlation between the plurality of first estimated values Ia and the plurality of evaluation values Ib, and the second estimated value Ic. Specifically, for example, the third calculator 31F may derive in advance the values of the counts r0 to rn and s1 to sn of the above equation (3). This correlation may be derived through experiments, simulations, machine learning, or the like. For machine learning, known machine learning methods such as CNN, hierarchical Bayesian modeling, DNN, and data assimilation techniques such as particle filters may be used. In the above description, Equation (3) is given as an example of the function indicating the correlation between the plurality of first estimated values Ia and the plurality of evaluation values Ib, and the second estimated value Ic. However, this correlation formula may be either a one-dimensional polynomial or a multi-dimensional polynomial. Also, this correlation formula may be a highly nonlinear formula.

Then, the third calculation unit 31F substitutes each of the plurality of first estimated values Ia and the plurality of evaluation values Ib into the function indicating this correlation (for example, the above equation (3)) to obtain the 2 estimated value Ic can be calculated.

In addition to the function indicating the correlation, the third calculation unit 31F uses a trained model that indicates the correlation represented by the above equation (3), etc., to obtain the plurality of first estimated values Ia and the plurality The second estimated value Ic may be calculated from the evaluation value Ib of .

Note that the third calculator 31F may calculate the second estimated value Ic of the temperature of each specific region different from each other. That is, the third calculator 31F may calculate the second estimated value Ic of the temperature of each of the plurality of specific regions from the plurality of first estimated values Ia and the plurality of evaluation values Ib (see FIG. 8 ). In FIG. 8, Ic indicates the second estimated value Ic, and the numerical value to the right of Ic is the identifier of the specific region. Thus, the third calculator 31F may calculate a plurality of second estimated values Ic.

Next, the determination section 31C and the output control section 31D will be described.

Similar to the determination unit 30C of the first embodiment, the determination unit 31C determines whether or not the first estimated value Ia of the amount of heat generated by the heat-generating component 14A1 to be determined as being abnormal is equal to or greater than the first threshold. do. If the determination unit 30C determines that the first estimated value Ia is equal to or greater than the first threshold value, the determination unit 30C determines that the heat generating component 14A1 is abnormal. The output control section 31D then outputs the error information to the output section 34 . In this case, the error information includes information indicating that an abnormality has occurred in the amount of heat generated by the heat-generating component 14A1, which is the object of abnormality determination, as determined by the determination unit 31C.

In the present embodiment, the determination unit 31C further determines whether or not the evaluation value Ib of factors other than the heat-generating component 14A is greater than or equal to the third threshold. The third threshold may be any threshold for determining that the factor represented by the evaluation value Ib is abnormal. The determination unit 31C may predetermine the third threshold for each factor. Specifically, the determination unit 31C determines that there is an abnormality for each factor other than the heat-generating component 14A, such as the contact state of the heat sink 14B, the clogging of the filter 17, and the insufficient rotation of the blower 14C. 3 thresholds may be determined in advance. It should be noted that the third threshold value may be changed as appropriate according to a user's change instruction or the like.

Further, in the present embodiment, the determination unit 31C determines that the specific region is abnormal when the second estimated value Ic is equal to or greater than the second threshold. That is, when the second estimated value Ic is equal to or greater than the second threshold, the determination unit 31C determines that an abnormality has occurred in the specific region.

As the second threshold, a threshold for determining that an abnormality has occurred may be determined in advance for each specific region. Also, the second threshold value may be changed as appropriate according to a user's change instruction or the like.

As described above, the specific area is preferably an area within the housing 12 of the electronic device 10 that cannot be directly measured by the sensor 16 . That is, the specific area is preferably an area within the housing 12 where the sensor 16 is not installed. Therefore, the determination unit 31C can easily determine whether or not an abnormality has occurred in the specific area where the sensor 16 is not installed.

Next, the flow of information processing executed by the information processing device 21 will be described.

