Patent Description:
Conventionally, in order to diagnose an anomaly (malfunction, failure, deterioration) in a component or the like of a device, a technology for generating a learned model that performs a diagnosis unique to the device based on the data of the device has been known (see, e.g., Patent Document <NUM>).

<CIT> and <CIT> disclose systems and methods for determining whether a learned model needs to be generated or not.

However, in order to generate a learned model for each device, processing costs, etc., will arise. The purpose of the present disclosure is to provide a technology that enables appropriate determination of anomaly of a device.

Accordingly, anomaly determination of the device can be appropriately performed.

Each embodiment will be described below with reference to the drawings. In this specification and the drawings, with respect to elements having substantially the same functional configuration, duplicate descriptions are omitted by assigning identical symbols.

First, the system configuration of an information processing system <NUM> will be described. <FIG> illustrates an example of the system configuration of the information processing system <NUM> according to the embodiment. As illustrated in <FIG>, the information processing system <NUM> includes an information processing apparatus 10A, an information processing apparatus 10B, and an information processing apparatus 10C (hereafter, when it is not necessary to distinguish each of these from each other, simply referred to as "the information processing apparatus <NUM>"). The information processing system <NUM> also includes a device 20A, a device 20B, a device 20C, and a device 20D (hereafter, when it is not necessary to distinguish each of these from each other, simply referred to as the "device <NUM>"). The numbers of the information processing apparatuses <NUM> and the devices <NUM> are not limited to the example in <FIG>.

The information processing apparatus <NUM> and the device <NUM> may be connected so as to be able to communicate via, for example, a network NW such as the Internet, a wireless LAN (Local Area Network), a LAN, a mobile phone network such as LTE (Long Term Evolution) and <NUM>, a signal line, or the like. The device <NUM> may be installed in, for example, a residence, an office, a public facility, or the like. The information processing apparatus <NUM> may be, for example, a server on a cloud. Further, the information processing apparatus <NUM> may be, for example, an edge server installed in a building where multiple devices <NUM> are installed. Further, the information processing apparatus <NUM> may be housed in, for example, the device <NUM> (for example, the indoor housing of an air conditioner). The information processing apparatus 10A, the information processing apparatus 10B and the information processing apparatus 10C may be the same apparatus.

The device <NUM> may be, for example, a variety of devices such as air conditioners, refrigerators, water heaters, lighting, and the like, and may have an Internet of Things (IoT) device that transmits various kinds of measured information to the information processing apparatus <NUM>.

Next, the hardware configuration of the information processing apparatus <NUM> according to the embodiment will be described. <FIG> illustrates an example of the hardware configuration of the information processing apparatus <NUM> according to the embodiment. As illustrated in <FIG>, the information processing apparatus <NUM> includes a CPU (Central Processing Unit) <NUM>, a ROM (Read Only Memory) <NUM>, and a RAM (Random Access Memory) <NUM>. The CPU <NUM>, the ROM <NUM>, and the RAM <NUM> form what is referred to as a computer. Further, the information processing apparatus <NUM> includes an auxiliary storage device <NUM>, a display device <NUM>, an operation device <NUM>, an I/F (Interface) device <NUM>, and a drive device <NUM>. The pieces of hardware of the information processing apparatus <NUM> are connected to each other via a bus <NUM>.

The CPU <NUM> is an arithmetic device that executes various programs (e.g., machine learning programs, etc.) installed in the auxiliary storage device <NUM>. The ROM <NUM> is a non-volatile memory. The ROM <NUM> functions as a main storage device and stores various programs, data, etc., necessary for the CPU <NUM> to execute various programs installed in the auxiliary storage device <NUM>. Specifically, the ROM <NUM> stores boot programs, such as BIOS (Basic Input/Output System) and EFI (Extensible Firmware Interface).

The RAM <NUM> is a volatile memory, such as DRAM (Dynamic Random Access Memory) and SRAM (Static Random Access Memory). The RAM <NUM> functions as a main storage device and provides a work area that is expanded when various programs installed in the auxiliary storage device <NUM> are executed by the CPU <NUM>.

The auxiliary storage device <NUM> stores various programs and information used when various programs are executed.

The display device <NUM> is a display device for displaying various kinds of information. The operation device <NUM> is an operation device for receiving various operations. The I/F device <NUM> is a communication device that communicates with external devices.

