Diagnostic equipment for electric motor load equipment

Abstract

A diagnostic device (30) for diagnosing an abnormality in an electric motor load equipment composed of an electric motor (50) driven by a power conversion device (20) and a fluid machine (70) connected to the electric motor (50), the diagnostic device (30) includes an input unit (33) for inputting specifications of the electric motor (50), a detection unit (31) for inputting current and voltage, an analysis unit (32) for performing frequency analysis on time-series data of the current or voltage to obtain the drive frequency of the electric motor (50), a calculation unit (35) for calculating the rotation speed and output of the electric motor (50) based on data stored in a memory unit (34) and for calculating the performance of the electric motor load equipment based on the calculated relationship between the rotation speed and output of the electric motor (50), and a determination unit (36) for determining an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit (35).

Description

[Technical Field]

[0001] The present disclosure relates to a diagnostic device for electric motor load equipment. [Background technology]

[0002] The performance of fluid machinery such as fans, compressors, and pumps is determined by the pressure and shaft power relative to the flow rate, and the following Patent Document 1 describes that the performance degradation of these fluid machinery can be evaluated using measurement data such as pressure.

[0003] Furthermore, Patent Document 2 describes a technique for evaluating the performance of a commercially powered pump using pressure and motor current as measurement data, and shows that it is possible to distinguish between abnormalities such as increased pressure loss in the piping, increased mechanical loss in the pump, and impeller deterioration from differences in the characteristics that appear when performance deteriorates.

[0004] Patent Document 2 listed below discloses a pump system comprising: a centrifugal pump for pumping fluid; a current detection unit for detecting the current of an electric motor that drives the centrifugal pump; a first pressure detection unit for detecting the pressure on the suction side of the centrifugal pump; a second pressure detection unit for detecting the pressure on the discharge side of the centrifugal pump; and a computing device, wherein the computing device has a head calculation unit for calculating the head of the centrifugal pump based on the detection values ​​of the first pressure detection unit and the second pressure detection unit; a current value determination unit for determining whether the detection value of the current detection unit is within an appropriate range; a head determination unit for determining whether the head is within an appropriate range; and an abnormality determination unit for determining the cause of an abnormality based on the determination results of the current value determination unit and the head determination unit. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-125275 [Patent Document 2] Japanese Patent Application Publication No. 2019-173579 Summary of the Invention [Problem to be solved by the invention]

[0006] In Patent Document 2, the performance of a fluid machine driven by a commercial power source, in which the voltage supplied to the motor and the rotation speed of the motor are fixed, is evaluated using the current of the motor. However, when the motor is driven by a power conversion device such as an inverter, the voltage and current supplied to the motor and the rotation speed of the motor vary depending on the driving conditions, making it difficult to evaluate the performance of the fluid machine using the current of the motor.

[0007] The present disclosure discloses a technology for solving the above-mentioned problems, and aims to provide a diagnostic device for electric motor load equipment that can accurately diagnose performance degradation of electric motor load equipment that includes a fluid machine, even when the electric motor is driven by a power conversion device such as an inverter. [Means for solving the problem]

[0008] The diagnostic device for electric motor load equipment disclosed herein comprises: A diagnostic device for electric motor load equipment that diagnoses an abnormality in electric motor load equipment including an electric motor driven by power converted by a power conversion device and a fluid machine connected to the electric motor, The diagnostic device comprises: Specifications of the electric motor and the specifications of the motor load equipment an input unit for inputting a detection unit that detects and inputs the current and voltage supplied from the power conversion device to the electric motor; an analysis unit that performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit, and acquires a drive frequency of the electric motor; a memory unit that stores data from the input unit, the detection unit, and the analysis unit; a calculation unit that calculates the rotation speed and output of the electric motor based on the data stored in the memory unit, and calculates the performance of the electric motor load equipment based on the calculated relationship between the rotation speed and output of the electric motor; a determination unit that determines an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit; An output unit is provided to output the diagnosis result determined by the determination unit. picture, When diagnosing an abnormality in the motor load equipment, the diagnostic device determines the abnormality in the motor load equipment based on the specifications of the motor load equipment, as well as data on the rotation speed of the motor and the output of the motor. Further, the diagnostic device for electric motor load equipment of the present disclosure includes: A diagnostic device for electric motor load equipment that diagnoses an abnormality in electric motor load equipment including an electric motor driven by power converted by a power conversion device and a fluid machine connected to the electric motor, The diagnostic device comprises: an input unit for inputting specifications of the motor, specifications of the motor load equipment, a discharge pressure of the motor load equipment, and a flow rate of the motor load equipment; a detection unit that detects and inputs the current and voltage supplied from the power conversion device to the electric motor; an analysis unit that performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit, and acquires a drive frequency of the electric motor; a memory unit that stores data from the input unit, the detection unit, and the analysis unit; a calculation unit that calculates the rotation speed and output of the electric motor based on the data stored in the memory unit, and calculates the performance of the electric motor load equipment based on the pressure and flow rate of the electric motor load equipment, and the output and rotation speed of the electric motor; a determination unit that determines an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit; an output unit that outputs the diagnosis result determined by the determination unit, Before the diagnostic device diagnoses an abnormality, the calculated performance of the electric motor load equipment is stored as normal data, When the diagnostic device diagnoses an abnormality, it judges whether an abnormality exists by comparing the calculated performance of the motor load equipment with the normal data. Further, the diagnostic device for electric motor load equipment of the present disclosure includes: A diagnostic device for electric motor load equipment that diagnoses an abnormality in electric motor load equipment including an electric motor driven by power converted by a power conversion device and a fluid machine connected to the electric motor, The diagnostic device comprises: an input unit for inputting the specifications of the motor, the specifications of the motor load equipment, the discharge pressure of the motor load equipment, the flow rate of the motor load equipment, and pressures at a plurality of locations in a piping system of the motor load equipment; a detection unit that detects and inputs the current and voltage supplied from the power conversion device to the electric motor; an analysis unit that performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit, and acquires a drive frequency of the electric motor; a memory unit that stores data from the input unit, the detection unit, and the analysis unit; a calculation unit that calculates the rotation speed and output of the electric motor based on the data stored in the memory unit, and calculates the performance of the electric motor load equipment and the performance of the piping system based on the pressure and flow rate of the electric motor load equipment, the rotation speed and output of the electric motor, and the pressure of the piping system of the electric motor load equipment; a determination unit that determines an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit; an output unit that outputs the diagnosis result determined by the determination unit, Before the diagnostic device diagnoses an abnormality, the calculated performance of the motor load equipment and the performance of the piping system are stored as normal data; When the diagnostic device diagnoses an abnormality, it judges whether an abnormality exists by comparing the calculated performance of the motor load equipment and the performance of the piping system with the normal data. [Effects of the Invention]

