Deterioration diagnosis device, deterioration diagnosis system, deterioration estimation learning device, and deterioration diagnosis method

Abstract

Provided is a deterioration diagnosis device for diagnosing deterioration in an apparatus to which an electric motor is mounted, the deterioration diagnosis device including a deterioration diagnosis unit that diagnoses the degree of deterioration of the apparatus, the deterioration diagnosis unit comprising: a first deterioration index generation unit that outputs a first deterioration index obtained from the respective phases of at least two-phase currents flowing through the electric motor; and a deterioration determination unit that determines the deterioration status of the electric motor on the basis of the first deterioration index.

Description

Deterioration diagnosis device, deterioration diagnosis system, deterioration estimation learning device, and deterioration diagnosis method

[0001] This technology relates to a degradation diagnosis device, a degradation diagnosis system, a degradation estimation learning device, and a degradation diagnosis method for diagnosing degradation of equipment, and in particular to diagnosing degradation in a rotating shaft of equipment.

[0002] Equipment equipped with an electric motor, such as a compressor that constitutes a refrigeration cycle device, has a rotating shaft (hereinafter referred to as the shaft) that transmits power from the electric motor and bearings that support the shaft. For example, when a compressor is operated, compressor failures are often caused by deterioration or damage to the bearings. If the equipment is operated with worn and deteriorated bearings, the electric motor will stop, resulting in significant downtime for the compressor and reducing the compressor's availability.

[0003] For this reason, it is desirable to predict the occurrence of a failure based on the condition of the motor's bearings, etc. It is then preferable to take appropriate measures, such as repairing the motor's bearings, before the deterioration progresses to the point where the motor's bearings wear and a failure occurs. This can reduce or shorten compressor downtime, improve the compressor's availability, and enhance reliability. Therefore, a deterioration diagnosis device has been proposed that creates a Lissajous distribution diagram by overlaying two types of current data obtained by a current measurement unit over multiple periods, and diagnoses the condition of a rotating machine system based on the evaluation results of the distribution diagram (see, for example, Patent Document 1).

[0004] International Publication No. 2018 / 158910

[0005] The degradation diagnosis device in Patent Document 1 estimates degradation from a plurality of phase currents using a Lissajous figure as an index. However, in the degradation diagnosis device in Patent Document 1, if a load fluctuation occurs during measurement of the phase currents and the current amplitude is affected by the load fluctuation, the accuracy of the degradation diagnosis deteriorates.

[0006] Therefore, an object of this disclosure is to provide a degradation diagnosis device, a degradation diagnosis system, a degradation estimation learning device, and a degradation diagnosis method that can solve the above-mentioned problems and perform shaft degradation diagnosis with high accuracy.

[0007] The disclosed degradation diagnosis device is a degradation diagnosis device that diagnoses degradation in equipment equipped with an electric motor, and has a degradation diagnosis unit that diagnoses the degree of degradation of the equipment, and the degradation diagnosis unit includes a degradation first index generation unit that outputs a degradation first index obtained from each phase of at least two phase currents flowing through the electric motor, and a degradation determination unit that determines the degradation status of the electric motor based on the degradation first index.

[0008] In addition, the disclosed degradation diagnosis system includes a degradation determination unit in the degradation diagnosis unit, a server recording device that records data related to the equipment equipped with an electric motor contained in a signal sent via a telecommunications line, and a server control device that detects abnormalities related to the equipment, counts the number of detected abnormalities, and uses the number of occurrences as a second degradation index and sends a signal including the second degradation index via the telecommunications line, and the degradation determination unit diagnoses the degradation of the electric motor based on the first degradation index and the second degradation index sent via the telecommunications line.

[0009] Furthermore, the disclosed degradation estimation learning device is a degradation estimation learning device that learns to estimate degradation in equipment equipped with an electric motor, and is equipped with a learning unit that performs a learning process using machine learning with at least one type of data from among the phase current flowing through the electric motor, the state quantity of the equipment, and the bus voltage, and corresponding degradation amount data, as input data, to generate a learned model that estimates the degree of abnormality of the equipment, and a memory unit that stores the generated learned model.

[0010] The disclosed degradation diagnosis method is a degradation diagnosis method for diagnosing degradation in equipment equipped with an electric motor, and includes a calculation step of calculating a first degradation index based on each phase of at least two phase currents flowing through the electric motor, and a degradation determination step of determining the degradation status of the electric motor based on the first degradation index.

[0011] The disclosed degradation diagnosis device estimates the degradation state of the motor shaft based on the phase of the phase current flowing through the electric motor mounted on the equipment, and diagnoses the degradation state based on the estimation result. The degradation diagnosis device performs diagnosis based on the phase of the current, which is less affected by load fluctuations, and can therefore diagnose the progression of motor degradation with high accuracy. This can improve the reliability of the equipment and the electric motor.

[0012] 1 is a diagram illustrating an example of the configuration of a deterioration diagnosis system SYS1 according to a first embodiment. FIG. 2 is a diagram illustrating the internal configuration of an example of a compressor 5 according to the first embodiment. FIG. 3 is a diagram illustrating the state of a main shaft 52 and a main bearing 57 when a compressor 5 according to the first embodiment is driven. FIG. 4 is a block diagram illustrating the configuration of a deterioration diagnosis unit 100 in a control device 10 according to the first embodiment. FIG. 5 is a diagram comparing current vectors between two phases when an electric motor 53 according to the first embodiment is driven normally and when it is degraded. FIG. 6 is a diagram illustrating the flow of deterioration determination processing performed by a deterioration determination unit 130 according to the first embodiment. FIG. 7 is a diagram illustrating thresholds in the deterioration determination processing performed by the deterioration determination unit 130 according to the first embodiment. FIG. 8 is a diagram illustrating the flow of processing based on deterioration diagnosis in a control device 10 according to the first embodiment. FIG. 9 is a diagram illustrating an example of period determination processing performed by a control unit 200 of a control device 10 based on a shaft deterioration diagnosis result SWR. FIG. 10 is a block diagram illustrating the configuration of a deterioration diagnosis unit 100 in a control device 10 according to a second embodiment. FIG. 11 is a block diagram illustrating the configuration of a deterioration diagnosis unit 100 in a control device 10 according to a third embodiment. FIG. 12 is a diagram comparing current vectors between three phases when an electric motor 53 according to the third embodiment is driven normally and when it is degraded. FIG. 10 is a block diagram showing the configurations of a degradation diagnosis unit 100 and a degradation estimation learning unit 300 in a control device 10 according to a fourth embodiment. FIG. 11 is a diagram explaining the flow of processing by the degradation estimation learning unit 300 according to the fourth embodiment. FIG. 12 is a diagram showing the flow of processing by an abnormality estimation unit 126 of the degradation diagnosis unit 100 according to the fourth embodiment. FIG. 13 is a block diagram showing the configuration of a degradation determination unit 130 of the degradation diagnosis unit 100 according to a fifth embodiment. FIG. 14 is a diagram explaining thresholds in the degradation determination processing performed by the degradation determination unit 130 according to the fifth embodiment. FIG. 15 is a block diagram showing an example configuration of a degradation diagnosis system SYS2 according to a sixth embodiment.

[0013] Degradation diagnosis devices, degradation diagnosis systems, degradation estimation learning devices, and degradation diagnosis methods according to embodiments will be described below with reference to the drawings. In the drawings, components denoted with the same reference numerals are identical or equivalent components, and are common to all of the embodiments described below. Furthermore, the configurations of the components shown in all of the embodiments described below are merely examples and are not limited to the configurations described below. In particular, the combinations of the components are not limited to the combinations in each embodiment, and components described in other embodiments may be applied to other embodiments. Furthermore, the levels of parameters such as pressure and temperature are not determined in relation to absolute values, but are determined relatively in terms of the state and operation of the device, etc.