The drive device <NUM> is a device for setting the recording medium <NUM>. The recording medium <NUM> here includes media for recording information optically, electrically or magnetically, such as a CD-ROM, a flexible disk, a magneto-optical disk, etc. Further, the recording medium <NUM> may include a semiconductor memory, etc., for electrically recording information, such as ROM, flash memory, etc..

Various programs installed in the auxiliary storage device <NUM> are installed, for example, when the distributed recording medium <NUM> is set in the drive device <NUM> and various programs recorded in the recording medium <NUM> are read by the drive device <NUM>. Alternatively, various programs installed in the auxiliary storage device <NUM> may be installed by being downloaded from a network (not illustrated).

Next, the configuration of the device <NUM> according to the embodiment will be described. An example in which the device <NUM> is an air conditioner will be described below.

<FIG> illustrates an example of the configuration of an outdoor unit <NUM> of the device <NUM> according to the embodiment. <FIG> illustrates an example of the configuration of an indoor unit <NUM> of the device <NUM> according to the embodiment.

The device <NUM>, which is an air conditioner, includes a control device <NUM> that controls each unit of the device <NUM>. The control device <NUM> may be built into, for example, the outdoor unit <NUM> or the indoor unit <NUM> of the device <NUM>. The control device <NUM> of the device <NUM> may be implemented by a microcontroller in which the CPU, a memory, and an input/output unit are built into one integrated circuit. Further, the control device <NUM> of the device <NUM> may be implemented by a circuit such as an ASIC (Application Specific Integrated Circuit), a DSP (digital signal processor), an FPGA (field programmable gate array), or the like. Further, the control device <NUM> of the device <NUM> may be implemented by the computer illustrated in <FIG> above.

Further, the device <NUM> is equipped with various devices configuring a refrigerant circuit and various sensors. Each device configuring the refrigerant circuit is controlled by the control device <NUM>. The refrigerant circuit is, for example, a closed circuit filled with a refrigerant such as chlorofluorocarbon. The refrigerant circuit may be configured, for example, such that the refrigerant circulates to perform a vapor compression type refrigeration cycle.

In the example illustrated in <FIG>, the outdoor unit <NUM> of the device <NUM> includes a compressor <NUM>, an outdoor heat exchanger <NUM>, an electronic expansion valve <NUM>, an electronic expansion valve <NUM>, an electromagnetic valve <NUM> (oil separator oil return), an electromagnetic valve <NUM> (oil separator bypass), an electromagnetic valve <NUM> (accumulator oil return), a four-way switching valve <NUM>, a high-pressure sensor <NUM>, a low-pressure sensor <NUM>, a high-pressure switch <NUM>, a pressure adjustment valve <NUM>, a supercooling heat exchanger <NUM>, a capillary tube <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, a refrigerant cooling plate <NUM>, etc..

The compressor <NUM> compresses and discharges the taken in refrigerant. For example, by changing the rotational speed of the motor (rotational speed of the compressor <NUM>) by changing the frequency of the alternating current supplied from the inverter of the compressor <NUM> to the motor (not illustrated), the capacity of the compressor <NUM> can be changed. The rotational speed may be, for example, the number of rotations per unit time.

In the outdoor heat exchanger <NUM> (air heat exchanger), the outdoor air taken in by the outdoor fan and the refrigerant exchange heat. The opening (hole size) of the electronic expansion valve <NUM> (main) and the electronic expansion valve <NUM> (injection) is adjusted, for example, by the valve element being driven by a pulse motor. The electronic expansion valve <NUM> is controlled so that the outlet overheat of the outdoor heat exchanger becomes constant during a heating operation. The electronic expansion valve <NUM> is controlled so that the outlet overheat of the supercooling heat exchanger <NUM> becomes constant.

The electromagnetic valve <NUM> (oil separator oil return) and the electromagnetic valve <NUM> (oil separator bypass) control the amount of oil returned from an oil separator <NUM> to the compressor <NUM>. The electromagnetic valve <NUM> (accumulator oil return) is used to return oil from an accumulator <NUM> to the compressor <NUM>. The four-way switching valve <NUM> switches between cooling and heating operations.

The high pressure sensor <NUM> is a sensor that detects high pressure. The high pressure is the high pressure in the refrigeration cycle of the device <NUM> (hereafter, also referred to simply as "high pressure" as appropriate), and may be, for example, the pressure of the refrigerant compressed and discharged by the compressor <NUM> (the discharge pressure of the compressor <NUM>) or the pressure of the refrigerant in a condenser.