[0009] According to the motor load equipment diagnostic device disclosed herein, it is possible to diagnose performance degradation of motor load equipment that includes a fluid machine with high accuracy, even when the motor is driven by a power conversion device such as an inverter. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a schematic configuration of a diagnostic device for electric motor load equipment according to a first embodiment. [Figure 2] 1 is a circuit configuration diagram showing a power conversion device according to a first embodiment. [Figure 3] 1 is a block diagram showing the configuration of a diagnostic device according to a first embodiment. [Figure 4] FIG. 10 is a diagram showing an example of a performance curve of a pump. [Figure 5] FIG. 10 is a diagram showing an example of the total head H and shaft power W of a pump when the rotation speed n is changed. [Figure 6] FIG. 10 is a diagram illustrating a change in the performance curve of a pump that occurs when the performance of the pump deteriorates. [Figure 7] FIG. 2 is a diagram showing an equivalent circuit for one phase of an electric motor. [Figure 8] FIG. 2 is a diagram showing specifications of an electric motor. [Figure 9] 4 is a flowchart for calculating the output of the electric motor according to the first embodiment. [Figure 10] 4 is a flowchart for calculating the output of the electric motor according to the first embodiment. [Figure 11] 4 is a diagram showing the accuracy of the output of the electric motor calculated according to the first embodiment with respect to the load factor. FIG. [Figure 12] 4 is a diagram showing a flowchart during learning of the diagnostic device for electric motor load equipment according to the first embodiment. FIG. [Figure 13] 4 is a diagram showing a flowchart during diagnosis by the diagnostic device for electric motor load equipment according to the first embodiment. FIG. [Figure 14] FIG. 14A shows the output obtained from the learning flow of pump diagnosis, and FIG. 14B shows the output obtained from the diagnosis flow of pump diagnosis. [Figure 15] FIG. 10 is a block diagram showing a schematic configuration of a diagnostic device for electric motor load equipment according to a third embodiment. [Figure 16] FIG. 10 is a block diagram showing the configuration of a diagnostic device according to a third embodiment. [Figure 17] 11 is a flowchart for learning the performance of the motor load equipment according to the third embodiment. [Figure 18] 10 is a flowchart for diagnosing the performance of the motor load equipment according to the third embodiment. [Figure 19] FIG. 10 is a block diagram showing a schematic configuration of a diagnostic device for electric motor load equipment according to a fourth embodiment. [Figure 20] FIG. 10 is a diagram showing a diagnostic flowchart of a diagnostic device for electric motor load equipment according to a fourth embodiment. [Figure 21] FIG. 10 is a diagram showing a diagnostic flowchart of a diagnostic device for electric motor load equipment according to a fifth embodiment. [Figure 22] 1 is a block diagram showing an example of the hardware configuration of a diagnostic device for electric motor load equipment according to an embodiment; [Figure 23] FIG. 2 is a diagram showing a performance curve of a fluid machine of an electric motor load facility. DETAILED DESCRIPTION OF THE INVENTION

[0011] Embodiment 1 [Configuration of diagnostic equipment for motor load equipment] FIG. 1 is a block diagram showing a schematic configuration of a diagnostic device for motor load equipment according to a first embodiment. In this embodiment, a pump system will be described as an example of motor load equipment including fluid machinery such as a fan, a compressor, a pump, etc. The motor load equipment includes not only fluid machinery such as a fan, a compressor, a pump, etc., but also a piping system connected to the fluid machinery. 1, power from a three-phase AC power supply 10, such as a commercial power supply, is input to a power conversion device 20. The voltage, current, and frequency of the power input to the power conversion device 20 are controlled by the power conversion device 20, and the power is supplied to an electric motor 50. At least two of the three phases of current supplied from the power conversion device 20 to the electric motor 50 are detected by a current detector 41, and the three phase voltages are detected by a voltage detector . The electric power controlled by the power converter 20 drives the electric motor 50 , which then drives the pump 70 via the power transmission mechanism 60 . The pump 70 is connected to a suction pipe 82 and a discharge pipe 81 . The pump 70 transports fluid from the suction pipe 82 to the discharge pipe 81 . The current detected by the current detector 41 and the voltage detected by the voltage detector 42 are input to the diagnostic device 30. The current detector 41 and the voltage detector 42 may be built into the power conversion device 20, may be built into the diagnostic device 30, or may be externally attached.

