Information processing device, information processing method, and power conversion device

Abstract

To detect an abnormality more properly.SOLUTION: Provided is an information processing device including an acquisition unit that acquires information indicating a temperature transition in a semiconductor element of a power conversion device driving an electric motor and information indicating a transition of current being inputted to the electric motor, and an estimation unit that estimates an abnormality degree of a specific type in at least either one of the power conversion device and the electric motor on the basis of the information acquired by the acquisition unit.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a power conversion device. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there is known a technique for periodically monitoring an abnormality in a power conversion device (inverter) (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-078728 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional technology, for example, there are cases where an abnormality in a power conversion device or an electric motor cannot be appropriately detected. Other problems and novel features will become apparent from the description of this specification and the accompanying drawings. [Means for solving the problem]

[0005] In one embodiment of the present disclosure, an information processing device is provided that has an acquisition unit that acquires information indicating the change in temperature in a semiconductor element of a power conversion device that drives an electric motor and information indicating the change in current input to the electric motor, and an estimation unit that estimates a specific type of degree of abnormality in at least one of the power conversion device and the electric motor based on the information acquired by the acquisition unit.

[0006] In addition, in one embodiment of the present disclosure, an information processing method is provided in which an information processing device executes a process to acquire information indicating the change in temperature in a semiconductor element of a power conversion device that drives an electric motor and information indicating the change in current input to the electric motor, and estimates a specific type of degree of abnormality in at least one of the power conversion device and the electric motor based on the acquired information.

[0007] In addition, in one embodiment of the present disclosure, there is provided a power conversion device that drives an electric motor, the power conversion device having an acquisition unit that acquires information indicating a change in temperature in a semiconductor element of the power conversion device and information indicating a change in a current input to the electric motor, and an estimation unit that estimates a specific type of degree of abnormality in at least one of the power conversion device and the electric motor based on the information acquired by the acquisition unit. Effect of the Invention

[0008] According to one aspect, anomalies can be detected more appropriately. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of a drive system according to an embodiment. [Diagram 2] FIG. 1 is a diagram illustrating an example of a configuration of an information processing device according to an embodiment. [Diagram 3] 10 is a flowchart illustrating an example of processing of the information processing device according to the embodiment. [Figure 4] FIG. 13 is a diagram showing an example of a spectrum when an abnormality occurs in the semiconductor element according to the embodiment. [Diagram 5] FIG. 13 is a diagram illustrating an example of information recorded in an abnormality degree coefficient table according to the embodiment. [Figure 6] FIG. 11 is a diagram showing an example of the power cycle life (number of power cycles) according to the temperature difference of the semiconductor element 21 according to the embodiment. [Figure 7] FIG. 4 is a diagram illustrating an example of an abnormality degree coefficient Fi according to the embodiment. [Figure 8]FIG. 2 is a diagram showing an example of a frequency Di of each measurement value i according to the embodiment. [Figure 9] FIG. 11 is a diagram showing an example of information recorded in correlation definition information according to the embodiment. [Figure 10] FIG. 13 is a diagram illustrating an example of information recorded in an abnormality test frequency table according to the embodiment. [Figure 11] FIG. 1 is a diagram illustrating an example of a configuration of an information processing device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The principles of the present disclosure are described with reference to some exemplary embodiments. It should be understood that these embodiments are set forth for illustrative purposes only, to aid those skilled in the art in understanding and practicing the present disclosure, without implying any limitation on the scope of the present disclosure. The disclosure described herein may be implemented in various ways other than those described below.

[0011] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. <Configuration> The configuration of a drive system 1 according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration of the drive system 1 according to an embodiment. In the example of Fig. 1, the drive system 1 has a power conversion device 20, an electric motor 30, and a vibration sensor 40.

[0013] The electric motor 30 is a machine that converts electrical energy into mechanical energy. The power conversion device 20 is a device that generates AC of a different frequency from DC or AC to drive the electric motor 30 at a desired rotation speed and torque.