[0014] Embodiment 1. Fig. 1 is a diagram illustrating an example of the configuration of a degradation diagnosis system SYS1 according to embodiment 1. The degradation diagnosis system SYS1 according to embodiment 1 will be described with reference to Fig. 1 and other figures. In the degradation diagnosis system SYS1 of embodiment 1, as will be described later, a control device 10 serves as a degradation diagnosis device and performs degradation diagnosis processing on equipment to be diagnosed. In this case, a compressor 5 having an electric motor 53 and used in a refrigeration cycle device or the like serves as the equipment to be diagnosed. The configuration of the compressor 5 will be described later.

[0015] The compressor 5 is driven at a predetermined drive frequency by components including an AC power source 1, a rectifier circuit 2, an electrolytic capacitor 3, and an inverter circuit 4. The rectifier circuit 2 converts three-phase (U-phase, V-phase, and W-phase) AC power from the AC power source 1 into DC power. The electrolytic capacitor 3 smoothes the DC power from the rectifier circuit 2. Based on a gate pulse GP, which is an instruction from the control device 10, the inverter circuit 4 converts the DC power from the rectifier circuit 2 into three-phase AC power based on the drive frequency, and supplies the three-phase AC power to the compressor 5.

[0016] The current sensor 6 is provided midway along the wiring 7 leading from the inverter circuit 4 to the compressor 5. The current sensor 6 detects two phase currents of the three-phase AC current flowing from the inverter circuit 4 to the compressor 5 based on a predetermined drive frequency. Here, the current sensor 6 detects, for example, a U-phase current Iu and a V-phase current Iv, and sends the detected currents as a current detection signal Iuv to the control device 10. The voltage sensor 8 detects the bus voltage Vdc. The voltage sensor 8 sends the detected voltage as a voltage detection signal related to the bus voltage Vdc to the control device 10.

[0017] Furthermore, the control device 10 is a component that drives the compressor 5. The control device 10 outputs a gate pulse GP to the inverter circuit 4 based on the current detection signal Iuv detected by the current sensor 6, the voltage detection signal detected by the voltage sensor 8, and the like, to control the driving of the compressor 5.

[0018] Here, the control device 10 has hardware such as a computer that executes various programs and performs various processes such as control processing and degradation diagnosis processing. The hardware computing element that performs the various processes is a processing device 11, such as a central processing unit (CPU) or a field-programmable gate array (FPGA). The processing performed by the control device 10 is performed by the processing device 11 executing the processes based on program data stored in a storage device 12. The storage device 12 includes a volatile storage device (not shown) such as a random access memory (RAM) that can temporarily store data and a non-volatile auxiliary storage device (not shown) such as a flash memory that can store data long-term. As described above, the storage device 12 stores the programs used by the processing device 11. The storage device 12 also temporarily or long-term stores data used in the degradation diagnosis processing described below. Hereinafter, the processing and storage performed by the processing device 11 and the storage device 12 will be described as being performed by the control device 10.

[0019] Here, the control device 10 in embodiment 1 has a degradation diagnosis unit 100 and a control unit 200, as described below. Therefore, the control device 10 in embodiment 1 functions as a degradation diagnosis device. The degradation diagnosis unit 100 performs diagnostic processing for degradation related to the relationship between the main shaft 52 and the main bearing 57 in the compressor 5, described below, such as damage to the main bearing 57, based on the current detection signal Iuv related to the detection by the current sensor 6, and outputs a diagnostic result signal including a shaft degradation diagnosis result SWR. The configuration and processing content related to the processing function of the degradation diagnosis unit 100 will be described below. Deterioration related to the relationship between the main shaft 52 and the main bearing 57 will hereinafter be referred to as shaft degradation. The control unit 200 performs drive control of the compressor 5, etc. Here, the control unit 200 also performs some of the processing related to the degradation diagnosis.

[0020] The notification device 20 is a device that issues a notification based on a signal from the control device 10. Here, the notification device 20 has a display device, and displays based on the diagnosis result signal including the shaft deterioration diagnosis result SWR output by the deterioration diagnosis unit 100, and notifies the user, maintenance team, or the like of the degree of shaft deterioration.

[0021] Fig. 2 is a diagram illustrating an example of the internal configuration of compressor 5 according to the first embodiment. As shown in Fig. 2, compressor 5 has a suction pipe 51, a main shaft 52, an electric motor 53, lubricating oil 54, an oil pump 55, an auxiliary bearing 56, a main bearing 57, a compression mechanism 58, and a discharge pipe 59. Compressor 5 is a device that constitutes part of a refrigerant circuit in a refrigeration cycle device such as an air conditioner. In compressor 5, refrigerant is drawn in through suction pipe 51 of compressor 5 and discharged from discharge pipe 59.

[0022] The suction pipe 51 is a pipe for drawing low-temperature, low-pressure refrigerant into the compressor 5. Here, for example, a pressure sensor, a temperature sensor, and a humidity sensor may be attached to the suction pipe 51 to detect and measure the pressure, temperature, humidity, etc. of the refrigerant flowing through the pipe. Furthermore, these sensors may be attached to piping in the refrigerant circuit to estimate the pressure, temperature, humidity, etc. of the refrigerant flowing through the suction pipe 51.

[0023] The electric motor 53 is connected to a three-phase AC power line (not shown) and is driven in response to a voltage applied from the inverter circuit 4. The main shaft 52, which serves as a rotating shaft, is connected to the electric motor 53 and rotates when driven by the electric motor 53, transmitting rotational energy as power to the compression mechanism 58. Lubricating oil 54 is stored at the bottom of the compressor 5 and is supplied to the auxiliary bearing 56 and the main shaft 52 by an oil pump 55, lubricating the auxiliary bearing 56 and the main shaft 52. Here, as a means for checking the amount of lubricating oil 54, a liquid level sensor capable of detecting the oil level of the lubricating oil 54 may be attached to the compressor 5 to measure the amount of lubricating oil 54. The auxiliary bearing 56 and the main bearing 57 support the main shaft 52.

[0024] The discharge pipe 59 is a pipe for discharging the high-temperature, high-pressure refrigerant compressed by the compression mechanism 58 to the outside of the compressor 5. Here, a pressure sensor, a temperature sensor, a humidity sensor, etc. may be attached to the discharge pipe 59 to detect and measure the pressure, temperature, humidity, etc. of the refrigerant flowing inside the pipe. Furthermore, these sensors may be attached to piping inside the air conditioning equipment to estimate the pressure, temperature, humidity, etc. of the refrigerant flowing inside the discharge pipe 59.

[0025] 3A and 3B are diagrams illustrating the state of the main shaft 52 and the main bearing 57 when the compressor 5 according to the first embodiment is driven. Here, Fig. 3A is a diagram illustrating a cross section when the main shaft 52 and the main bearing 57 are in a lubricated state and the compressor 5 is operating normally. Fig. 3B is a diagram illustrating a cross section when the main shaft 52 and the main bearing 57 are not in a lubricated state and the shaft is deteriorated.

[0026] 3A, when the compressor 5 is operating normally, a sufficient amount of lubricating oil 54 is filled between the main shaft 52 and the main bearing 57. The main shaft 52 rotates smoothly with a certain clearance secured between the main bearing 57 and the main shaft 52.

[0027] On the other hand, as shown in FIG. 3B , when the compressor 5 deteriorates, the viscosity of the lubricating oil 54 decreases due to the influence of temperature, aging, and the like, and an oil film cannot be secured between the main shaft 52 and the main bearing 57. As a result, the main shaft 52 and the main bearing 57 come into contact with each other in part, causing wear of the main bearing 57 at the contacting portion. If the main shaft 52 continues to rotate in a worn state, the wear of the main bearing 57 will worsen and shaft deterioration will progress. This will eventually force the control device 10 to stop the compressor 5 (downtime), which may result in a decrease in the availability of the compressor 5.