FIG. 9 is a flowchart showing an example of the flow of information processing executed by the information processing apparatus 21 of this embodiment. Note that the order of each of the plurality of steps can be changed as appropriate, and is not limited to the example of FIG. 9 . Also, at least some of the steps may be executed in parallel.

In addition, in FIG. 9, the form which calculates the 2nd estimated value Ic of each of several specific area|regions is demonstrated as an example.

First, the acquisition unit 30A acquires a plurality of first detection values Xa (step S200).

Next, the first calculator 30B calculates a first estimated value Ia of the amount of heat generated by the heat-generating component 14A subject to abnormality determination according to the plurality of first detection values Xa acquired in step S200 (step S202). Here, it is assumed that the first estimated value Ia of the calorific value of each of the plurality of heat-generating components 14A different from each other is calculated.

Next, the determination unit 31C and the output control unit 31D repeatedly execute the processes of steps S204 and S206 for each of the plurality of first estimated values Ia calculated in step S202.

Specifically, the determination unit 31C determines whether or not the first estimated value Ia is greater than or equal to the first threshold (step S204). If the first estimated value Ia is greater than or equal to the first threshold value (step S204: Yes), the determination unit 31C generates error information indicating that the heat-generating component 14A with the first estimated value Ia used for determination is abnormal. is output to the output unit 34 (step S204). Then, this iterative process ends. Moreover, when a negative determination is made in step S204 (step S204: No), this repetitive process ends. If the determination in step S204 is affirmative (step S204: Yes), this routine may be terminated after outputting the error information in step S206.

Next, the second calculation unit 31E determines factors that affect the abnormality in the specific area to be determined as an abnormality determination target, in accordance with the plurality of first detection values Xa acquired in step S200, and is calculated (step S208). Here, it is assumed that evaluation values Ib are calculated for each of a plurality of mutually different factors.

Next, the determination unit 31C and the output control unit 31D repeatedly execute the processes of steps S210 and S212 for each of the plurality of evaluation values Ib calculated in step S208.

Specifically, the determination unit 31C determines whether or not the evaluation value Ib is greater than or equal to the third threshold (step S210). When the evaluation value Ib is equal to or greater than the third threshold (step S220: Yes), the determination unit 31C outputs error information indicating that the factor of the evaluation value Ib used for determination is abnormal to the output unit 34. (Step S212). Then, this iterative process ends. If the determination in step S210 is negative (step S210: No), this repetitive process ends. If the determination in step S210 is affirmative (step S210: Yes), this routine may be terminated after outputting the error information in step S212.

Next, the third calculator 31F calculates the second estimated value Ic using the plurality of first estimated values Ia calculated in step S202 and the plurality of evaluation values Ib calculated in step S208. Calculate (step S214). Here, it is assumed that the second estimated value Ic of each temperature of a plurality of different specific regions is calculated.

Next, the determination unit 31C and the output control unit 31D repeatedly execute the processes of steps S216 and S218 for each of the plurality of second estimated values Ic calculated in step S214.

Specifically, the determination unit 31C determines whether or not the second estimated value Ic is greater than or equal to the second threshold (step S216). If the second estimated value Ic is equal to or greater than the second threshold (step S216: Yes), the determination unit 31C outputs error information indicating that the specific region indicating the second estimated value Ic used for determination is abnormal. is output to the output unit 34 (step S218). Then, this iterative process ends. Moreover, when a negative determination is made in step S216 (step S216: No), this repetitive process ends.

As described above, the first calculator 30B of the abnormality detection device 1A of the present embodiment determines factors affecting the abnormality in the specific region of the electronic device 10 according to the plurality of first detection values Xa. A first estimated value Ia of the amount of heat generated by the heat generating component 14A is calculated. The second calculation unit 31E calculates an evaluation value Ib of a factor affecting the abnormality in the specific region and other than the heat-generating component 14A, according to the first detection value Xa of each of the plurality of environment sensors 16A. calculate. The third calculator 31F calculates a second estimated value Ic of the temperature of the specific region based on the first estimated value Ia and the evaluation value Ib. The determination unit 31C determines that the specific region is abnormal when the second estimated value Ic is equal to or greater than the second threshold.