[0014] The vibration sensor 40 is provided near the electric motor 30 and measures vibrations near the electric motor 30. The vibration sensor 40 may be, for example, a piezoelectric sensor or a MEMS sensor that detects acceleration. The vibration sensor 40 may be, for example, a laser Doppler sensor that irradiates laser light and detects speed based on a change in frequency of the reflected laser light. The vibration sensor 40 may be, for example, a capacitance sensor that measures a displacement (change in distance) between the sensor and a measurement object based on a change in capacitance between the sensor and the measurement object. The vibration sensor 40 may estimate vibrations based on a speed fluctuation value or a position differential value output by a speed sensor that detects the rotation speed or magnetic pole position attached to the electric motor 30.

[0015] 1, the power conversion device 20 includes a semiconductor element 21, a temperature sensor 22, a current sensor 23, and an information processing device 10. The semiconductor element 21 includes a rectifier circuit 24, a smoothing circuit 25, an inverter circuit 26, and a gate drive circuit 27. In this case, the semiconductor element 21 may include, for example, a thyristor, a power transistor, a MOS-FET (Metal-Oxide-Semiconductor Field Effect Transistor), an insulated-gate bipolar transistor (IGBT), or the like. The semiconductor element 21 may also include, for example, a SiC power semiconductor (silicon carbide MOSFET) that consumes less power than an IGBT.

[0016] The temperature sensor 22 is a sensor provided near the semiconductor element 21, and measures the temperature near the semiconductor element 21. The current sensor 23 is a sensor that measures a current input from the power conversion device 20 to the electric motor 30. The current sensor 23 may detect the current of each of the three-phase (three wires) output lines of the power conversion device 20, i.e., the current of each of the three phases of the electric motor 30. The current sensor 23 may be integrated with the semiconductor element, or may be provided in a heat sink section that cools the semiconductor element. The current sensor 23 may also be a temperature sensor inside the CPU, which will be described later, or a temperature sensor installed on a board.

[0017] The information processing device 10 is a computer that controls each part of the power conversion device 20. The information processing device 10 may be, for example, a microcontroller in which a CPU (Central Processing Unit), a RAM (Random access memory), a ROM (Read only memory), an I / O (Input / Output) port, and the like are implemented as a single integrated circuit (IC).

[0018] The rectifier circuit 24 is configured to be capable of rectifying the three-phase AC power input from the power source PS and outputting DC power. The rectifier circuit 24 has positive and negative output terminals connected to one end of a positive line PL and a negative line NL, respectively, and can output DC power to the smoothing circuit 25 through the positive line PL and the negative line NL. The rectifier circuit 24 may be, for example, a bridge-type full-wave rectifier circuit including six semiconductor diodes SD, in which three sets of series-connected bodies of two semiconductor diodes SD constituting upper and lower arms are connected in parallel.

[0019] The smoothing circuit 25 suppresses and smoothes pulsations in the DC power output from the rectifier circuit 24 and the DC power regenerated from the inverter circuit 26. The smoothing circuit 25 includes, for example, a smoothing capacitor C. The smoothing capacitor C may be provided in parallel with the rectifier circuit 24 and the inverter circuit 26 in a path connecting the positive line PL and the negative line NL. The smoothing capacitor C smoothes the DC power output from the rectifier circuit 24 and the DC power output (regenerated) from the inverter circuit 26 while repeatedly charging and discharging as appropriate. There may be only one smoothing capacitor C. Also, a plurality of smoothing capacitors C may be disposed, and the plurality of smoothing capacitors C may be connected in parallel or in series between the positive line PL and the negative line NL. Also, the plurality of smoothing capacitors C may be configured in a form in which a series connection of two or more smoothing capacitors is connected in parallel between the positive line PL and the negative line NL.

[0020] Furthermore, the smoothing circuit 25 may include, for example, a reactor. The reactor may be provided on the positive line PL between the rectifier circuit 24 and the smoothing capacitor C (specifically, a branch point from the path where the smoothing capacitor C is disposed). The reactor smoothes the DC power output from the rectifier circuit 24 and the DC power output (regenerated) from the inverter circuit 26 while generating a voltage to appropriately prevent a change in the current.

[0021] The inverter circuit 26 has positive and negative input terminals connected to the other ends of the positive line PL and the negative line NL. The inverter circuit 26 converts the DC power supplied from the smoothing circuit 25 into three-phase AC power (e.g., U phase, V phase, and W phase) having a predetermined frequency and a predetermined voltage by the switching operation of the semiconductor switch SW, and outputs the power to the electric motor 30. The semiconductor switch SW may be, for example, an IGBT (Insulated Gate Bipolar Transistor) or a MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) made of silicon (Si). The semiconductor switch SW may also be, for example, a semiconductor element using a wide band gap semiconductor such as silicon carbide (SiC) or gallium nitride (GaN).