[0028] 4 is a block diagram showing the configuration of the degradation diagnosis unit 100 in the control device 10 according to the first embodiment. As described above, the control device 10 according to the first embodiment has the degradation diagnosis unit 100 and functions as a degradation diagnosis device. The degradation diagnosis unit 100 performs degradation diagnosis processing and outputs a diagnosis result signal including a shaft degradation diagnosis result SWR. The degradation diagnosis unit 100 according to the first embodiment has a first degradation index generation unit 110, a second degradation index generation unit 120, and a degradation determination unit 130.

[0029] The first degradation indicator generating unit 110 in the first embodiment includes a phase calculating unit 111 and a phase difference calculating unit 112. The phase calculating unit 111 performs a phase calculation step, such as calculating a U-phase phase θu and a V-phase phase θv from the U-phase current Iu and V-phase current Iv of two phases. The phase calculating unit 111 calculates the U-phase phase θu and the V-phase phase θv based on the following equation (1). In equation (1), θ is the phase. The sine component and cosine component are calculated using the sine component and cosine component of the input signal, respectively.

[0030]

[0031] Although not particularly limited, the phase calculation unit 111 in the first embodiment further calculates a U-phase phase average value θu_ave and a V-phase phase average value θv_ave based on the multiple U-phase phases θu and V-phase phases θv, respectively. By calculating the average value for the phase θ, it is possible to suppress and smooth the variation in the phase θ due to the detected current, and it is possible to calculate the phase θ with higher accuracy.

[0032] Furthermore, the phase difference calculation unit 112 performs a phase difference calculation step in which it calculates a difference based on the U-phase phase average value θu_ave and the V-phase phase average value θv_ave calculated by the phase calculation unit 111 using the following equation (2) to calculate a phase difference dθ based on the phase θ of the phase current. The phase difference dθ is a first deterioration indicator. In equation (2), dθ is the phase difference, θu_ave is the U-phase phase average value, and θv_ave is the V-phase phase average value. Furthermore, 120 in equation (2) is a numerical value representing the angle formed by the ideal phase difference between the phases of the electric motor 53 when three-phase balance is achieved. Here, the phase difference calculation unit 112 uses the average values ​​of the phases in the U phase and the V phase to calculate the phase difference dθ, but this is not limited to this. For example, when the current sensor 6 detects the W-phase current Iw, the W-phase phase average value θw_ave may be calculated instead of the U-phase phase average value θu_ave, and the phase difference dθ between the V phase and the W phase or the phase difference dθ between the W phase and the U phase may be used as the first indicator of deterioration.

[0033]

[0034] 5 is a diagram comparing current vectors between two phases when the electric motor 53 according to the first embodiment is driven normally and when it is driven in a deteriorated state. When the electric motor 53 is driven, a three-phase current vector is generated. In the deterioration diagnosis system SYS1 according to the first embodiment, the current sensor 6 detects the U-phase current Iu and the V-phase current Iv of the three-phase AC current and sends a current detection signal Iuv to the control device 10. For this reason, FIG. 5 illustrates current vectors in the U phase and the V phase.

[0035] When the electric motor 53 is normal, no shaft eccentricity occurs during operation, and the angle between the U-phase and V-phase current vectors is 120°, as shown by the solid line in Figure 5. On the other hand, when shaft deterioration of the electric motor 53 progresses, shaft eccentricity occurs during operation, and the direction of the U-phase and V-phase current vectors changes, changing the angle they form, resulting in a deviation and displacement from the normal angle of 120°. The phase difference calculation unit 112 in the first embodiment calculates the deviation displacement amount (deviation amount) as the phase difference dθ based on equation (2).

[0036] The degradation diagnosis unit 100 may simply perform a degradation diagnosis by using the first degradation index generation unit 110 to perform a calculation based on the phase θ of the phase current. However, in this embodiment, the degradation diagnosis unit 100 includes the second degradation index generation unit 120 so as to perform a diagnosis with higher accuracy. The diagnosis is performed based on two types of degradation indexes: the first degradation index generated by the first degradation index generation unit 110 and the second degradation index generated by the second degradation index generation unit 120. As the deterioration of the electric motor 53 progresses, the possibility of an abnormality occurring in the electric motor 53 and the compressor 5 equipped with the electric motor 53 increases. Therefore, the second degradation index generation unit 120 detects an abnormality related to the electric motor 53 and outputs a second degradation index based on the number of times the abnormality occurs.

[0037] The second degradation indicator generating unit 120 in the first embodiment includes an abnormality detecting unit 121 and an abnormality counting unit 122. The abnormality detecting unit 121 performs an abnormality detection step of detecting an abnormality related to the electric motor 53. The abnormality detecting unit 121 in the first embodiment detects an abnormality in the U-phase current Iu and the V-phase current Iv. When at least one of the U-phase current Iu and the V-phase current Iv is equal to or greater than an overcurrent setting value, which is a preset threshold, the abnormality detecting unit 121 turns on and outputs an overcurrent detection signal OC. On the other hand, when both the U-phase current Iu and the V-phase current Iv are less than the overcurrent setting value, the abnormality detecting unit 121 turns off and outputs the overcurrent detection signal OC.

[0038] The abnormality counting unit 122 performs an abnormality counting step of counting up. In the first embodiment, the abnormality counting unit 122 counts up when the overcurrent detection signal OC from the abnormality detection unit 121 is on. Then, the abnormality counting unit 122 outputs an overcurrent count value COC, which serves as a second indicator of deterioration, based on the count. Here, the abnormality counting unit 122 does not count up when the overcurrent detection signal OC is off.

[0039] The deterioration determination unit 130 performs a deterioration determination step of determining the deterioration state of the motor 53 of the compressor 5 based on the phase difference dθ serving as the first deterioration index and the overcurrent count value COC serving as the second deterioration index.

[0040] FIG. 6 is a diagram illustrating the flow of the deterioration determination process performed by the deterioration determination unit 130 according to the first embodiment. In diagnosing shaft deterioration (step S10), the deterioration determination unit 130 determines whether the overcurrent count value COC is equal to or greater than 0 and less than Y1, and whether the phase difference dθ is equal to or greater than 0 and less than X1 (hereinafter referred to as condition 1) (step S11). If the deterioration determination unit 130 determines that condition 1 is met, it determines that the condition is "normal" (step S12). The deterioration determination unit 130 then outputs a diagnosis result signal including a shaft deterioration diagnosis result SWR indicating "normal" to the notification device 20 (step S18). The notification device 20 displays the normal state based on the diagnosis result signal.

[0041] On the other hand, if the degradation determination unit 130 determines that Condition 1 is not satisfied, it further determines whether the overcurrent count value COC is greater than or equal to Y1 and less than Y2, and whether the phase difference dθ is greater than or equal to X1 and less than X2 (hereinafter referred to as Condition 2) (step S13). If the degradation determination unit 130 determines that Condition 2 is satisfied, it determines that the degradation is "minor degradation" (step S14). Then, the degradation determination unit 130 outputs a diagnosis result signal including a shaft degradation diagnosis result SWR indicating "minor degradation" to the notification device 20 (step S18). The notification device 20 displays the indication of minor degradation based on the diagnosis result signal.