The low pressure sensor <NUM> is a sensor that detects low pressure. The low pressure is the low pressure in the refrigeration cycle of the device <NUM> (hereafter, also referred to simply as "low pressure" as appropriate), and may be, for example, the pressure of the refrigerant taken into the compressor <NUM> (the pressure of the refrigerant before being compressed into the compressor <NUM>).

The high pressure switch <NUM> stops the operation of the device <NUM> when the pressure is above a predetermined pressure in order to avoid the increase of the high pressure when the compressor <NUM> has an anomaly.

The pressure adjustment valve <NUM> opens the liquid tube when the pressure is above the predetermined pressure to avoid the increase of the high pressure in order to prevent the breakage of the functional components due to the increase of the pressure during transportation and storage.

The supercooling heat exchanger <NUM> supercools the liquid refrigerant.

The capillary tube <NUM> returns the refrigerating machine oil separated by the oil separator <NUM> to the compressor <NUM>.

The thermistor <NUM> is a sensor (temperature sensor) that detects (measures) the outside air temperature. The outside air temperature measured by the thermistor <NUM> may be used, for example, to correct the discharge tube temperature.

The thermistor <NUM> is a sensor that detects the discharge tube temperature indicating the temperature of the refrigerant discharged from the compressor <NUM>. The discharge tube temperature measured by the thermistor <NUM> may be used, for example, for temperature protection control, etc., of the compressor <NUM>.

The thermistor <NUM> is a sensor for detecting the gas tube temperature at the inlet of the accumulator <NUM>. The gas tube temperature measured by the thermistor <NUM> may be used, for example, for control to keep the intake superheating level constant during heating. The thermistor <NUM> is a sensor for detecting the liquid tube temperature of the outdoor heat exchanger <NUM>. The liquid tube temperature measured by the thermistor <NUM> may be used, for example, for overfill determination, etc., during test operation. The thermistor <NUM> is a sensor for detecting the liquid tube temperature of the supercooling heat exchanger <NUM>. The thermistor <NUM> is a sensor for detecting the evaporation-side gas tube temperature of the supercooling heat exchanger <NUM>. The evaporation-side gas tube temperature measured by the thermistor <NUM> may be used, for example, to control the overheat at the outlet of the supercooling heat exchanger <NUM> to a constant value, etc..

The thermistor <NUM> is a sensor that detects the liquid tube temperature of the outdoor heat exchanger <NUM>. The liquid tube temperature measured by the thermistor <NUM> may be used, for example, to determine a defrost operation. The thermistor <NUM> is a sensor that detects the surface temperature of the compressor <NUM>. The control device <NUM> may stop the operation of the device <NUM> when the surface temperature exceeds the threshold value in order to avoid the temperature of the compressor <NUM> rising in the event of an anomaly. The thermistor <NUM> is a sensor that detects the inlet tube temperature indicating the temperature of the refrigerant taken into the compressor <NUM>. The refrigerant cooling plate <NUM> is a cooling plate for cooling the refrigerant liquid.

The accumulator <NUM> is, for example, a device that separates the liquid and gas of the refrigerant taken into the compressor <NUM>, and allows only the gas to be taken into the compressor <NUM>. The outdoor unit <NUM> may have a receiver (refrigerant quantity adjustment container) in place of or in addition to the accumulator <NUM>. The receiver is a container for temporarily storing the refrigerant liquid condensed by the condenser. The receiver can temporarily store the refrigerant liquid when, for example, the amount of refrigerant in the evaporator changes due to variations in air conditioning load.

In the example of <FIG>, the indoor unit <NUM> ( indoor unit) of the device <NUM> includes an electronic expansion valve <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, a thermistor <NUM>, an indoor heat exchanger <NUM>, etc..

The electronic expansion valve <NUM> may be used for gas superheating control during a cooling operation and for supercooling control during a heating operation. The thermistor <NUM> is a sensor that detects the temperature (indoor temperature) of air taken in by the fan of the indoor heat exchanger <NUM>. The thermistor <NUM> is a sensor that detects the liquid tube temperature between the electronic expansion valve <NUM> and the indoor heat exchanger <NUM>. The liquid tube temperature measured by the thermistor <NUM> may be used, for example, for gas superheating control during a cooling operation, for supercooling control during a heating operation, etc..