[0022] The inverter circuit 26 includes, for example, six semiconductor switches SW, and is configured to include a bridge circuit in which three sets of series-connected bodies (switch legs) of two semiconductor switches SW constituting upper and lower arms are connected in parallel between a positive line PL and a negative line NL. The inverter circuit 26 may output three-phase AC power through three output lines drawn from the connection points of the three sets of upper and lower arms. In addition, a free wheel diode may be connected in parallel to each of the six semiconductor switches SW. Under the control of the control circuit 80, the gate drive circuit 27 outputs a drive signal for switching (ON / OFF) the six semiconductor switches SW of the inverter circuit 26 to the gate terminals of the six semiconductor switches SW.

[0023] <<Configuration of information processing device 10>> Next, the configuration of the information processing device 10 according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the information processing device 10 according to the embodiment. The information processing device 10 has an acquisition unit 11, an estimation unit 12, and a control unit 13. Each of these units may be realized by cooperation between one or more programs installed in the information processing device 10 and hardware such as a processor and memory of the information processing device 10.

[0024] The acquisition unit 11 acquires data measured by, for example, the temperature sensor 22, the current sensor 23, and the vibration sensor 40. The estimation unit 12 estimates a specific type of abnormality degree in at least one of the power conversion device 20 and the electric motor 30 based on the information acquired by the acquisition unit 11. The control unit 13 performs control (output) according to the specific type of abnormality degree estimated by the estimation unit 12.

[0025] The specific type (type of abnormality) may include, for example, at least one of damage to the bearings of the electric motor 30 (e.g., inner ring damage), wear of the bearings of the electric motor 30, overload of the electric motor 30, overload of the power conversion device 20, dielectric breakdown of the electric motor 30, dielectric breakdown of the power conversion device 20, unbalance of the electric motor 30, misalignment of the electric motor 30, and load abnormality of the electric motor 30.

[0026] An overload on the electric motor 30 occurs when a relatively large amount of electric power is input from the power conversion device 20 to the electric motor 30, for example, when a relatively large load is applied to the electric motor 30 while the electric motor 30 is rotating. Overloading the electric motor 30 may, for example, increase the temperature of the electric motor 30, accelerating wear on the bearings and leading to bearing failure.

[0027] An imbalance in the motor 30 occurs when the mass of mechanical parts, structures, or shapes connected to the rotating shaft is not uniformly distributed, resulting in an overall imbalance in the centrifugal forces that occur during rotation. An imbalance in the motor 30 can cause the shaft to rotate outwardly, for example, which can accelerate bearing wear and lead to bearing failure.

[0028] Misalignment of the electric motor 30 occurs when the shaft rotates in a bent state due to, for example, incorrect installation of a load shaft on the shaft. Since the shaft rotates while swinging outward, this accelerates wear on the bearings and may lead to bearing failure.

[0029] <Processing of information processing device 10> Next, an example of processing of the information processing device 10 according to the embodiment will be described with reference to Figs. 3 to 7. Fig. 3 is a flowchart showing an example of processing of the information processing device 10 according to the embodiment. Fig. 4 is a diagram showing an example of a spectrum when an abnormality occurs in the semiconductor element 21 according to the embodiment. Fig. 5 is a diagram showing an example of information recorded in an abnormality coefficient table 501 according to the embodiment. Fig. 6 is a diagram showing an example of a power cycle life (number of power cycles) according to a temperature difference of the semiconductor element 21 according to the embodiment. Fig. 7 is a diagram showing an example of an abnormality coefficient F according to the embodiment. i 8 is a diagram showing an example of the frequency D of each measurement value i according to the embodiment. i Fig. 9 is a diagram showing an example of information recorded in correlation definition information 901 according to the embodiment. Fig. 10 is a diagram showing an example of information recorded in an abnormality test frequency table 1001 according to the embodiment.