[0042] If the degradation determination unit 130 determines that Condition 2 is not satisfied, it further determines whether the overcurrent count value COC is equal to or greater than Y2 and whether the phase difference dθ is equal to or greater than X2 (hereinafter referred to as Condition 3) (step S15). If the degradation determination unit 130 determines that Condition 3 is satisfied, it determines that the condition is "severely deteriorated" (step S16). Then, the degradation determination unit 130 outputs a diagnosis result signal including a shaft deterioration diagnosis result SWR indicating "severely deteriorated" to the notification device 20 (step S18). The notification device 20 displays the fact that the condition is "severely deteriorated" based on the diagnosis result signal.

[0043] If the deterioration determination unit 130 determines that condition 3 is not satisfied, it determines that the current diagnosis result is the same as the previous one (step S17). The deterioration determination unit 130 then outputs a diagnosis result signal including the same shaft deterioration diagnosis result SWR as the previous one to the notification device 20 (step S18). The notification device 20 displays the same result as the previous diagnosis result based on the diagnosis result signal. Shaft deterioration progresses gradually. Therefore, the deterioration determination unit 130 may compare the previous diagnosis result with the current diagnosis result, and if it determines that the current diagnosis result indicates a worsening degree of shaft deterioration, it may output a diagnosis result signal based on the shaft deterioration diagnosis result SWR indicating the worsening to the notification device 20.

[0044] 7 is a diagram illustrating thresholds used in the degradation determination process performed by the degradation determination unit 130 according to the first embodiment. As shown in FIG. 7 , in the degradation determination process, two thresholds (0<Y1<Y2) are set for the overcurrent count value COC. Two thresholds (0<X1<X2) are also set for the phase difference dθ. The degradation determination unit 130 determines the degree of shaft degradation based on the overcurrent count value COC and the phase difference dθ using these thresholds, diagnoses shaft degradation based on the determination, and outputs a diagnosis result signal based on the shaft degradation diagnosis result SWR to the notification device 20.

[0045] While FIG. 7 illustrates the thresholds set at equal intervals, this is not a limitation. For example, thresholds may be set at intervals other than equal intervals depending on the user's installation environment. In the first embodiment, two thresholds are set for each of the overcurrent count value COC and the phase difference dθ, dividing them into three categories. The degradation determination unit 130 then performs determination and diagnosis by dividing them into three degradation patterns: "normal," "slight degradation," and "severe degradation," resulting in the shaft degradation diagnosis result SWR. However, this is not a limitation. For example, the degradation determination unit 130 may determine and diagnose two degradation patterns: "normal" and "degraded." In this way, the number of thresholds for the overcurrent count value COC and the phase difference dθ do not have to be limited to two, and the number of categories do not have to be limited to three. Furthermore, although not particularly limited, there is a possibility that the first degradation indicator generation unit 110 or the second degradation indicator generation unit 120 may be unable to calculate the overcurrent count value COC or the phase difference dθ and thus fail to obtain either result. At this time, a diagnostic result signal including the same shaft deterioration diagnostic result SWR as the previous time may be output to the notification device 20 .

[0046] 8 is a diagram showing the flow of processing based on degradation diagnosis in the control device 10 according to the first embodiment. As described above, the degradation diagnosis unit 100 of the control device 10 executes the degradation diagnosis processing and obtains the shaft degradation diagnosis result SWR (step S20). Then, the control unit 200 of the control device 10 executes a diagnosis period determination processing based on the shaft degradation diagnosis result SWR to determine the diagnosis period until the next time the degradation diagnosis unit 100 executes the degradation diagnosis processing (step S21).

[0047] FIG. 9 is a diagram illustrating an example of a period determination process performed by the control unit 200 of the control device 10 based on the shaft deterioration diagnosis result SWR. Here, the control unit 200 determines, based on the shaft deterioration diagnosis result SWR, to shorten the diagnostic period for the next degradation diagnosis process as the deterioration of the electric motor 53 progresses. Specifically, if the shaft deterioration diagnosis result SWR is "normal," the diagnostic period is set to one year, and the degradation diagnosis unit 100 performs the next diagnosis one year later. If the shaft deterioration diagnosis result SWR is "slightly deteriorated," the diagnostic period is set to one month, and the degradation diagnosis unit 100 performs the next diagnosis one month later. If the shaft deterioration diagnosis result SWR is "severely deteriorated," the diagnostic period is set to one week, and the degradation diagnosis unit 100 performs the next diagnosis one week later.

[0048] By the control unit 200 determining a shorter diagnostic period depending on the shaft deterioration diagnosis result SWR, the number of diagnoses increases depending on the state of shaft deterioration, thereby improving the reliability of the compressor 5, which is an equipment equipped with the electric motor 53.

[0049] As described above, in the control device 10 serving as the degradation diagnosis device according to the first embodiment, the degradation diagnosis unit 100 includes the degradation first index generation unit 110 and outputs a degradation first index based on the U-phase phase θu and the V-phase phase θv obtained from the U-phase current Iu and the V-phase current Iv. The degradation determination unit 130 then determines the state of degradation based on the degradation first index and diagnoses the progression of shaft deterioration of the electric motor 53. Therefore, the degradation diagnosis unit 100 diagnoses shaft deterioration of the electric motor 53 based on the phase of the phase current, which is less affected by load fluctuations, thereby enabling highly accurate diagnosis and improving the reliability of the equipment. This allows for better service to be provided to users.

[0050] Furthermore, the first degradation index generating unit 110 of the degradation diagnosing unit 100 in the first embodiment includes a phase calculating unit 111 and a phase difference calculating unit 112. The phase calculating unit 111 calculates a U-phase phase θu and a V-phase phase θv, and the phase difference calculating unit 112 calculates a phase difference dθ indicating the degree of deviation between the balanced state in each phase and the phase of each phase current, and outputs the phase difference dθ as the first degradation index. At this time, the phase difference calculating unit 112 calculates the phase difference dθ as the amount of deviation (error amount) from 120°, which is the balanced state. This allows the phase displacement of each phase current to be calculated with high accuracy.

[0051] The deterioration diagnosis unit 100 also includes a second degradation index generation unit 120 that generates a second degradation index based on the number of abnormalities that have occurred in the compressor 5 or the electric motor 53. In the first embodiment, the second degradation index generation unit 120 has an abnormality detection unit 121 that detects whether the U-phase current Iu and the V-phase current Iv are equal to or greater than an overcurrent setting value, an abnormality counting unit 122 that counts the number of times the current is detected to be equal to or greater than the overcurrent setting value, and outputs the counted number as a second degradation index. By combining the second degradation index generated by the second degradation index generation unit 120 with the first degradation index calculated by the first degradation index generation unit 110 and related to the phase of the phase current, the accuracy of determining the progression of deterioration can be improved. However, deterioration diagnosis can be performed using only the second degradation index generated by the second degradation index generation unit 120.

[0052] Furthermore, in the control device 10 according to the first embodiment, the control unit 200 determines the period for performing the next diagnosis based on the shaft deterioration diagnosis result SWR. At this time, the control unit 200 determines that the diagnosis period will be shortened as shaft deterioration progresses, thereby increasing the number of diagnoses, enabling diagnosis to be performed with high accuracy and improving the reliability of the device. This allows better service to be provided to users.

[0053] Embodiment 2 Fig. 10 is a block diagram showing the configuration of a degradation diagnosis unit 100 in a control device 10 according to embodiment 2. In Fig. 10, units having the same reference numerals as those in Fig. 4 and the like perform the same processing as that described in embodiment 1 unless otherwise specified. The degradation diagnosis unit 100 in embodiment 2 differs from embodiment 1 in the configuration of the second degradation indicator generation unit 120.

[0054] The second degradation indicator generating unit 120 in the second embodiment includes an abnormality detecting unit 121, an abnormality counting unit 122, and an abnormality history storing unit 123. The abnormality history storing unit 123 further includes an abnormality accumulating unit 124 and a calculating unit 125.