[0012] FIG. 2 is a circuit configuration diagram showing the power conversion device according to the first embodiment. In FIG. 2, the power conversion device 20 includes a main circuit 21 and a control circuit 22. The main circuit 21 includes a converter 21A, a smoothing capacitor 21B, and an inverter 21C. Converter 21A converts AC power from AC power supply 10 into DC power and stores it in smoothing capacitor 21B. The inverter 21C converts the DC power stored in the smoothing capacitor 21B into AC power and supplies it to the electric motor 50. The converter 21A is configured by a three-phase bridge circuit having six diodes Da, and the input lines of each phase are connected to the AC power supply 10. The inverter 21C is configured by a three-phase bridge circuit including six switching elements Q, each with a diode connected in anti-parallel, and the output line of each phase is connected to the electric motor 50. The switching elements Q are, for example, IGBTs (Insulated Gate Bipolar Transistors) or MOSFETs (Metal-Oxide-Semiconductor Field Effect Transistors). The switching operation of the inverter 21C is controlled by a control signal generated by a control circuit 22. The configurations of converter 21A and inverter 21C are not limited to those shown in the drawings. Also, while main circuit 21 in Fig. 2 is shown equipped with converter 21A and connected to AC power supply 10, it is sufficient to have inverter 21C that converts DC power to AC power and supplies the power to motor 50, and converter 21A may not be necessary.

[0013] FIG. 3 is a block diagram showing the configuration of the diagnostic device according to the first embodiment. The diagnostic device 30 of the first embodiment includes a detection unit 31 , an analysis unit 32 , an input unit 33 , a memory unit 34 , a calculation unit 35 , a determination unit 36 ​​, and an output unit 37 . The input unit 33 receives input of the specifications of the electric motor 50 . The memory unit 34 stores the specifications of the electric motor 50 inputted through the input unit 33. The detector 31 receives the current detected by the current detector 41 and the voltage detected by the voltage detector 42, digitizes them, and converts them into time-series data of the current and time-series data of the voltage, respectively. The analysis unit 32 extracts the drive frequency f0 of the electric motor 50 controlled by the power conversion device 20 by performing frequency analysis on the time series data of the current or the time series data of the voltage converted by the detection unit 31. The calculation unit 35 calculates the performance of the pump 70 using the time series data of the current, the time series data of the voltage, the drive frequency f0, and the specifications of the electric motor. The determination unit 36 ​​determines whether the pump 70 is abnormal based on the calculation result of the calculation unit 35. The output unit 37 outputs the determination result of the determination unit 36. The output unit 37 may be built in the diagnostic device 30 or may output the result via a network.

[0014] [Operation of diagnostic device for motor load equipment] FIG. 4 is a diagram showing an example of a performance curve of a pump. Generally, the total head H of a pump decreases as the flow rate Q increases, and the shaft power W increases as the flow rate Q increases. The total head H of a pump is the energy that the pump gives to the fluid, expressed as a height; the shaft power W is the work input to the pump shaft; and the system head R is the pressure loss that occurs in the piping system in which the pump is operating, expressed as a height. When a pump with the performance shown in Figure 4 is connected to a piping system consisting of pipes, valves, etc., and the system head is represented by R, the pump operates at the point where the system head R intersects with the total head H. In other words, the flow rate Q, total head H, and shaft power W are Q0, H0, and W0, respectively. Furthermore, the flow rate Q, total head H, and shaft power W depend on the rotation speed n, as shown in equation (1).

[0015]

number

[0016] However, at rotation speed n0, the flow rate is Q0, the total head is H0, and the shaft power is W0, and at rotation speed n1, the flow rate is Q1, the total head is H1, and the shaft power is W1.

[0017] Figure 5 shows an example of the total head H and shaft power W when the rotation speed n is changed. However, the rotation speed n is n2 <n0<n1である。 From this, we can see that the pump's total head H and shaft power W fluctuate depending on the rotation speed n.

[0018] On the other hand, the total head H and shaft power W also decrease when the pump performance deteriorates.

[0019] 6A to 6C are diagrams illustrating changes in the performance curve of a pump that occur when the performance of the pump deteriorates. Figure 6A shows the case where the mechanical loss of the pump increases, and although the total head H does not change, the shaft power W increases. This is because the increase in mechanical loss requires the pump to output more power to ensure the same head. Figure 6B shows the case where the impeller has deteriorated, and both the total head curve and shaft power curve decrease. This is because as the pump impeller deteriorates, the work that the pump can do to the fluid decreases, and the required output also decreases accordingly. Figure 6C shows an increase in pressure loss in the piping connected to the pump. The total head curve and shaft power curve do not change, but the flow rate at the operating point decreases. As a result, the output decreases and the head increases. It can be seen that the output of the electric motor changes for all of the deterioration factors shown in FIGS. 6A to 6C.

[0020] On the other hand, when the pump is driven by a power conversion device such as an inverter and the rotation speed n changes, the motor output also changes as shown in equation (1) and Figure 5. Therefore, in order to detect pump deterioration due to the above factors, it is necessary to know the pump rotation speed in advance and accurately calculate the motor output.

[0021] The output W of the electric motor 50 is expressed by equation (2) using the drive frequency f0, torque T, and slip s.

[0022]

number

[0023] The drive frequency f0 can be determined by the analysis unit 32 performing frequency analysis on either the time series data of the current or the time series data of the voltage, and extracting the frequency component with the highest signal intensity.

[0024] The torque T can be calculated by using the current data and voltage data obtained by αβ conversion of the current detected by the current detector 41 and the voltage detected by the voltage detector 42 in equation (3).

[0025]

number

[0026] where p, i, and ψ respectively represent the number of pole pairs, current, and magnetic flux of the electric motor 50. The αβ transformation is as shown in the following equations (4-1) and (4-2).

[0027]

number

[0028] iu and iv are the u-phase current and the v-phase current supplied to the motor 50, respectively. Vuv and Vvw are line voltages determined by Vuv=Vu-Vv and Vvw=Vv-Vw, where Vu, Vv, and Vw are phase voltages, respectively. If these are used to define the flux linkage in equation (5), equation (3) can be calculated, and torque T can be found.

[0029]

number

[0030] where r1 is the stator winding resistance.

[0031] To calculate torque T with high accuracy, a bandpass filter based on the extracted drive frequency f0 can be applied to the current time series data and voltage time series data. Current and voltage are used in the process of calculating the magnetic flux linkage using equation (5) and when calculating torque using equation (3). However, the time series data of current and voltage may contain frequency components other than the drive frequency f0, which can have a significant effect on the torque calculation error. For example, if a small DC component is added to the current or voltage, the effect will be significant when integrating using equation (5). A bandpass filter is effective in eliminating such effects.