Therefore, in the abnormality detection device 1A of the present embodiment, even if the specific region is a region that cannot be directly measured by the sensor 16, the first estimated value Ia and the first estimated value IaB are used to , the abnormality of the specific region can be determined by calculating the second estimated value Ic of the temperature of the specific region.

Therefore, the abnormality detection device 1A of the present embodiment can accurately and easily determine an abnormality in a specific area in the electronic device 10 in addition to the effects of the above-described embodiments.

Further, in the abnormality detection device 1A of the present embodiment, even if the sensor 16 is not arranged in the specific area to be identified as an abnormality, the abnormality in the specific area can be determined. It is possible to reduce the amount of sensors 16 to be mounted.

Further, in the abnormality detection device 1A of the present embodiment, by determining abnormality using each of the first estimated value Ia, the evaluation value Ib, and the second estimated value Ic, the output control unit 31D detects an error Output information. The error information includes information indicating that an abnormality has occurred in the amount of heat generated by the heat-generating component 14A1 to be subjected to abnormality determination, information indicating that an abnormality has occurred in a factor other than the heat-generating component 14A, and information indicating that an abnormality has occurred in a specific area. information indicating that the is occurring.

For this reason, the abnormality detection apparatus 1A of the present embodiment can perform replacement of the parts 14, maintenance of the parts 14, analysis of the parts 14 that are likely to cause an abnormality, optimization of the life of the parts 14, optimization of the parts 14 constituting the electronic device 10, Information that can be used for analysis such as the design of the electronic device 10 and clarification of the cause of failure can be provided as error information.

In the first embodiment and the second embodiment, an example is shown in which the electronic device 10 and the information processing device 20 are separately configured (see FIG. 1). However, the electronic device 10 and the information processing device 20 may be configured integrally. That is, the electronic device 10 may be configured to include the information processing device 20 . In this case, for example, the information processing device 20 may be arranged inside the housing 12 of the electronic device 10 .

-Hardware configuration-
Next, the hardware configuration of the control unit 30 of the first embodiment, the second embodiment, and the modification will be described.

FIG. 10 is a block diagram showing a hardware configuration example of the control unit 30 and the control unit 31 of the above embodiment and modification.

The control unit 30 and the control unit 31 of the above-described embodiment and modifications include a control device such as a CPU (Central Processing Unit) 51 and a storage device such as a ROM (Read Only Memory) 52 and a RAM (Random Access Memory) 53. , a communication I/F 54 that connects to a network and performs communication, and a bus 61 that connects each unit.

The programs executed by the control unit 30 and the control unit 31 of the above-described embodiment and modified example are pre-installed in the ROM 52 or the like and provided.

The programs executed by the control unit 30 and the control unit 31 of the above-described embodiment and modifications are stored as files in an installable format or an executable format on a CD-ROM (Compact Disk Read Only Memory), a flexible disk (FD). , CD-R (Compact Disk Recordable), DVD (Digital Versatile Disk), or other computer-readable recording medium, and provided as a computer program product.

Furthermore, the programs executed by the control unit 30 and the control unit 31 of the above embodiment and modifications are stored on a computer connected to a network such as the Internet, and are provided by being downloaded via the network. You may Further, the programs executed by the control unit 30 and the control unit 31 of the above embodiments and modifications may be provided or distributed via a network such as the Internet.

The programs executed by the control unit 30 and the control unit 31 of the above embodiments and modifications can cause a computer to function as each unit of the control unit 30 and the control unit 31 of the above embodiments and modifications. In this computer, the CPU 51 can read a program from a computer-readable storage medium into the main memory and execute it.

While several embodiments and variations of the invention have been described, these embodiments and variations are provided by way of example and are not intended to limit the scope of the invention. These novel embodiments and modifications can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the scope of the invention described in the claims and equivalents thereof.