[0055] As in the first embodiment, the abnormality detection unit 121 performs an abnormality detection step of detecting an abnormality related to the electric motor 53. However, in the second embodiment, the abnormality detection unit 121 detects abnormalities not only in the U-phase current Iu and the V-phase current Iv, but also in the state quantities of the compressor 5 and the bus voltage Vdc. Here, the state quantities of the compressor 5 are data related to the compressor 5, such as the temperature, internal pressure, humidity, and refrigerant. The temperature, internal pressure, and other data related to the compressor 5 are acquired by various sensors (not shown) attached to the compressor 5. Here, the state quantities of the compressor 5 used for abnormality detection are the pressure, refrigerant flow rate, temperature, and humidity. Therefore, the abnormality detection unit 121 detects seven types of abnormalities.

[0056] Similarly to the first embodiment, the anomaly counting unit 122 performs an anomaly counting step of counting the number of times an anomaly has occurred. Here, the anomaly counting unit 122 in the second embodiment counts each type of anomaly detected. For each type of anomaly detection, the anomaly counting unit 122 counts up the corresponding anomaly count A through anomaly count G when a preset threshold is exceeded. For example, when the U-phase current Iu exceeds a preset threshold, the anomaly counting unit 122 counts up the anomaly count A. On the other hand, when the U-phase current Iu is below the threshold, the anomaly counting unit 122 does not count up and leaves the value of the anomaly count A unchanged. Similarly, for the other types of detection, the counting unit 122 counts up the set threshold for each type, but does not count up when the set threshold is exceeded, and does not count up when the threshold is below the threshold.

[0057] An abnormality accumulation unit 124 of the abnormality history storage unit 123 stores the abnormality counts of the U-phase current Iu, the V-phase current Iv, the bus voltage Vdc, the refrigerant pressure, the refrigerant flow rate, the temperature, and the humidity as abnormality counts A to G, respectively. A calculation unit 125 calculates the total value of the various abnormality counts A to G accumulated by the abnormality accumulation unit 124. The calculation unit 125 outputs the calculation result as an abnormality degree ABN, which is a second deterioration index.

[0058] The deterioration determination unit 130 performs a deterioration determination process similar to that described in embodiment 1 based on the phase difference dθ, which is the first deterioration index, and the abnormality degree ABN, which is the second deterioration index, to determine whether the motor 53 has shaft deterioration.

[0059] As described above, in the control device 10 serving as the degradation diagnosis device according to the second embodiment, the second degradation index generating unit 120 of the degradation diagnosis unit 100 has the anomaly detection unit 121 detect multiple types of anomalies in addition to the phase current of the electric motor 53. The anomaly counting unit 122 counts the number of times each anomaly has occurred, and the anomaly history storage unit 123 stores the counted number. The calculation unit 125 of the anomaly history storage unit 123 outputs the degree of anomaly ABN, which is the total value of the number of times anomaly has occurred counted by the anomaly counting unit 122, as the second degradation index. Therefore, the degradation diagnosis unit 100 can perform more accurate degradation diagnosis by counting the number of times anomaly has occurred based on many types of anomalies.

[0060] Embodiment 3 Fig. 11 is a block diagram showing the configuration of a degradation diagnosis unit 100 in a control device 10 according to embodiment 3. In Fig. 11, parts having the same reference numerals as in Fig. 4 etc. perform the same processing as described in embodiment 1 etc. unless otherwise specified. The degradation diagnosis unit 100 in embodiment 3 differs from embodiment 1 in the configuration of the first degradation index generation unit 110. The first degradation index generation unit 110 in embodiment 3 has a three-phase calculation unit 113, a phase calculation unit 111, a phase difference calculation unit 112 and a comparison calculation unit 114.

[0061] The three-phase calculation unit 113 calculates the remaining phase current from the two phase currents of the three-phase AC current detected by the current sensor 6. Here, the current sensor 6 detects the U-phase current Iu and the V-phase current Iv, and a current detection signal Iuv is sent to the control device 10. Therefore, the three-phase calculation unit 113 calculates the W-phase current Iw from the U-phase current Iu and the V-phase current Iv based on equation (3).

[0062]

[0063] The phase calculation unit 111 in the third embodiment calculates a U-phase phase θu, a V-phase phase θv, and a W-phase phase θw from the U-phase current Iu, the V-phase current Iv, and the W-phase current Iw. The phase calculation unit 111 also calculates a U-phase phase average value θu_ave, a V-phase phase average value θv_ave, and a W-phase phase average value θw_ave.

[0064] Furthermore, by the three-phase calculation unit 113 calculating the W-phase current Iw, the abnormality detection unit 121 of the second degradation indicator generation unit 120 can detect an abnormality not only in the U-phase current Iu and the V-phase current Iv but also in the W-phase current Iw. Therefore, the abnormality detection unit 121 in the third embodiment outputs an overcurrent detection signal OC in an ON state when at least one of the U-phase current Iu, the V-phase current Iv, and the W-phase current Iw is equal to or greater than a preset overcurrent setting value. On the other hand, when all of the U-phase current Iu, the V-phase current Iv, and the W-phase current Iw are less than the overcurrent setting value, the abnormality detection unit 121 outputs an overcurrent detection signal OC in an OFF state.

[0065] The phase difference calculation unit 112 in the third embodiment calculates a UV phase difference value dθuv, a VW phase difference value dθvw, and a WU phase difference value dθwu from the U-phase phase average value θu_ave, the V-phase phase average value θv_ave, and the W-phase phase average value θw_ave based on the following equation (4): where dθuv is the UV phase difference value, dθvw is the VW phase difference value, dθwu is the WU phase difference value, θu_ave is the U-phase phase average value, θv_ave is the V-phase phase average value, and θw_ave is the W-phase phase average value.

[0066]

[0067] 12 is a diagram comparing three-phase current vectors when the electric motor 53 according to the third embodiment is driven normally and when it is driven in a deteriorated state. When the electric motor 53 is driven, three-phase current vectors are generated. In the third embodiment, of the three-phase AC currents, the current sensor 6 detects the U-phase current Iu and the V-phase current Iv, and the three-phase calculation unit 113 calculates the W-phase current Iw. For this reason, FIG. 12 illustrates current vectors in the U-phase, V-phase, and W-phase.

[0068] When the electric motor 53 is normal, no shaft eccentricity occurs during driving, and the angle formed by the U-phase, V-phase, and W-phase current vectors is 120°, as shown by the solid line in Figure 12. On the other hand, when shaft deterioration of the electric motor 53 progresses, shaft eccentricity occurs during driving, and the angle formed by the U-phase and V-phase current vectors deviates from 120° and displaces. The phase difference calculation unit 112 in the third embodiment calculates the amount of displacement (amount of deviation) as a UV phase difference value dθuv, a VW phase difference value dθvw, and a WU phase difference value dθwu based on equation (4).

[0069] In addition, in embodiment 3, the comparison calculation unit 114 compares the UV phase difference value dθuv, VW phase difference value dθvw, and WU phase difference value dθwu calculated by the phase difference calculation unit 112, and outputs the largest phase difference dθ to the deterioration determination unit 130 as a first deterioration indicator.

[0070] The deterioration determination unit 130 performs a deterioration determination process similar to that described in embodiment 1 based on the phase difference dθ, which is the first deterioration index, and the overcurrent count value COC, which is the second deterioration index, to determine whether the motor 53 has shaft deterioration.