[0032] The slip s is found from the equivalent circuit per phase of the electric motor 50 shown in FIG. 7, x and r represent the reactance and resistance of the stator and rotor of the motor 50, and s represents slip. The subscripts 0, 1, and 2 represent the excitation circuit, stator, and rotor, respectively. Furthermore, Y0 is the excitation admittance, g0 is the excitation conductance, and b0 is the excitation susceptance. It is noted that when the electric motor 50 is obtained, the results of a locked test, a no-load test, and the stator winding resistance are generally available as specifications of the electric motor 50, as shown in FIG. Using the specifications of the electric motor 50 in FIG. 8, each value in FIG. 7 can be calculated. The current detected by current detector 41 and the voltage detected by voltage detector 42 correspond to I1 and V1, respectively, in Fig. 7. I1 is expressed as in equation (6) from Fig. 6, and the output Wm' of the induction machine serving as electric motor 50 is expressed as in equation (7).

[0033]

number

[0034]

number

[0035] However, reactance X is set to X = x1 + x2, and using inductance L and drive angular frequency ω0, X = ω0·L. The slip s that makes Wm' = W can be calculated from equations (2), (6), and (7) as shown in equation (8), where s2 (squared) ~ 0 is used.

[0036]

number

[0037] By applying the drive frequency f0, torque T, and slip s obtained as above to equation (2), the output W of the electric motor 50 can be obtained. However, in the process up to this point, the current used in equation (3) is I1 in Figure 7, so the calculated torque is greater than the actual torque. To reduce this effect, iron loss is taken into account as in equation (9).

[0038]

number

[0039] The second term on the right side of equation (9) corresponds to iron loss. Here, iron loss is considered, but other losses such as stray load loss may also be considered as needed. To consider the loss, the output Wm calculated using equation (9) is applied to equation (2) to correct the torque T. In addition, the slip s is corrected by applying the corrected torque T to equation (8). Furthermore, the corrected torque T and slip s are applied to equation (2) to calculate the output W.

[0040] The above steps are shown in the schematic flow chart of FIG. 9 and FIG. 10 for calculating the output of the electric motor. 9 and 10, in step S101, the specifications of the electric motor 50 are input to the input unit 33. In step S102, the detection unit 31 acquires time-series data of current and voltage using the current detector 41 and the voltage detector 42. In step S103, the analysis unit 32 extracts the drive frequency f0 by performing frequency analysis on the time-series data of the current and voltage.

[0041] In step S104, the calculation unit 35 calculates the phase voltage effective value V1.

[0042] In step S105, the calculation unit 35 estimates the torque T using equations (3), (4-1), (4-2), and (5).

[0043] In step S106, the calculation unit 35 calculates the equivalent circuit constants based on the equivalent circuit of the electric motor in FIG. 7 and the specifications of the electric motor.

[0044] In step S107, the calculation unit 35 estimates the slip s that satisfies Wm'=W based on equations (2), (7), and (8).

[0045] In step S108, the calculation unit 35 estimates the output W of the electric motor by applying the drive frequency f0, the torque T, and the slip s to equation (2).

[0046] In step S109, the calculation unit 35 calculates the output Wm, for example, as shown in equation (9), taking into account iron loss and the like.

[0047] In step S110, the calculation unit 35 corrects the torque T by applying the output Wm calculated by, for example, equation (9) to equation (2).

[0048] In step S111, the calculation unit 35 corrects the slip s by applying the corrected torque T to equation (8).

[0049] In step S112, the calculation unit 35 applies the corrected torque T and slip s to equation (2) to correct the output W of the electric motor.

[0050] Fig. 11 is a diagram showing the accuracy of the output of the electric motor calculated according to the flowcharts shown in Fig. 9 and Fig. 10 versus the load factor. The output accuracy is defined as the error rate of the following equation (10).

[0051]

number

[0052] It can be seen from FIG. 11 that the output of the electric motor is calculated with an error rate of 10% or less at drive frequencies of 40 Hz to 70 Hz by the method of this embodiment.

[0053] 12 and 13 show pump diagnosis flows according to the first embodiment, with FIG. 12 being a flowchart showing the learning stage of pump diagnosis, and FIG. 13 being a flowchart showing the diagnosis stage of pump diagnosis. 14A shows the output obtained from the learning flow of pump diagnosis in FIG. 12, and FIG. 14B shows the output obtained from the diagnosis flow of pump diagnosis in FIG.

[0054] First, a flow chart of learning pump diagnosis according to the first embodiment will be described with reference to FIG. In step S201, as shown in FIGS. 9 and 10, the output of the motor is calculated based on the specifications of the motor and the data on the current and voltage. In step S202, a driving frequency f0 of the motor is extracted by performing a frequency analysis on the time series data of the current or voltage. In step S203, the motor drive frequency f0 (Hz) and the motor rotation speed n (rpm) have the relationship n = (60 / p) × f0, where p is the number of pole pairs of the motor. Using this relationship, the motor rotation speed n is calculated from the drive frequency f0.

[0055] In step S204, before diagnosing the pump 70, the diagnostic device 30 acquires the motor rotation speed and motor output as normal data and maps the motor output relative to the motor rotation speed. At this time, since the motor rotation speed n and motor output W have the relationship of equation (1), the acquired data is dispersed near the function (theoretical value) expressed by equation (1) as shown in FIG. 14A. This dispersed data is statistically processed to define a normal range (step S205).