The thermistor <NUM> is a sensor that detects the gas tube temperature between the indoor heat exchanger <NUM> and the gas-side piping to the outdoor unit <NUM>. The gas tube temperature measured by the thermistor <NUM> may be used, for example, to control the degree of gas superheating during a cooling operation. The thermistor <NUM> is a sensor that detects the temperature of air blown out of the indoor heat exchanger <NUM>.

In the indoor heat exchanger <NUM>, the refrigerant exchanges heat with the indoor air taken in by the indoor fan. The indoor fan may be, for example, a cylindrical fan (cross-flow fan) that takes in air through the inlet and discharges air through the outlet by rotating an impeller tilted forward in the direction of rotation. With the rotation of the indoor fan, the indoor air is taken into the indoor unit, and air adjusted in temperature or the like is discharged into the room.

During the cooling operation, the four-way switching valve <NUM> is set to the first state. When the compressor <NUM> is operated in this state, the outdoor heat exchanger <NUM> becomes a condenser (radiator) and the indoor heat exchanger <NUM> becomes an evaporator to perform the refrigeration cycle. In this case, the refrigerant discharged from the compressor <NUM> flows to the outdoor heat exchanger <NUM> and radiates heat to the outdoor air. The refrigerant that has radiated heat then expands (upon being decompressed) when passing through the electronic expansion valve <NUM> of the indoor unit <NUM> and flows to the indoor heat exchanger <NUM>. In the indoor heat exchanger <NUM>, the refrigerant absorbs heat from the indoor air and evaporates, and the cooled indoor air is supplied to the room. The evaporated refrigerant is taken into the compressor <NUM> and compressed.

During the heating operation, the four-way switching valve <NUM> is set to the second state. When the compressor <NUM> is operated in this state, the indoor heat exchanger <NUM> becomes a condenser (radiator) and the outdoor heat exchanger <NUM> becomes an evaporator, and the refrigeration cycle is performed. In this case, the refrigerant discharged from the compressor <NUM> flows to the indoor heat exchanger <NUM> and radiates heat to the indoor air. This supplies heated indoor air into the room. The refrigerant that has radiated heat expands (upon being decompressed) as the refrigerant passes through the electronic expansion valve <NUM>. The refrigerant expanded by the electronic expansion valve <NUM> flows to the outdoor heat exchanger <NUM>, absorbs heat from the outdoor air, and evaporates. The evaporated refrigerant is taken into the compressor <NUM> and is compressed.

Next, referring to <FIG>, the functional configuration of the control device <NUM> of the device <NUM> according to the embodiment will be described. <FIG> is a diagram illustrating an example of the functional configuration of the control device <NUM> of the device <NUM> according to the embodiment.

The control device <NUM> of the device <NUM> includes an acquiring unit <NUM>, a determining unit <NUM>, an inference unit <NUM>, a generating unit <NUM>, an anomaly determining unit <NUM>, and a control unit <NUM>. These units may be implemented by the cooperation of, for example, one or more programs installed in the device <NUM> and the CPU or the like of the device <NUM>.

The acquiring unit <NUM> acquires various kinds of data. The acquiring unit <NUM> acquires, for example, actual measurement values of values relating to components included in the device <NUM> from various sensors of the device <NUM>.

The determining unit <NUM> performs various determinations. The determining unit <NUM> determines whether to generate a learned model that estimates a value relating to a component included in the device <NUM>.

The inference unit <NUM> calculates an estimation value of a value relating to a component included in the device <NUM> based on a predetermined estimation method that is set in advance (hereinafter also referred to as the "default inference model" as appropriate), or a learned model specific (unique) to the device <NUM> generated by the generating unit <NUM>.

When it is determined by the determining unit <NUM> that a learned model is to be generated to estimate a value relating to a component included in the device <NUM>, the generating unit <NUM> generates the learned model by performing machine learning based on the data of the device <NUM>.

The anomaly determining unit <NUM> detects an anomaly in a component based on the actual measurement value of the value relating to the component included in the device <NUM> acquired by the acquiring unit <NUM> and the estimation value of the value relating to the component calculated by the inference unit <NUM>. The control unit <NUM> controls each unit of the device <NUM>.

An example of processing of the control device <NUM> of the device <NUM> according to the embodiment will be described with reference to <FIG> is a flow chart illustrating an example of processing of the control device <NUM> of the device <NUM> according to the embodiment.