[0071] As described above, in the control device 10 serving as the degradation diagnosis device according to the third embodiment, the degradation first index generating unit 110 of the degradation diagnosis unit 100 includes a three-phase calculation unit 113 and calculates phase currents in the U, V, and W phases. The phase calculation unit 111 calculates a U-phase phase average value θu_ave, a V-phase phase average value θv_ave, and a W-phase phase average value θw_ave. The phase difference calculation unit 112 calculates a UV phase difference value dθuv, a VW phase difference value dθvw, and a WU phase difference value dθwu, and outputs the largest phase difference dθ as the degradation first index to the degradation determination unit 130. Therefore, by diagnosing shaft degradation in the electric motor 53 based on the degree of deviation from the phase in a three-phase balanced state, highly accurate diagnosis can be performed, improving the reliability of the equipment. This allows for better service to be provided to users.

[0072] Fourth Embodiment. Figure 13 is a block diagram showing the configurations of the degradation diagnosis unit 100 and the degradation estimation learning unit 300 in a control device 10 according to a fourth embodiment. In Figure 13, units denoted by the same reference numerals as in Figure 4, etc., perform the same processing as described in the first embodiment, etc., unless otherwise specified. The control device 10 of the fourth embodiment has a degradation estimation learning unit 300, as will be described later. The degradation diagnosis unit 100 of the fourth embodiment differs from the degradation diagnosis unit 100 of the first embodiment in the processing related to the second degradation index. Specifically, the degradation diagnosis unit 100 of the fourth embodiment has an abnormality estimation unit 126 instead of the abnormality detection unit 121 and the abnormality counting unit 122 described in the first embodiment.

[0073] The abnormality estimation unit 126 in the fourth embodiment receives as input, as data, the U-phase current Iu and V-phase current Iv from the current sensor 6, the state quantities of the compressor 5, and the bus voltage Vdc from the voltage sensor 8. The degree of abnormality ABN estimated based on a learned model created by the degradation estimation learning unit 300 (described later) is used as a second degradation index. Here, the state quantities of the compressor 5 include information on the temperature, pressure, humidity, and refrigerant described in the second embodiment, and refer to data acquired based on signals sent from various sensors (not shown) attached to the compressor 5.

[0074] Furthermore, the control device 10 in the fourth embodiment includes a degradation estimation learning unit 300 in addition to the degradation diagnosis unit 100. The degradation estimation learning unit 300 performs machine learning using data on the U-phase current Iu, the V-phase current Iv, the state quantities of the compressor 5, and the bus voltage Vdc, and creates a trained model for the abnormality estimation unit 126 to obtain an estimation result. Therefore, in the fourth embodiment, the control device 10 serves as a degradation estimation learning device and performs processing related to learning. Here, the description is given assuming that machine learning is performed using seven types of data, namely the U-phase current Iu, the V-phase current Iv, the state quantities of the compressor 5, and the bus voltage Vdc, but machine learning may be performed based on at least one type of data out of these types.

[0075] 14 is a diagram illustrating the processing flow of the degradation estimation learning unit 300 according to the fourth embodiment. The processing procedure described below is an example of a learning method. Therefore, the order of the processes performed by the degradation estimation learning unit 300 can be changed as much as possible. Furthermore, processes can be omitted, replaced, or added as appropriate depending on the content of the processes.

[0076] Deterioration estimation learning unit 300 acquires, as input data, U-phase current Iu, V-phase current Iv, state quantities of compressor 5, and bus voltage Vdc (step S30). When learning unit 310 performs the learning process, control device 10 acquires U-phase current Iu, V-phase current Iv, state quantities of compressor 5, and bus voltage Vdc when motor 53 is driven in a state in which main shaft 52 is deteriorated. This makes it possible to acquire U-phase current Iu, V-phase current Iv, state quantities of compressor 5, and bus voltage Vdc relative to the deterioration amount of main shaft 52. Here, when learning unit 310 performs the learning process, a simulation model simulating a state in which main shaft 52 is deteriorated may be used to acquire U-phase current Iu, V-phase current Iv, state quantities of compressor 5, and bus voltage Vdc when the simulation model is operated.

[0077] The learning unit 310 also acquires the known abnormality level ABN as a label (step S31). Here, the known abnormality level ABN refers to, for example, the amount of deterioration and wear (e.g., 50 μm, 100 μm, or 150 μm) that the main shaft 52 of the compressor 5 has experienced from a normal state. The higher the abnormality level ABN, the greater the degree of deterioration.

[0078] The learning unit 310 acquires a set of data (hereinafter referred to as a training data set) consisting of the input data acquired in step S30 and the label acquired in step S31 (step S32). Then, the learning unit 310 performs machine learning. The learning unit 310 performs machine learning based on the training data set to construct a trained model. This enables the learning unit 310 to associate the degree of deterioration of the compressor 5 with the state quantities of the compressor 5.

[0079] In the fourth embodiment, the machine learning performed by the learning unit 310 on the compressor 5 is learning with teacher data using a neural network configured by combining perceptrons. Specifically, the learning unit 310 provides the neural network with a teacher data set consisting of input data indicating the state of current in the electric motor 53 and a label corresponding to the deterioration state of the compressor 5. The learning unit 310 then performs a process of repeating learning while changing the weighting value for each perceptron so that the output of the neural network is the same as the label. During the learning process, the learning unit 310 repeatedly performs a process called back-propagation (back-propagation) to adjust the weighting value so that the error in the output of each perceptron is reduced.

[0080] In this way, the learning unit 310 performs a process of learning the features of the teacher data set and inductively acquires a trained model for estimating results from the input data. As a result, the learning unit 310 adjusts the weighting values ​​by learning with the teacher data as described above, thereby eliminating errors between the labels and the output data.

[0081] In this way, through the learning with teacher data, the learning unit 310 obtains, as a learning result, a trained model for determining the abnormality degree ABN from the U-phase current Iu, the V-phase current Iv, the state quantities of the compressor 5, and the bus voltage Vdc. Then, the learning unit 310 saves and stores data related to the trained model constructed by the learning unit 310 through the learning with teacher data in the database storage unit 320 as a pattern generation function PGF (step S33). The pattern generation function PGF saved in the database storage unit 320 is used when the abnormality estimation unit 126 executes estimation processing, as described below.

[0082] Here, the learning unit 310 may update the pattern generation function PGF by periodically executing the processes of steps S30 to S33. Alternatively, the learning unit 310 may update the pattern generation function PGF by executing the processes of steps S30 to S33 each time the deterioration state of the compressor 5 changes. Although the learning unit 310 stores the pattern generation function PGF using the U-phase current Iu, the V-phase current Iv, the state quantities of the compressor 5, and the bus voltage Vdc as input data, not all types of data may be used. For example, the learning unit 310 may use at least one type of input data, such as the U-phase current Iu, to construct a trained model and store it as the pattern generation function PGF. Furthermore, although not particularly limited, the learning unit 310 may have a multi-layered neural network and perform machine learning using so-called deep learning.

[0083] 15 is a diagram showing the flow of processing in the abnormality estimation unit 126 of the degradation diagnosis unit 100 according to the fourth embodiment. The abnormality estimation unit 126 estimates the degree of abnormality ABN from the input U-phase current Iu, V-phase current Iv, state quantities of the compressor 5, and bus voltage Vdc. The abnormality estimation unit 126 acquires the pattern generation function PGF stored in the database storage unit 320 (step S40). The abnormality estimation unit 126 also acquires the U-phase current Iu, V-phase current Iv, state quantities of the compressor 5, and bus voltage Vdc as input data (step S41). The abnormality estimation unit 126 then inputs the data on the U-phase current Iu, V-phase current Iv, state quantities of the compressor 5, and bus voltage Vdc into the pattern generation function PGF to calculate the degree of abnormality ABN (step S42).