[0056] After a learning period defines normal ranges, pump diagnostics begin. FIG. 13 shows a flowchart of the pump diagnosis according to the first embodiment. In step S301, the output of the motor is calculated based on the specifications of the motor and the current and voltage data. In step S302, a driving frequency f0 of the motor is extracted by performing a frequency analysis on the time series data of the current or voltage. In step S303, the motor drive frequency f0 (Hz) and the motor rotation speed n (rpm) have the relationship n = (60 / p) × f0, where p is the number of pole pairs of the motor. Using this relationship, the motor rotation speed n is calculated from the drive frequency f0.

[0057] In step S304, the relationship between the rotation speed of the electric motor and the output of the electric motor is mapped as shown in FIG. 14B. In step S305, it is determined whether the data acquired in FIG. 14B is within the normal range defined during learning, and if it is outside the normal range, an abnormality warning is issued. More specifically, if the acquired data in steps S306 and S307 is higher than the normal range, as shown by the cross in Fig. 14B, it means that more power is being consumed than normal, and the corresponding abnormality is an increase in pump mechanical loss. Furthermore, in steps S306 and S308, if the acquired data is lower than the normal range, as shown by the triangle in Figure 14B, it means that less power is being consumed than normal. The corresponding abnormality is deterioration of the pump impeller or an increase in piping loss. Although an increase in pump mechanical loss, deterioration of the pump impeller, and an increase in piping loss have been mentioned here, other types of abnormalities caused by changes in power consumption may also be warned.

[0058] As described above, according to the first embodiment, A diagnostic device for electric motor load equipment that diagnoses an abnormality in electric motor load equipment including an electric motor driven by power converted by a power conversion device and a fluid machine connected to the electric motor, The diagnostic device comprises: an input unit for inputting specifications of the electric motor; a detection unit that detects and inputs the current and voltage supplied from the power conversion device to the electric motor; an analysis unit that performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit, and acquires a drive frequency of the electric motor; a memory unit that stores data from the input unit, the detection unit, and the analysis unit; a calculation unit that calculates the rotation speed and output of the electric motor based on the data stored in the memory unit, and calculates the performance of the electric motor load equipment based on the calculated relationship between the rotation speed and output of the electric motor; a determination unit that determines an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit; The output unit outputs the diagnosis result determined by the determination unit. Even when the electric motor is driven by a power conversion device such as an inverter, the performance degradation of the electric motor load equipment can be diagnosed with high accuracy.

[0059] Furthermore, the diagnostic device stores data on the number of revolutions of the motor and the output of the motor as normal data before diagnosing an abnormality in the motor load equipment, When diagnosing an abnormality in the motor load equipment, the diagnostic device determines an abnormality in the motor load equipment by comparing the rotation speed of the motor and the output data of the motor with the normal data. It is possible to diagnose performance degradation of motor load equipment with high accuracy.

[0060] Furthermore, the output of the electric motor is calculated by calculating the torque and slip of the electric motor based on the data of the current and voltage detected by the detection unit and the specifications of the electric motor, and then calculating the output of the electric motor based on the torque, the slip and the drive frequency. Even when the electric motor is driven by a power conversion device such as an inverter, the performance degradation of the electric motor load equipment can be diagnosed with high accuracy.

[0061] Embodiment 2 In the first embodiment, the diagnostic device stores the data of the motor's rotation speed and motor's output as normal data before diagnosing an abnormality in the motor load equipment, and when diagnosing an abnormality in the motor load equipment, determines whether there is an abnormality in the motor load equipment by comparing the data of the motor's rotation speed and motor's output with the normal data. In the second embodiment, in addition to the specifications of the motor, the specifications of the motor load equipment are input to the input section, and when diagnosing an abnormality in the motor load equipment, the diagnostic device determines the abnormality in the motor load equipment by comparing the specifications of the motor load equipment with the data on the motor rotation speed and motor output.

[0062] The second embodiment will be described below, focusing on the differences from the first embodiment. In the second embodiment, the input unit 33 of the diagnostic device 30 receives input of the specifications of the pump, which is a fluid machine, in addition to the specifications of the electric motor. The specifications of a pump, which is a fluid machine, include data on the total head H and shaft power W of the pump at the rated rotation speed, as shown in Figure 4. The relationship between the rotation speed n and shaft power W is expressed as in equation (1) above, so if the rated rotation speed is nr and the rated output is Wr, the relationship between any rotation speed n and shaft power W can be obtained. The relationship between the rotation speed n and the flow rate Q can also be calculated in the same way using equation (1). As described above, by obtaining information on the specifications of the pump, which is a fluid machine, it is possible to obtain normal data on the output (shaft power) and rotation speed of the electric motor when the pump is normal. Then, by defining the normal range shown in FIG. 14A in the first embodiment, diagnostic device 30 can determine whether there is an abnormality in the motor load equipment during diagnosis.

[0063] In the second embodiment, it is possible to eliminate the need for learning to acquire the rotation speed and output of the electric motor, which was necessary in the first embodiment. When defining the normal range, it is also possible to take into consideration the flow rate range of the pump, which is a fluid machine, and the calculation error of the output calculated by the diagnostic device 30.

[0064] As described above, according to the second embodiment, In addition to the motor specifications, the specifications of the motor load equipment are entered into the input section. When diagnosing an abnormality in the motor load equipment, the diagnostic device determines an abnormality in the motor load equipment by comparing the specifications of the motor load equipment with the rotation speed and output of the motor. This eliminates the need for a learning process for acquiring normal data on the rotation speed and output of the motor before diagnosis.