[0030] 3 may be executed at different timings for each type of abnormality to be estimated (diagnosed). In this case, for example, the degree of abnormality of the first type may be estimated in a first cycle (e.g., every 20 minutes), and the degree of abnormality of the second type may be estimated in a second cycle (e.g., every 5 minutes). In the following, an example using data measured by three sensors, the temperature sensor 22, the current sensor 23, and the vibration sensor 40, is described, but the information processing device 10 may perform estimation using data measured by at least two sensors.

[0031] In step S101, the acquisition unit 11 acquires information indicating the change in temperature of the semiconductor element 21 of the power conversion device 20 that drives the electric motor 30 during a first period (e.g., the last 20 minutes), information indicating the change in the current input to the electric motor 30 during the first period, and information indicating the change in vibration in the electric motor 30 during the first period.

[0032] Here, the acquiring unit 11 may acquire data measured at each time point by, for example, the temperature sensor 22, the current sensor 23, and the vibration sensor 40. In this case, the acquiring unit 11 may record the data measured at each time point by, for example, the temperature sensor 22, the current sensor 23, and the vibration sensor 40 in a storage device.

[0033] Fig. 4 shows an example of a spectrum in the frequency domain that occurs in the current input to the electric motor 30 when an abnormality occurs in the semiconductor element 21. The example of Fig. 4 shows that an abnormal spectrum called a sideband increases according to the rotation speed of the electric motor 30.

[0034] When acquiring vibration data based on the vibration sensor 40, the acquiring unit 11 may perform pre-processing using envelope processing, which is used to remove the resonance frequency of the metal of the housing and obtain a desired vibration component, and FFT (Fast Fourier Transform). When acquiring abnormal vibration based on the current detected by the current sensor 23, the acquiring unit 11 may perform spectrum analysis of minute current changes using FFT. The acquiring unit 11 may use sidebands due to the mechanical angle and integral multiple frequencies of the electrical angle as search points. The acquiring unit 11 may normalize the spectrum value according to the rotation speed of the electric motor 30. Since the spectrum and range of frequencies at which abnormal symptoms occur differ depending on the type of abnormality described later, the condition of the frequency range and the threshold value for determining an abnormality for each abnormality type may be stored in advance in the abnormality coefficient table 501 as the abnormality coefficient of each current and the abnormality coefficient of each vibration.

[0035] Next, the estimation unit 12 estimates a first degree of abnormality of a specific type in the first period based on the transition of the temperature of the semiconductor element 21 in the first period and information indicating the likelihood of abnormality of the specific type corresponding to each temperature of the semiconductor element 21 (step S102). Here, the estimation unit 12 may estimate the first degree of abnormality based on, for example, the temperature change of the semiconductor element 21 and the abnormality coefficient of each temperature corresponding to the specific type of abnormality registered in the abnormality coefficient table 501 of FIG. 5. The abnormality coefficient of each temperature corresponding to the specific type of abnormality may be a value set based on, for example, the power cycle life curve (life characteristic data) of the semiconductor element 21 shown in FIG. 6. The power cycle life curve quantitatively represents damage such as peeling of wire bonding caused by thermal fatigue of metal due to temperature change in the semiconductor element and cracking of solder between the chip and the base substrate.

[0036] In the example of Fig. 5, the abnormality coefficient table 501 records the abnormality coefficient of each temperature, the abnormality coefficient of each current, and the abnormality coefficient of each vibration in association with the type of abnormality. The data in the abnormality coefficient table 501 may be created, for example, based on past experience and set in advance by an administrator of the information processing device 10 or the like. Numerical data generally used in device life design, such as the power cycle life curve of the parts described above, may also be used. In this case, for example, the abnormality coefficient F i may be determined based on a power cycle lifetime defined as the lifetime for each measurement i of temperature change in the semiconductor device 21 according to a particular type.

[0037] The vertical axis of Fig. 6 indicates the power cycle life (number of power cycles) on a logarithmic scale. In the example of Fig. 6, the power cycle life of the semiconductor element 21 becomes relatively long (large) when the cycle temperature difference ΔTj is relatively small, and becomes exponentially smaller as the cycle temperature difference ΔTj becomes larger.