[0084] As described above, in the fourth embodiment, the control device 10 includes the deterioration estimation learning unit 300. The deterioration estimation learning unit 300 obtains a trained model that the learning unit 310 uses to determine the abnormality degree ABN from the U-phase current Iu, the V-phase current Iv, the state quantities of the compressor 5, and the bus voltage Vdc. The database storage unit 320 of the deterioration estimation learning unit 300 then stores the trained model as a pattern generation function PGF. In the deterioration diagnosis process, the abnormality estimation unit 126 inputs the actually obtained data of the U-phase current Iu, the V-phase current Iv, the state quantities of the compressor 5, and the bus voltage Vdc into the pattern generation function PGF to estimate and calculate the abnormality degree ABN. Therefore, machine learning can more accurately obtain the abnormality degree ABN, which is the second deterioration index. This allows for more accurate shaft deterioration diagnosis, thereby improving the reliability of the equipment.

[0085] Fifth Embodiment Fig. 16 is a block diagram showing the configuration of the deterioration determination unit 130 of the deterioration diagnosis unit 100 according to the fifth embodiment. The deterioration diagnosis unit 100 according to the fifth embodiment differs from that according to the first embodiment in the processing performed by the deterioration determination unit 130. Fig. 16 shows the processing contents performed by the deterioration determination unit 130 according to the fifth embodiment, broken down into each part. The deterioration determination unit 130 according to the fifth embodiment has a first deterioration determination unit 131, a second deterioration determination unit 132, and a comparison determination unit 133.

[0086] The first degradation determining unit 131 determines the first degradation degree MUF based on the first degradation index, which is the phase difference dθ calculated by the phase difference calculating unit 112. For example, the first degradation determining unit 131 compares the phase difference dθ with preset thresholds Y1 and Y2, and determines the first degradation degree MUF to be either "normal," "slightly deteriorated," or "severely deteriorated."

[0087] Here, the magnitude relationship of the threshold value for the phase difference dθ is set as 0<Y1<Y2. The first degradation determination unit 131 determines that the phase difference dθ is "normal" when it is equal to or greater than 0 and less than Y1. The first degradation determination unit 131 determines that the phase difference dθ is "slightly deteriorated" when it is equal to or greater than Y1 and less than Y2. The first degradation determination unit 131 determines that the phase difference dθ is "severely deteriorated" when it is equal to or greater than Y2.

[0088] The second degradation determination unit 132 determines the second degradation degree MWest based on the second degradation index, which is the overcurrent count value COC counted by the abnormality counting unit 122. For example, the second degradation determination unit 132 compares the overcurrent count value COC with preset thresholds X1 and X2, and determines the second degradation degree MWest to be either "normal," "slightly deteriorated," or "severely deteriorated."

[0089] Here, the magnitude relationship of the threshold value with respect to the overcurrent count value COC is set as 0<X1<X2. The second degradation determination unit 132 determines that the overcurrent count value COC is "normal" when it is equal to or greater than 0 and less than X1. The second degradation determination unit 132 determines that the overcurrent count value COC is "minor degradation" when it is equal to or greater than X1 and less than X2. The second degradation determination unit 132 determines that the overcurrent count value COC is "major degradation" when it is equal to or greater than X2.

[0090] The comparison / determination unit 133 outputs a shaft deterioration diagnosis result SWR based on the first deterioration level MUF determined by the first deterioration determination unit 131 and the second deterioration level MWest determined by the second deterioration determination unit 132. The comparison / determination unit 133 compares the first deterioration level MUF with the second deterioration level MWest and outputs the greater deterioration level as the shaft deterioration diagnosis result SWR. Here, if the deterioration levels obtained when the comparison / determination unit 133 compares the first deterioration level MUF with the second deterioration level MWest are the same, the comparison / determination unit 133 outputs the deterioration level as the shaft deterioration diagnosis result SWR. For example, if the first deterioration level MUF is "normal" and the second deterioration level MWest is "slightly deteriorated," the comparison / determination unit 133 outputs "slightly deteriorated" as the shaft deterioration diagnosis result SWR.

[0091] FIG. 17 is a diagram illustrating thresholds used in the degradation determination process performed by the degradation determination unit 130 according to the fifth embodiment. As shown in FIG. 17, two thresholds (0<Y1<Y2) are set for the phase difference dθ. Two thresholds (0<X1<X2) are also set for the overcurrent count value COC. The comparison determination unit 133 determines the degree of shaft degradation based on the phase difference dθ and the overcurrent count value COC using these thresholds. The comparison determination unit 133 then diagnoses the shaft degradation based on the greater degree of shaft degradation and outputs a diagnosis result signal based on the shaft degradation diagnosis result SWR to the notification device 20.

[0092] While FIG. 17 shows the thresholds set at equal intervals, this is not a limitation. For example, thresholds may be set at intervals other than equal intervals in advance depending on the user's installation environment. In the fifth embodiment, two thresholds are set for each of the phase difference dθ and the overcurrent count value COC, dividing them into three categories. The deterioration determination unit 130 then performs determination and diagnosis by dividing them into three deterioration patterns: "normal," "slight deterioration," and "severe deterioration," and outputs the shaft deterioration diagnosis result SWR. However, this is not a limitation. For example, two deterioration patterns, "normal" and "deteriorated," may be used.

[0093] As described above, in the fifth embodiment, in the degradation determination unit 130 of the degradation diagnosis unit 100, the comparison determination unit 133 compares the first degradation level MUF determined by the first degradation determination unit 131 with the second degradation level MWest determined by the second degradation determination unit 132. The degradation determination unit 130 then determines the greater degree of degradation between the first degradation level MUF and the second degradation level MWest as the shaft degradation diagnosis result SWR, and outputs a diagnosis result signal to the notification device 20. Therefore, even if the degree of either the first degradation index or the second degradation index is biased, shaft degradation can be diagnosed with high accuracy, thereby improving the reliability of the device. This allows for better service to be provided to users.

[0094] Sixth Embodiment. FIG. 18 is a block diagram illustrating an example configuration of a degradation diagnosis system SYS2 according to a sixth embodiment. In FIG. 18, parts denoted by the same reference numerals as in FIG. 4 and the like perform the same processing as described in the first embodiment and the like, unless otherwise specified. In the degradation diagnosis system SYS2 according to the sixth embodiment, the device in which the processing of the second degradation indicator generation unit 120 is performed is different from that of the degradation diagnosis system SYS1. Specifically, in the degradation diagnosis system SYS2 according to the sixth embodiment, the second degradation indicator generation unit 120, which was included in the degradation diagnosis unit 100 in the first to fifth embodiments, is included in a cloud server 30 external to the control device 10. Therefore, in the sixth embodiment, the degradation diagnosis device includes not only the control device 10 but also the cloud server 30. The cloud server 30 is, for example, a device that performs cloud storage processing in a cloud service.

[0095] A signal including data on the phase current of the electric motor 53 and the state quantities of the compressor 5 is sent to the cloud server 30 from a refrigeration cycle apparatus (not shown) or the like via an electric communication line 40. The cloud server 30 has a server recording device 31 configured with a nonvolatile auxiliary storage device such as a flash memory capable of long-term data storage, and records data related to the state or control of the refrigeration cycle apparatus. In this example, the cloud server 30 records, in particular, data on the U-phase current Iu, the V-phase current Iv, the bus voltage Vdc, the refrigerant pressure, the refrigerant flow rate, the temperature, and the humidity related to the compressor 5. The cloud server 30 also has a server control device 32, which sends the abnormality degree ABN, which serves as the second degradation index described in the second embodiment, to the control device 10 based on the recorded data.

[0096] The deterioration determination unit 130 of the deterioration diagnosis unit 100 performs a deterioration determination process to determine the progression of deterioration in the motor 53 of the compressor 5 based on the abnormality degree ABN, which is the second deterioration index, and the phase difference dθ, which is the first deterioration index, contained in the signal sent via the telecommunications line 40.