[0065] Embodiment 3 [Configuration of diagnostic equipment for motor load equipment] FIG. 15 is a block diagram showing a schematic configuration of a diagnostic device for motor load equipment according to the third embodiment. In this embodiment, a pump system will be described as an example of motor load equipment including fluid machinery such as a fan, a compressor, a pump, etc. The motor load equipment includes not only fluid machinery such as a fan, a compressor, a pump, etc., but also a piping system connected to the fluid machinery. 15 , power from a three-phase AC power supply 10 from a commercial power source or the like is input to a power conversion device 20. The voltage, current, and frequency of the power input to the power conversion device 20 are controlled by the power conversion device 20, and the power is supplied to an electric motor 50. The current supplied from the power conversion device 20 to the electric motor 50 is detected by a current detector 41, and the voltage is detected by a voltage detector . The electric power controlled by the power converter 20 drives the electric motor 50 , which then drives the pump 70 via the power transmission mechanism 60 . The pump 70 sucks the fluid from the first tank 91 through the suction pipe 82 and transports it to the second tank 92 through the discharge pipe 81. The discharge pressure of the pump 70 is detected by a pressure detector 43 attached to the pump system. The flow rate of fluid delivered from pump 70 is detected by a flow detector 44 attached to the pump system. The current detected by the current detector 41 , the voltage detected by the voltage detector 42 , the pressure detected by the pressure detector 43 , and the flow rate detected by the flow rate detector 44 are input to the diagnostic device 30 . The current detector 41 and the voltage detector 42 may be built into the power conversion device 20, the diagnostic device 30, or may be externally attached. The pressure detector 43 and the flow rate detector 44 may be built into the pump 70.

[0066] The power conversion device 20 is similar to the power conversion device 20 described in the first embodiment, and therefore a description thereof will be omitted.

[0067] FIG. 16 is a block diagram showing the configuration of a diagnostic device 30 according to the third embodiment. The input unit 33 receives inputs of the specifications of the electric motor 50, the specifications of the pump 70 which is a fluid machine, the pressure detected by the pressure detector 43, the flow rate detected by the flow rate detector 44, and the pressures of the first tank 91 and the second tank 92. The memory unit 34 stores the information input to the input unit 33 . Here, if the pump system is pressure-controlled, the target pressures of the first tank 91 and the second tank 92 may be input. Alternatively, pressure detectors may be provided in the first tank 91 and the second tank 92, and the detected pressures may be input. Alternatively, if the approximate value can be determined, such as atmospheric pressure, without providing pressure detectors, that pressure may be input. The detection unit 31 receives the current detected by the current detector 41 and the voltage detected by the voltage detector 42, digitizes them, and converts them into time-series data of the current and time-series data of the voltage, respectively. The analysis unit 32 extracts the drive frequency f0 of the electric motor 50 controlled by the power conversion device 20 by performing frequency analysis on the time series data of the current or the time series data of the voltage converted by the detection unit 31. The calculation unit 35 calculates the performance of the pump using time series data of the current, time series data of the voltage, drive frequency f0, specifications of the electric motor 50, specifications of the pump 70 which is a fluid machine, the pressure detected by the pressure detector 43, the flow rate detected by the flow rate detector 44, and the pressures of the first tank 91 and the second tank 92. The determination unit 36 ​​determines whether the pump 70 is abnormal based on the calculation result of the calculation unit 35. The output unit 37 outputs the determination result of the determination unit 36. The output unit 37 may be built into the diagnostic device 30 or may output the result via a network. The pressure detected by the pressure detector 43 and the flow rate detected by the flow rate detector 44 that are input to the input unit 33 may be input to the detection unit 31 and stored in the memory unit 34 as respective time-series data.

[0068] [Operation of diagnostic device for motor load equipment] The head equations (pressure equations) of the pump system according to the third embodiment shown in FIG. 15 are expressed as equations (11) and (12).

[0069]

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[0070]

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[0071] In equations (11) and (12), p0, ρ, g, H, p1, R1, u, and Δh1 are the pressure of the first tank 91, the mass density of the fluid, the acceleration of gravity, the total head of the pump 70, the discharge pressure of the pump 70, the flow resistance of the suction-side piping 82, the flow velocity, and the difference in elevation from the first tank 91 to the pump 70. Also, p2, R2, and Δh2 are the pressure of the second tank 92, the flow resistance of the discharge-side piping 81, and the difference in elevation from the pump 70 to the second tank 92. The flow velocity u can be calculated by the equation u=Q / S, where S is the cross-sectional area of ​​the pipe determined from the suction port diameter or discharge port diameter of the pump 70 and Q is the flow rate detected by the flow detector 44 . The total head H and bore of the pump 70 are generally pump-specific values ​​and are disclosed by the pump manufacturer. Furthermore, to accurately determine the flow velocity u, it is necessary to use the cross-sectional area of ​​each point in the piping, but in the subsequent diagnostic flow, it is sufficient to simply calculate the flow velocity from the bore of the pump 70.

[0072] FIG. 17 shows a flowchart for learning the performance of the motor load equipment (pump system) according to the third embodiment. First, in step S501, the specifications of the electric motor 50, the specifications of the pump 70 which is a fluid machine, the pressure detected by the pressure detector 43, the flow rate detected by the flow rate detector 44, and the pressures of the first tank 91 and the second tank 92 are input to the input unit 33. Next, in steps S502, S503, and S504, the calculation unit 35 calculates the rotation speed and output of the electric motor 50 using the same method as in the first or second embodiment. Next, in step S505, the total head H and shaft power W are corrected using equation (1) based on the rotation speed of the electric motor 50 calculated in step S503.

[0073] Next, in step S506, the total head H corrected in step S505, the detected flow rate Q, and pressures p0, p1, p2, etc. are used in equations (11) and (12) to estimate the flow rate resistance R1 of the suction-side piping 82 and the flow rate resistance R2 of the discharge-side piping 81. At this time, the elevation difference Δh1 from the first tank 91 to the pump 70 and the elevation difference Δh2 from the pump 70 to the second tank 92 may be given based on the installation status of the pump system. In this case, the flow rate resistances R1 and R2 can be calculated with high accuracy. However, if these are unknown, the flow rate resistances R1 and R2 may be calculated assuming Δh1 = 0 and Δh2 = 0. In this case, the flow rate resistances R1 and R2 will be estimated in a form that includes the elevation difference, but this does not affect the subsequent diagnostic flow.