In step S1, the determining unit <NUM> determines whether to generate a learned model that estimates values relating to the components included in the device <NUM>. The values relating to the components included in the device <NUM> may include, for example, measurement values measured by each of the sensors of the device <NUM>. In this case, the values relating to the components included in the device <NUM> may include, for example, at least one of the following: the outside air temperature, the inlet tube temperature, the discharge tube temperature, the heat exchanger temperature, the supercooling heat exchange outlet temperature, the receiver liquid tube temperature, the accumulator inlet temperature, the high pressure, the low pressure, the inverter current value, the inverter rotational speed, and the electromagnetic valve opening degree of the outdoor unit.

The outside air temperature may be, for example, the outside air temperature measured by the thermistor <NUM>. The intake tube temperature is, for example, the temperature of the refrigerant taken into the compressor <NUM> measured by the thermistor <NUM>. The discharge tube temperature is, for example, the temperature of the refrigerant discharged from the compressor <NUM> measured by the thermistor <NUM>.

The heat exchange temperature may be, for example, the liquid tube temperature of the outdoor heat exchanger <NUM> measured by the thermistor <NUM>. The supercooling heat exchange outlet temperature may be, for example, at least one of the liquid tube temperature of the supercooling heat exchanger <NUM> measured by the thermistor <NUM> and the evaporation-side gas tube temperature of the supercooling heat exchanger <NUM> measured by the thermistor <NUM>.

The receiver fluid tube temperature may be, for example, the fluid tube temperature measured by a sensor (thermistor) that detects the fluid tube temperature at the inlet of the receiver when the outdoor unit <NUM> has a receiver (refrigerant quantity adjustment container) instead of or in addition to the accumulator <NUM>. The accumulator inlet temperature may be the gas tube temperature at the inlet of the accumulator <NUM> as measured by the thermistor <NUM>.

The high pressure may be, for example, the high pressure measured by the high pressure sensor <NUM>. The low pressure may be, for example, the low pressure measured by the low pressure sensor <NUM>. The inverter current value is, for example, the current value supplied to the inverter that changes the rotational speed of the motor of the compressor <NUM>. The inverter rotational speed may be, for example, the number of revolutions per unit time (rotational speed) of the motor of the compressor <NUM>. The electromagnetic valve opening of the outdoor unit is, for example, the opening of the electromagnetic valve <NUM>, the electromagnetic valve <NUM>, the electromagnetic valve <NUM>, etc..

Here, the determining unit <NUM> may determine whether to generate the learned model based on at least one of the measurement values measured by each of the sensors of the device <NUM>, the anomaly diagnosis result of the device <NUM>, the result of the control on the device <NUM>, and the conditions relating to the device <NUM>.

The determining unit <NUM> may determine the environment in which the device <NUM> is used based on the measurement values measured by each of the sensors of the device <NUM>, and when the environment satisfies a predetermined condition, the determining unit <NUM> may determine that a learned model is to be generated. In this case, the determining unit <NUM> may determine that the learned model is to be generated if, for example, the transition of the outside air temperature measured by the thermistor <NUM> satisfies a predetermined condition. This can reduce the decrease in the accuracy of anomaly diagnosis caused by the use of a default inference model when, for example, the device <NUM> is installed in an extremely cold area or a subtropical location.

The determining unit <NUM> may determine that the learned model is to be generated when the information on the property where the device <NUM> is installed satisfies a predetermined condition. The information on the property where the device <NUM> is installed may be set in the device <NUM> by a worker, etc., when the device <NUM> is installed on the property, for example. This can reduce the decrease in the accuracy of anomaly diagnosis caused by the use of a default inference model when, for example, the device <NUM> is installed in an extremely cold area or a subtropical location.

The determining unit <NUM> may also determine that the learned model is to be generated when the machine type of the device <NUM> satisfies a predetermined condition. This can reduce the decrease in the accuracy of anomaly diagnosis caused by the use of a default inference model when, for example, when the device <NUM> is a machine type with low air-conditioning capacity, and the device <NUM> is installed in an extremely cold area or a subtropical location, the property has extremely high or low insulation performance, or the property is a store with a large number of visitors, etc..