[0097] As described above, in the degradation diagnosis system SYS2 according to the sixth embodiment, the cloud server 30 functions as the second degradation index generating unit 120 and sends the abnormality degree ABN, which serves as the second degradation index, to the control device 10. Therefore, the degradation diagnosis unit 100 of the control device 10 according to the sixth embodiment does not need to perform the processing of the second degradation index generating unit 120, and the load of processing, etc. on the control device 10 can be reduced.

[0098] In particular, as described in the second embodiment and elsewhere, when detecting many types of anomalies and counting the number of occurrences of the anomalies, the amount of processing and storage required increases. By having the cloud server 30 function as the second deterioration index generating unit 120, even if the number of types of anomalies to be detected increases, the cloud server 30 can perform processes such as the anomaly detection step and the anomaly counting step. Therefore, the deterioration diagnosis unit 100 can perform highly accurate diagnosis based on the determination using the anomaly degree ABN for many types of anomalies, thereby improving the reliability of the equipment. This allows for better service to be provided to users.

[0099] Here, in the sixth embodiment, the cloud server 30 has been described as functioning as the second degradation indicator generating unit 120, but this is not limited to this. For example, the cloud server 30 may perform part of the functions of the degradation diagnosis device, such as the function of the degradation determining unit 130. Furthermore, for example, the cloud server 30 may perform the degradation diagnosis process as a degradation diagnosis device instead of the control device 10.

[0100] Embodiment 7 In the above-described embodiments 1 to 6, the compressor 5, which is a device having a rotating shaft, has been described as the device for which degradation diagnosis is performed, but this is not limited to this. The present invention can be applied to any device having an electric motor 53 that rotates a rotating shaft using electric current. It can also be applied to a generator or the like.

[0101] The above-described first to seventh embodiments are illustrative and not restrictive. The contents of the disclosure are intended to encompass all modifications within the scope of the claims and equivalent meanings. The embodiments are intended to be implemented, as far as possible, either individually or in combination.

[0102] REFERENCE SIGNS LIST 1 AC power supply, 2 Rectifier circuit, 3 Electrolytic capacitor, 4 Inverter circuit, 5 Compressor, 6 Current sensor, 7 Wiring, 8 Voltage sensor, 10 Control device, 11 Processing device, 12 Storage device, 20 Notification device, 30 Cloud server, 31 Server recording device, 32 Server control device, 40 Telecommunications line, 51 Suction pipe, 52 Main shaft, 53 Electric motor, 54 Lubricating oil, 55 Oil pump, 56 Auxiliary bearing, 57 Main bearing, 58 Compression mechanism, 59 Discharge pipe, 100 Deterioration diagnosis unit, 110 First deterioration index generation unit, 111 Phase calculation unit, 112 Phase difference calculation unit, 113 Three-phase calculation unit, 114 Comparison calculation unit, 120 Second deterioration index generation unit, 121 Abnormality detection unit, 122 Abnormality counting unit, 123 Abnormality history storage unit, 124 Abnormality accumulation unit, 125 Calculation unit, 126 Abnormality estimation unit, 130 Deterioration determination unit, 131 First deterioration determination unit, 132 Second deterioration determination unit, 133 Comparison determination unit, 200 Control unit, 300 Deterioration estimation learning unit, 310 Learning unit, 320 Database storage unit, SYS1, SYS2 Deterioration diagnosis system.

Claims

1. A degradation diagnosis device for diagnosing degradation in equipment equipped with an electric motor, comprising: a degradation diagnosis unit for diagnosing a degree of degradation of the equipment, the degradation diagnosis unit comprising: a first degradation index generation unit for outputting a first degradation index obtained from each phase of at least two phase currents flowing through the electric motor; and a degradation determination unit for determining a degradation state of the electric motor based on the first degradation index.

2. A degradation diagnosis device according to claim 1, wherein the first degradation index generating unit calculates the phase, calculates the degree of deviation between the phase and an equilibrium state of each phase, and outputs the degree of deviation as the first degradation index.

3. The degradation diagnosis device according to claim 2, wherein the first degradation index generating unit has: a phase calculation unit which calculates the phase of each of the phase currents from the phase currents for two phases; and a phase difference calculation unit which calculates a phase difference between the two phases and calculates an amount of displacement of the phase difference from 120° as the first degradation index.

4. The degradation diagnosis device according to claim 2, wherein the first degradation index generating unit has: a three-phase calculation unit which calculates the phase current of the remaining phase from the phase currents of two of the three phases; a phase calculation unit which calculates the phase of each of the phase currents; and a phase difference calculation unit which calculates phase differences of each of the three phases and calculates an amount of displacement of each of the phase differences from 120° as the first degradation index.

5. A degradation diagnosis device according to claim 3 or 4, wherein the phase calculation unit calculates an average value of phases based on a plurality of the phase currents in each phase, and sets the average value as the phase of the phase current.

6. A deterioration diagnosis device as claimed in any one of claims 2 to 5, comprising: a second deterioration index generating unit having an abnormality detection unit which detects an abnormality relating to the equipment; and an abnormality counting unit which counts the number of occurrences of the abnormality detected by the abnormality detection unit and outputs the number of occurrences as a second deterioration index, wherein the deterioration determination unit diagnoses deterioration of the motor based on the first deterioration index and the second deterioration index.

7. A deterioration diagnosis device as described in claim 6, wherein the abnormality detection unit determines whether the current flowing through the motor has exceeded a preset threshold value, and the abnormality counting unit counts the number of times that the current flowing through the motor has exceeded the set threshold value.

8. The degradation diagnosis device according to claim 6, wherein the abnormality detection unit detects abnormalities of a plurality of types related to the equipment, the abnormality counting unit counts the number of occurrences of the abnormalities of each of the plurality of types, and the device has an abnormality history storage unit that stores the number of occurrences of each of the plurality of types.

9. The deterioration diagnosis device according to claim 8, wherein the second deterioration index is a value obtained by adding up the number of occurrences of the abnormality in a plurality of the types.

10. A degradation diagnosis device as described in any one of claims 6 to 9, wherein the degradation determination unit determines that the greater the first degradation index and / or the second degradation index, the more advanced the degradation is.

11. A degradation diagnosis device as claimed in any one of claims 1 to 10, further comprising a control unit which determines the time interval at which the degradation diagnosis unit performs diagnosis based on the degradation state determined by the degradation determination unit.

12. A deterioration diagnosis system comprising: the deterioration determination unit in the deterioration diagnosis device described in any one of claims 1 to 5; a server recording device which records data relating to an equipment equipped with an electric motor contained in a signal sent via a telecommunications line; and a server control device which detects abnormalities relating to the equipment, counts the number of detected abnormalities, and sends a signal including the second deterioration index via the telecommunications line, the number of occurrences being a second deterioration index, wherein the deterioration determination unit diagnoses deterioration of the electric motor based on the first deterioration index and the second deterioration index sent via the telecommunications line.

13. A degradation estimation learning device that learns to estimate degradation in equipment equipped with an electric motor, comprising: a learning unit that performs a learning process using machine learning on at least one type of data among the phase current flowing through the electric motor, the state quantity of the equipment, and the bus voltage, and corresponding degradation amount data, as input data, to generate a trained model that estimates the degree of abnormality of the equipment; and a memory unit that stores the generated trained model.

14. A degradation diagnosis method for diagnosing degradation in equipment equipped with an electric motor, comprising: a calculation step of calculating a first degradation index based on each phase of at least two phase currents flowing through the electric motor; and a degradation determination step of determining a degradation state of the electric motor based on the first degradation index.

Images