[0074] Next, in step S507, in order to evaluate the performance degradation of the pump 70 independently of the flow rate or rotation speed, the total head coefficient kh and the shaft power coefficient kw shown in equations (13) and (14) are introduced, where H and W are the total head and shaft power before degradation, and H' and W' are the total head and shaft power after degradation.

[0075]

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[0076]

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[0077] When estimating the flow resistances R1 and R2, the total head H disclosed by the manufacturer is used, so kh = 1 during learning. On the other hand, since there is no item to estimate for the shaft power coefficient kw as in equation (11), the shaft power coefficient kw is calculated by using W as the output of the electric motor 50 from which the shaft power W' disclosed by the manufacturer was calculated and using this in equation (14).

[0078] Through the above learning flow, the total head coefficient kh, the shaft power coefficient kw, the flow resistance of the piping, and the rotation speed and output of the motor 50 are determined. Then, in step S508, these data are collected for a certain period of time and statistically processed to define their normal ranges. In the subsequent diagnostic flow, deviations from the normal ranges defined through learning are evaluated to diagnose abnormalities in the motor load equipment.

[0079] FIG. 18 is a flowchart for diagnosing the performance of a pump system of motor load equipment according to the third embodiment. First, in step S601, the calculation unit 35 reads the total head coefficient, the shaft power coefficient, and the output of the electric motor 50 calculated in the learning flow of FIG. Next, in steps S602, S603, and S604, the calculation unit 35 calculates the rotation speed and output of the electric motor 50 using the same method as in the first and second embodiments. Then, in step S605, the determination unit 36 ​​determines whether the rotation speed and output of the electric motor 50 are within the learned normal range.

[0080] In step S605, if the rotation speed and output of the electric motor 50 are outside the normal range, this indicates some kind of abnormality as shown in FIG. In step S606, pressure and flow rate are measured to isolate abnormal types of performance degradation. In step S607, the flow resistances R1 and R2 estimated during learning and the measured flow rate and pressure are used in equations (11) and (13) to determine the total head coefficient kh, which indicates the degree of deterioration. Furthermore, the calculated output of the electric motor 50 is used in equation (14) to determine the shaft power coefficient kw, which indicates the degree of deterioration. The magnitude relationship of these coefficients and the output are used to separate out abnormalities. Specifically, if the shaft power coefficient kw increases and there is no change in the total head coefficient kh, this indicates that the pump's mechanical losses are increasing. If the shaft power coefficient kw decreases and the total head coefficient kh also decreases, it indicates that the pump impeller is deteriorating. If the shaft power coefficient kw and the total head coefficient kh do not change and the output of the motor 50 decreases, this indicates a pipe blockage. To identify a pipe blockage, an increase in the flow resistance of the pipe may be confirmed using the same flow as during learning. Furthermore, the abnormalities listed here are merely exemplary, and other abnormalities that affect the total head coefficient, shaft power coefficient, flow resistance, rotational speed, and output may also be used.

[0081] In step S609, the result of the abnormality diagnosis in step S608 is presented to the user, and the user is asked to confirm whether or not the operation can be continued. If the user decides to continue operation, the process proceeds to step S610, where the normal range defined during learning is redefined so that the current measurement data is not judged to be abnormal. Furthermore, in step S609, if the user decides to stop operation, the process proceeds to the subsequent maintenance of the motor load equipment.

[0082] As described above, according to the third embodiment, The input unit of the diagnostic device inputs the specifications of the motor, the specifications of the motor load equipment, the discharge pressure of the motor load equipment, and the flow rate of the motor load equipment, the detection unit detects and inputs the current and voltage supplied from the power conversion device to the electric motor; the analysis unit performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit to obtain a drive frequency of the electric motor; The memory unit stores data from the input unit, the detection unit, and the analysis unit; The calculation unit calculates the rotation speed and output of the motor based on the data stored in the memory unit, and calculates the performance of the motor load equipment based on the pressure and flow rate of the motor load equipment, and the output and rotation speed of the motor; the determination unit determines an abnormality in the motor load equipment based on the performance of the motor load equipment calculated by the calculation unit; Before the diagnostic device diagnoses an abnormality, the calculated performance of the motor load equipment is stored as normal data, When the diagnostic device diagnoses an abnormality, it determines the abnormality by comparing the calculated performance of the motor load equipment with normal data. This makes it possible to isolate the type of abnormality in the motor load equipment, and diagnose performance degradation of the motor load equipment with higher accuracy.

[0083] In addition, the diagnostic device calculates the performance of the motor load equipment by estimating the changes in the total head and shaft power of the motor load equipment based on the pressure and flow rate of the motor load equipment, and the output and rotation speed of the motor. This makes it possible to isolate the type of abnormality in the motor load equipment, and diagnose performance degradation of the motor load equipment with higher accuracy.

[0084] Embodiment 4 FIG. 19 is a block diagram showing a schematic configuration of a diagnostic device for motor load equipment according to the fourth embodiment. In this embodiment, a pump system will be described as an example of load equipment configured with fluid machinery such as fans, compressors, pumps, etc. The motor load equipment includes not only fluid machinery such as fans, compressors, pumps, etc., but also piping systems connected to the fluid machinery. In this embodiment, the differences from the third embodiment will be mainly described.

[0085] The fourth embodiment is characterized in that pressures at a plurality of locations in the pump piping system, including the discharge pressure of the pump 70, are input to the input unit 33 of the diagnostic device 30. FIG. 19 shows an example in which a first pressure detector 43a is attached to a discharge-side pipe 81 of a pump 70, and a second pressure detector 43b is attached to a suction-side pipe 82. 19 includes a plurality of (two in this case) first pressure detectors 43a and second pressure detectors 43b, as well as a pressure detector 43 that detects the discharge pressure of pump 70, a flow rate detector 44 that detects the flow rate of pump 70, a current detector 41 that detects the current supplied from power conversion device 20 to electric motor 50, and a voltage detector 42 that detects three-phase voltage, and data detected by these detectors is input to diagnostic device 30. This detected data may be input to diagnostic device 30 wirelessly, or may be stored in a mass storage device such as the cloud.