In the processing of step S1, the determining unit <NUM> may first cause the inference unit <NUM> to estimate the value relating to the component of the device <NUM> by a predetermined estimation method based on at least one of the measurement values measured by each of the sensors of the device <NUM>, the anomaly diagnosis result of the device <NUM>, and the control result for the device <NUM>. The predetermined estimation method may be, for example, an estimation method for each machine type of the device <NUM> that is set in advance at the time of factory shipment of the device <NUM>. The predetermined estimation method may be, for example, a method using AI (Artificial Intelligence) that estimates values relating to components of the device <NUM> in an average usage environment of the device <NUM>.

Measurement values measured by each of the sensors of the device <NUM> may include, for example, a history of values relating to the components of the device <NUM> described above. In this case, the determining unit <NUM> may determine that the learned model is to be generated if the error (degree of divergence) between the value estimated by the inference unit <NUM> according to a predetermined estimation method and the measurement value of the value relating to the components of the device <NUM> is greater than or equal to a threshold.

The anomaly diagnosis results of the device <NUM> may include a set of information, including a history of measurement values measured made by each of the sensors of the device <NUM> and information indicating the results of the anomaly diagnosis set by the maintenance personnel (field engineer), etc. In this case, the determining unit <NUM> may determine that a learned model is to be generated if, for example, the degree of divergence between the anomaly determination result obtained by the anomaly determining unit <NUM> based on the value estimated by the inference unit <NUM> according to the predetermined estimation method and the actual measurement value measured by each of the sensors of the device <NUM>, and the anomaly diagnosis result set by the maintenance personnel, etc., is greater than or equal to a threshold.

The control result for the device <NUM> may include a control signal (an instruction) from the control device <NUM> to each unit of the device <NUM>, the operation mode and set temperature set by the user by a remote control, etc., and a history of measurement values measured by each of the sensors of the device <NUM>. In this case, the determining unit <NUM> may determine that the learned model is to be generated if, for example, the error (degree of divergence) between the actual measurement value of the feedback value for the control signal and the feedback value estimated by the inference unit <NUM> by the predetermined estimation method is greater than or equal to a threshold.

When it is determined that the learned model is to be generated (YES in step S1), the generating unit <NUM> generates the learned model unique to the device <NUM> by machine learning (step S2).

Here, the generating unit <NUM> may generate the learned model based on the learning data acquired when the components of the device <NUM> are normal.

In this case, the generating unit <NUM> may use the data acquired by the acquiring unit <NUM> within a predetermined period (for example, within two years) from the date and time when the device <NUM> is installed as learning data acquired when the components of the device <NUM> are normal. Thus, appropriate learning can be performed, even in a case where, for example, the failure rate of the components of the device <NUM> follows the bathtub curve (failure rate curve), and after a certain period of time passes to enter a wear failure period, the failure rate rapidly increases due to aging deterioration, etc..

In this case, the generating unit <NUM> may record, for example, the date and time when the device <NUM> is first started by the user as the date and time when the device <NUM> is installed. Also, the date and time when the device <NUM> is installed may be set by a worker or the like.

The generating unit <NUM> may use, as the learning data, a data set in which at least one of the measurement values measured by each of the sensors of the device <NUM>, the anomaly diagnosis result of the device <NUM>, and the control result for the device <NUM> is used as an explanatory variable, and the measurement value of the value relating to the component is used as the correct answer label. The generating unit <NUM> may perform machine learning by, for example, neural network, support vector machine (SVM), logistic regression, random forest, k-nearest neighbors, etc..

Then, the anomaly determining unit <NUM> determines (diagnoses) the anomaly of the components, etc., of the device <NUM> based on the measurement values measured by each of the sensors of the device <NUM> (step S3).

First, the anomaly determining unit <NUM> causes the inference unit <NUM> to calculate (inference, estimate) the value relating to the components included in the device <NUM>. Here, when the learned model is generated by the generating unit <NUM>, the anomaly determining unit <NUM> causes the inference unit <NUM> to calculate (inference, estimate) the value relating to the components included in the device <NUM> by using the learned model. On the other hand, when the learned model is not generated, the anomaly determining unit <NUM> causes the inference unit <NUM> to calculate the value relating to the components included in the device <NUM> by using the predetermined estimation method described above.

Then, when the error between the values relating to the components included in the device <NUM> calculated by the inference unit <NUM> and the actual measurement values of the values relating to the components measured by each of the sensors of the device <NUM> is greater than or equal to a threshold, the anomaly determining unit <NUM> may determine that the components have an anomaly.