[0038] Here, the estimation unit 12 refers to the abnormality coefficient table 501 and calculates an abnormality coefficient F iThen, the estimation unit 12 obtains a frequency D for each measurement value i as shown in the following formula (1): i The abnormality coefficient F i The sum of the values ​​multiplied by may be calculated as the first degree of abnormality value R for the first period. For each measurement value i, for example, a predetermined lower limit value (e.g., 10°C for temperature change) may be set to 0, and a predetermined upper limit value (e.g., 100°C for temperature change) may be set to n. i is a relative coefficient according to the likelihood of a specific type of abnormality due to each measurement value i. In this case, the abnormality coefficient F i may be calculated from, for example, the inverse of the number of power cycles at a point corresponding to the corresponding temperature change on the power cycle life curve, and a coefficient of the detection gain (margin). The detection gain may be a value for setting an adjustment value according to, for example, the intended use (for example, harsh vertical transport applications such as cranes) or the cooling capacity of the location where the power conversion device is installed. i is the number of times that the measured value i was measured at each time point in the first period. For example, if the number of times that a specific temperature change k was measured at each time point in the first period is 10, the frequency D k The value of is 10. According to formula (1), the closer R is to the maximum value (for example, 1.0), the closer to the end of the life of the semiconductor element, which is a quantitative indicator.

number

[0039] Figure 7 shows the anomaly coefficient F i 701 is shown. The horizontal axis of FIG. 7 is the measured value i, and the vertical axis is the abnormality coefficient F i The frequency D of each measurement value i is shown in Fig. 8. i An example of 801 is shown. The horizontal axis of FIG. 8 is the measurement value i, and the vertical axis is the frequency D i The value is

[0040] Next, the estimation unit 12 estimates a second degree of anomaly of a specific type in the first period based on the transition of the current input to the motor 30 in the first period and information indicating the likelihood of anomaly of the specific type of current input to the motor 30 (step S103). Here, the estimation unit 12 may refer to the anomaly coefficient table 501 to acquire an anomaly coefficient for each measured value of the current input to the motor 30 or a change in the current according to the specific type. Then, the estimation unit 12 may calculate, as the value of the second degree of anomaly in the first period, the sum of values ​​obtained by multiplying the frequency of each measured value of the current or the change in the current by the anomaly coefficient, as in step S102.

[0041] Next, the estimation unit 12 estimates a third abnormality degree of the specific type during the first period based on the progression of the vibration in the electric motor 30 during the first period and information indicating the likelihood of an abnormality of the specific type corresponding to each vibration in the electric motor 30 (step S104).

[0042] Here, the estimation unit 12 may refer to the abnormality coefficient table 501 to acquire the abnormality coefficient of each vibration in the electric motor 30 according to the specific type. Then, similar to step S102, the estimation unit 12 may calculate the sum of values ​​obtained by multiplying the frequency by the abnormality coefficient for each measurement value of the vibration as the value of the third abnormality degree in the first period. Note that each measurement value of the vibration may be a vibration amount (displacement) or a value of each vibration frequency calculated by FFT, a tracking filter, a bandpass filter, or the like.

[0043] Next, the estimation unit 12 estimates the degree of abnormality of the specific type based on each correlation of the estimated first abnormality degree, second abnormality degree, and third abnormality degree (correlation between the first abnormality degree and the second abnormality degree, correlation between the second abnormality degree and the third abnormality degree, and correlation between the third abnormality degree and the first abnormality degree) (step S105). Here, the estimation unit 12 may determine the value of the degree of abnormality of the specific type to be a higher value, for example, as at least one of the first abnormality degree, the second abnormality degree, and the third abnormality degree is higher and as each correlation of the first abnormality degree, the second abnormality degree, and the third abnormality degree is higher. Here, the estimation unit 12 may determine the degree of each correlation of the first abnormality degree, the second abnormality degree, and the third abnormality degree based on correlation definition information 901 shown in FIG. 9. Note that data of the correlation definition information 901 shown in FIG. 9 may be set (registered) in advance by an administrator (operator) of the information processing device 10 or the like.

[0044] In the example of FIG. 9, when the correlation between the overload information, a specific current pulsation, and the inner race damage vibration is equal to or greater than a threshold, it is determined that there is an abnormality in the bearing (inner race damage). The overload information is, for example, the abnormality coefficient F calculated from the reciprocal of the number of power cycles at the point corresponding to the temperature change in the power cycle life curve and the coefficient of the detection gain (margin) in the above-mentioned formula (1), i The specific current pulsation may be, for example, a value of a second abnormality R2 calculated based on an abnormality coefficient corresponding to the frequency spectrum of each period in which the magnitude of the current fluctuates in the above-mentioned formula (1). The inner race damaging vibration may be, for example, a value of a third abnormality R3 calculated based on an abnormality coefficient corresponding to the frequency spectrum of each period at 100 Hz or more in which the magnitude of the vibration fluctuates in the above-mentioned formula (1).