1, 1A Abnormality detection device 10 Electronic device 14 Parts 14A, 14A1, 14A2, 14A3 Heat-generating parts 16A Environment sensors 20, 21 Information processing device 30B First calculators 30C, 31C Judgment units 30D, 31D Output controller 31E Second Calculation unit 31F Third calculation unit

Claims (8)

A heat-generating component provided in an electronic device, driven by electric power, and having a variable heat generation amount;
a plurality of environment sensors arranged at different positions for detecting the environment of the heat-generating component;
a first calculator that calculates a first estimated value indicating the amount of heat generated by the heat-generating component according to a first detected value of each of the plurality of environment sensors;
a determination unit that determines that the heat-generating component is abnormal when the first estimated value is equal to or greater than a first threshold;
with
The environment sensor includes a sensor that detects the number of rotations or the amount of power of a component other than the heat-generating component provided in the electronic device,
The first calculator,
A value detected by the environmental sensor of a power amount or rotation speed of a component other than the heat-generating component provided in the electronic device, and a detected value of the temperature of the heat-generating component detected by the environment sensor are used as the first detected values. obtaining and calculating the first estimated value according to the first detected value;
Anomaly detector. 2. The abnormality detection device according to claim 1, wherein the amount of heat generated by said heat-generating component and the power consumption of said electronic device exhibit a non-linear relationship. The environment sensor detects the environment other than the current value of the heat-generating component, the voltage value of the heat-generating component, and the usage rate of the heat-generating component.
The abnormality detection device according to claim 1 or 2. The abnormality detection device according to any one of claims 1 to 3, wherein the heat-generating component is a processor. The first calculation unit
calculating the first estimated value of the heat generation amount of the heat-generating component, which is a factor affecting the abnormality in the specific region of the electronic device, according to the plurality of first detection values;
The abnormality detection device is
a second calculation unit that calculates an evaluation value of the factor other than the heat-generating component, which is a factor affecting the abnormality in the specific area, according to the first detection value of each of the plurality of environment sensors; prepared,
a third calculator that calculates a second estimated value of the temperature of the specific region based on the first estimated value and the evaluation value;
with
The determination unit is
If the second estimated value is greater than or equal to a second threshold, determine that the specific region is abnormal;
The abnormality detection device according to any one of claims 1 to 4. The first calculator,
calculating the first estimated value according to three or more of the first detected values;
The abnormality detection device according to any one of claims 1 to 5. A computer-implemented anomaly detection method comprising:
The amount of heat generated by the heat-generating component is detected according to the first detection value of each of a plurality of environment sensors arranged in different positions, which detect the environment of the heat-generating component which is provided in the electronic device and driven by electric power to change the heat generation. a calculating step of calculating a first estimated value indicating
a determination step of determining that the heat-generating component is abnormal when the first estimated value is equal to or greater than a first threshold;
including
The environment sensor includes a sensor that detects the number of rotations or the amount of power of a component other than the heat-generating component provided in the electronic device,
The calculating step includes:
A value detected by the environmental sensor of a power amount or rotation speed of a component other than the heat-generating component provided in the electronic device, and a detected value of the temperature of the heat-generating component detected by the environment sensor are used as the first detected values. obtaining and calculating the first estimated value according to the first detected value;
Anomaly detection method. The amount of heat generated by the heat-generating component is detected according to the first detection value of each of a plurality of environment sensors arranged in different positions, which detect the environment of the heat-generating component which is provided in the electronic device and driven by electric power to change the heat generation. a calculating step of calculating a first estimated value indicating
a determination step of determining that the heat-generating component is abnormal when the first estimated value is equal to or greater than a first threshold;
A program for causing a computer to execute
The environment sensor includes a sensor that detects the number of rotations or the amount of power of a component other than the heat-generating component provided in the electronic device,
The calculating step includes:
A value detected by the environmental sensor of a power amount or rotation speed of a component other than the heat-generating component provided in the electronic device, and a detected value of the temperature of the heat-generating component detected by the environment sensor are used as the first detected values. obtaining and calculating the first estimated value according to the first detected value;
program.

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