The determining unit <NUM> may determine which of the default inference model and the learned model unique to the device <NUM> generated by the generating unit <NUM> is to be used in order to cause the anomaly determining unit <NUM> to perform the anomaly determination. Thus, when, for example, the accuracy of the estimation by the learned model is insufficient due to lack of learning data, the anomaly determination using a default inference model can be performed.

In this case, the determining unit <NUM> may calculate the first error between the first estimation value of the value relating to the component of the device <NUM> estimated by the inference unit <NUM> by using the predetermined estimation method and the actual measurement value of the value relating to the component. Then, the determining unit <NUM> may calculate the second error between the second estimation value of the value relating to the component estimated by the learned model by the inference unit <NUM> and the actual measurement value of the value relating to the component.

Then, on the basis of the first estimation value and the second estimation value, the determining unit <NUM> may determine which inference model among the predetermined estimation method and the estimation method using the learned model is to be used to cause the anomaly determining unit <NUM> to perform the anomaly determination. Then, the anomaly determining unit <NUM> may cause the inference unit <NUM> to perform the inference by using the inference model determined by the determining unit <NUM>.

(Slackening anomaly determination until learned model is generated).

When it is determined by the determining unit <NUM> in the processing of step S1 that the anomaly determining unit <NUM> generates a learned model for estimating the value relating to the component included in the device <NUM>, for example, the anomaly determination unit may slacken the anomaly determination for the device <NUM> until the learned model is generated. Thus, erroneous anomaly determination can be reduced by inference using a default inference model that is determined to have low accuracy of anomaly determination.

In this case, when it is determined by the determining unit <NUM> that a learned model is not to be generated (NO in step S1), and when the error between the estimation value and the measurement value of the value relating to the component of the device <NUM> is greater than or equal to the first threshold, the anomaly determining unit <NUM> determines that the component has an anomaly. On the other hand, when the error is not greater than or equal to the first threshold, the anomaly determining unit <NUM> may determine that the component does not have an anomaly.

When the determining unit <NUM> determines that a learned model is to be generated (YES in step S1), the anomaly determining unit <NUM> determines that the component has an anomaly when the error between the estimation value and the measurement value of the value relating to the component of the device <NUM> is greater than or equal to the second threshold, which is greater than the first threshold. On the other hand, when the error is not greater than or equal to the second threshold, the anomaly determining unit <NUM> may determine that the component does not have an anomaly.

Subsequently, the anomaly determining unit <NUM> outputs the anomaly determination result of the device <NUM> (step S4). Here, the anomaly determining unit <NUM> may display the anomaly determination result on the display unit of the remote control or the like of the device <NUM>. The anomaly determining unit <NUM> may also notify the anomaly determination result to an external device such as the information processing apparatus 10A.

In the above described embodiment, an example of generating a learned model (machine learning) and performing inference at the device <NUM> has been described. Alternatively, a configuration in which the generation and inference of a learned model are each performed by either the information processing apparatus 10A or the information processing apparatus 10B, respectively, may be adopted.

Each functional unit of the control device <NUM> of the device <NUM>, the information processing apparatus 10A, and the information processing apparatus 10B may be implemented by cloud computing provided by, for example, one or more computers.

Claim 1:
An information processing apparatus (<NUM>) comprising:
a determining unit (<NUM>) configured to determine whether to generate a learned model that estimates a value relating to a component included in a device (<NUM>), based on at least one of a measurement value measured by each sensor of the device, an anomaly diagnosis result of the device (<NUM>), a result of controlling the device (<NUM>), or a condition relating to the device (<NUM>), an estimating unit configured to estimate the value relating to the component by a predetermined estimation method, based on at least one of the measurement value measured by each sensor of the device (<NUM>), the anomaly diagnosis result of the device (<NUM>), or the result of controlling the device (<NUM>), wherein
the determining unit (<NUM>) is configured to determine to generate the learned model, when an error between the value estimated by the estimating unit and an actual measurement value of the value relating to the component is greater than or equal to a threshold, the information processing apparatus (<NUM>) being characterized by: an anomaly determining unit (<NUM>) configured to determine that the component has an anomaly, when the determining unit (<NUM>) determines not to generate the learned model and the error between the estimation value and the actual measurement value of the value relating to the component is greater than or equal to a first threshold, and
determine that the component has an anomaly, when the determining unit (<NUM>) determines to generate the learned model and the error between the estimation value and the actual measurement value of the value relating to the component is greater than or equal to a second threshold that is greater than the first threshold.