[0045] The estimation unit 12 may determine that the correlation is higher when the trends or degrees of change of the degrees of abnormality (the first degree of abnormality, the second degree of abnormality, and the third degree of abnormality) are closer to each other. In this case, the estimation unit 12 may calculate, for example, a difference or ratio between the value of the first degree of abnormality estimated last time and the value of the first degree of abnormality estimated this time as a value indicating the degree of change of the first degree of abnormality. The estimation unit 12 may also calculate, for example, a correlation coefficient between the values ​​of the degrees of abnormality as the correlation. The estimation unit 12 may logically determine the degree of each correlation based on, for example, one or more decision trees in which each degree of abnormality is weighted or prioritized.

[0046] For example, if the current input to the electric motor 30 increases, it may simply be that a relatively heavy object is being moved, and this may not be an abnormality. According to the present disclosure, since an abnormality is estimated based on the correlation between the measured values ​​of multiple measurement items, the presence or absence of an abnormality can be more appropriately detected.

[0047] Next, the control unit 13 performs control (output) according to the degree of abnormality of the specific type estimated by the estimation unit 12 (step S106). Here, for example, when the degree of abnormality of the specific type is equal to or higher than a threshold value, the control unit 13 may set the frequency of inspection for the specific type of abnormality to a relatively high frequency as shown in Fig. 10. In the example of Fig. 10, an abnormality inspection frequency table 1001 records the abnormality inspection frequency in association with the type of abnormality.

[0048] This allows the abnormality to be detected more quickly, even if the time it takes for a relatively small abnormality to develop into a relatively large abnormality is short, and therefore the power conversion device 20 and the electric motor 30 can be stopped before a larger failure occurs.

[0049] Furthermore, for example, when the degree of abnormality of a specific type is less than a threshold, the control unit 13 may set the frequency of inspection for the specific type of abnormality to a relatively low frequency. This can reduce, for example, the processing load for abnormality inspection. For example, assume that the information processing device 10, which is a microcontroller of the power conversion device 20, performs processing for controlling the electric motor 30 in synchronization with the carrier frequency of PWM (Pulse Width Modulation) and performs processing for abnormality inspection between the processing. In this case, it is useful to be able to reduce the processing load for abnormality inspection.

[0050] Furthermore, for example, when the degree of abnormality of a specific type is equal to or higher than a threshold, the control unit 13 may output information indicating the specific type. This makes it possible to realize predictive maintenance that prompts a maintenance person to replace a component of the power conversion device 20 or the electric motor 30 before the component breaks down. In this case, the control unit 13 may notify the abnormality of the specific type by, for example, causing an LED (Light Emitting Diode) to emit light in a light emission mode (for example, a blinking interval) according to the specific type.

[0051] (About information processing devices) Fig. 11 is a diagram showing an example of the configuration of an information processing device 10 according to an embodiment. In the example of Fig. 11, the information processing device 10 includes a processor 101, a memory 102, and a communication interface 103. These units may be connected by a bus or the like. The memory 102 stores at least a part of a program 104. The communication interface 103 includes an interface required for communication with other network elements.

[0052] When the program 104 is executed by the processor 101, memory 102, etc. in cooperation, the computer 100 performs at least some processing of the embodiments of the present disclosure. The memory 102 may be of any type suitable for a local technology network. The memory 102 may be, as a non-limiting example, a non-transitory computer-readable storage medium. The memory 102 may also be implemented using any suitable data storage technology, such as semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. Although only one memory 102 is shown in the computer 100, several physically different memory modules may be present in the computer 100. The processor 101 may be of any type. The processor 101 may include one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture, as a non-limiting example. The computer 100 may have multiple processors, such as application-specific integrated circuit chips that are time-slaved to a clock that synchronizes the main processor.

[0053] The program can be stored and provided to the computer using various types of non-transitory computer-readable media. The non-transitory computer-readable media include various types of tangible recording media. Examples of the non-transitory computer-readable media include magnetic recording media, magneto-optical recording media, optical disk media, semiconductor memories, and the like. The magnetic recording media include, for example, flexible disks, magnetic tapes, hard disk drives, and the like. The magneto-optical recording media include, for example, magneto-optical disks, and the like. The optical disk media include, for example, Blu-ray disks, CD (Compact Disc)-ROM (Read Only Memory), CD-R (Recordable), CD-RW (ReWritable), and the like. The semiconductor memory includes, for example, solid-state drives, mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, RAMs (random access memories), and the like. The program may also be provided to the computer by various types of temporary computer-readable media. Examples of the temporary computer-readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire or an optical fiber, or via a wireless communication path.

[0054] The information processing device 10 may be provided, for example, outside the power conversion device 20. In this case, for example, the information processing device 10 may be housed in the housing of the electric motor 30 and configured as an integrated information processing device. The information processing device 10 may also be a device contained in one housing, but the information processing device 10 of the present disclosure is not limited to this. Each unit of the information processing device 10 may be realized, for example, by cloud computing configured by one or more computers. Such information processing devices 10 are also included in examples of the "information processing device" of the present disclosure.

[0055] The invention made by the inventor has been specifically described above based on the embodiments, but it goes without saying that the present invention is not limited to the embodiments already described, and various modifications are possible without departing from the gist of the invention. [Explanation of symbols]

[0056] 1. Drive system 10. Information processing device 11 Acquisition Department 12 Estimation part 13 Control section 20 Power conversion device 21 Semiconductor elements 22 Temperature Sensor 23 Current Sensor 30 Electric motor 40 Vibration Sensor

Claims

1. an acquisition unit that acquires information indicating a change in temperature of a semiconductor element of a power conversion device that drives an electric motor and information indicating a change in a current input to the electric motor; an estimation unit that estimates a specific type of abnormality degree in at least one of the power conversion device and the electric motor based on the information acquired by the acquisition unit; An information processing device having the above configuration.

2. and a control unit that increases a frequency of inspections for the specific type of abnormality when the degree of the specific type of abnormality is equal to or greater than a threshold value. The information processing device according to claim 1 .

3. a control unit that outputs information indicating the specific type when the degree of abnormality of the specific type is equal to or greater than a threshold value; The information processing device according to claim 1 .

4. The estimation unit is estimating the degree of abnormality of the specific type based on a correlation between a first degree of abnormality determined based on a transition in temperature of the semiconductor element and a second degree of abnormality determined based on a transition in the current; The information processing device according to claim 1 .

5. The estimation unit is estimating the first abnormality degree based on a transition of the temperature of the semiconductor element and information indicating the likelihood of the specific type of abnormality corresponding to each temperature of the semiconductor element; The information processing device according to claim 4.

6. The estimation unit is estimating the second abnormality degree based on a transition of a current input to the electric motor and information indicating the likelihood of the specific type of abnormality corresponding to each current input to the electric motor; The information processing device according to claim 4.

7. The acquisition unit acquires information indicating a transition of vibration in the electric motor. The information processing device according to claim 1 .

8. The estimation unit is estimating the degree of abnormality of the specific type based on correlations among a first degree of abnormality determined based on a transition in temperature of the semiconductor element, a second degree of abnormality determined based on a transition in the current, and a third degree of abnormality determined based on a transition in vibration in the electric motor; The information processing device according to claim 7.

9. The estimation unit is estimating the third abnormality degree based on a transition of vibration in the electric motor and information indicating the likelihood of the specific type of abnormality corresponding to each vibration in the electric motor; The information processing device according to claim 8.

10. Acquiring information indicating a change in temperature of a semiconductor element of a power conversion device that drives an electric motor and information indicating a change in a current input to the electric motor; estimating a degree of abnormality of a specific type in at least one of the power conversion device and the electric motor based on the acquired information; An information processing method in which processing is executed by an information processing device.

11. A power conversion device that drives an electric motor, an acquisition unit that acquires information indicating a change in temperature of a semiconductor element of the power conversion device and information indicating a change in a current input to the electric motor; an estimation unit that estimates a specific type of abnormality degree in at least one of the power conversion device and the electric motor based on the information acquired by the acquisition unit; A power conversion device having the following:

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