[0086] The following equation (15) is the head equation (pressure equation) in a pipe separated by two adjacent different pressure detectors 43x and 43y. The pressure detectors 43x and 43y correspond to the first pressure detector 43a and the second pressure detector 43b in the pump system of FIG.

[0087]

number

[0088] In equation (15), px, py, Rxy, and Δhxy respectively represent the pressure detected by pressure detector 43x, the pressure detected by pressure detector 43y, the flow resistance of the piping between pressure detectors 43x and 43y, and the elevation difference between pressure detectors 43x and 43y.

[0089] As in the third embodiment, in the fourth embodiment, the rotation speed and output of the electric motor 50, the shaft power coefficient kw, and the flow resistances R1 and R2 of the pipes are estimated during the learning period of the diagnostic device 30. However, in the fourth embodiment, the flow resistance Rxy of each part of the piping is estimated by using, in equation (15), pressure values ​​detected by a plurality of pressure detectors (pressure detectors 43x and 43y) installed in the piping of the pump system. At this time, the elevation difference Δhxy may be given from the installation conditions of the pump system. In this case, the flow resistance Rxy can be calculated with high accuracy. On the other hand, if the elevation difference Δhxy is unknown, the flow resistance Rxy may be calculated by setting Δhxy = 0. In this case, the flow resistance Rxy will be estimated in a form that includes the elevation difference, but this does not affect the subsequent diagnostic flow.

[0090] In the fourth embodiment, as in the third embodiment, the normal ranges of the rotation speed of the motor 50, the output of the motor 50, the shaft power coefficient, and the flow resistance of the piping are defined according to the learning flow shown in FIG. During diagnosis, an abnormality diagnosis is performed according to the diagnostic flow shown in Fig. 18, as in the third embodiment. However, if the total head coefficient and shaft power coefficient remain unchanged and the output of the motor 50 decreases, and a clogged pipe is detected, the clogged pipe location is identified according to the flow for identifying the clogged pipe location shown in Fig. 20. 20, if a pipe blockage is detected in step S701, the flow resistance of each pipe is calculated in step S702, and the location of the pipe blockage is identified in step S703. After identifying the clogged pipe location, the system presents the identified location to the user, as in the diagnostic flow of Figure 18, and leaves it to them to decide whether to continue operation.

[0091] As described above, according to the fourth embodiment, The input unit of the diagnostic device inputs the specifications of the motor, the specifications of the motor load equipment, the discharge pressure of the motor load equipment, the flow rate of the motor load equipment, and pressures at multiple locations in the piping system of the motor load equipment, the detection unit detects and inputs the current and voltage supplied from the power conversion device to the electric motor; the analysis unit performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit to obtain a drive frequency of the electric motor; The memory unit stores data from the input unit, the detection unit, and the analysis unit; The calculation unit calculates the rotation speed and output of the motor based on the data stored in the memory unit, and calculates the performance of the motor load equipment and the flow resistance of the piping system based on the pressure and flow rate of the motor load equipment and the output and rotation speed of the motor; the determination unit determines an abnormality in the motor load equipment based on the performance of the motor load equipment calculated by the calculation unit; Before the diagnostic device diagnoses an abnormality, the calculated performance of the motor load equipment is stored as normal data, When the diagnostic device diagnoses an abnormality, it determines the abnormality by comparing the calculated performance of the motor load equipment with normal data. This makes it possible to isolate the type of abnormality in the motor load equipment, and diagnose performance degradation of the motor load equipment with higher accuracy.

[0092] The diagnostic device estimates changes in the total head and shaft power of the motor load equipment and changes in the flow resistance of the piping system based on the pressure and flow rate of the motor load equipment, the output and rotation speed of the motor, and the pressure of the piping system of the motor load equipment, thereby calculating the performance of the motor load equipment and calculating the pressure of the piping system of the motor load equipment, and diagnosing abnormalities in the motor load equipment and the piping system. This makes it possible to isolate the type of abnormality in the motor load equipment, and diagnose performance degradation of the motor load equipment with higher accuracy.

[0093] Embodiment 5 In the fifth embodiment, an abnormality type that could not be detected by the diagnostic device in the first to fourth embodiments is set as a target variable, and explanatory variables are extracted using the current, voltage, and drive frequency stored in the memory unit of the diagnostic device, and the performance of the motor load equipment and piping system calculated by the calculation unit, so that when the abnormality type reappears, an abnormality is determined based on the explanatory variables.

[0094] FIG. 21 is a flowchart showing a diagnosis process performed by the motor load equipment diagnosis device according to the fifth embodiment. As shown in FIG. 21, in step S801, if an abnormality occurs that the diagnostic device 30 fails to detect, the type of the abnormality is set as the objective variable. The abnormality types that the diagnostic device 30 cannot detect are abnormalities that the diagnostic device 30 judged to be normal but in fact were not, and the mechanical loss of the pump, the pump impeller, piping loss, or clogged locations in the piping are set as the objective variables. For example, suppose that the diagnostic device 30 judges the condition to be "normal," but a pipe clog is discovered by another method, such as a diagnosis by a field worker. In this case, the diagnostic device 30 has failed to diagnose the pipe clog. The cause of the diagnosis failure is that the normal range for a pipe clog has been set incorrectly, for example, it has been set too wide. Therefore, the normal range is redefined using the measured data, namely the motor output, motor rotation speed, current, voltage, detected pressure value, flow rate, total head coefficient, shaft power coefficient, and flow resistance of the pipe. That is, in steps S802 and S803, explanatory variables are extracted using data used by diagnostic device 30 when diagnosing an abnormality, specifically, the rotation speed and output of electric motor 50, the current detected by current detector 41, the voltage detected by voltage detector 42, the pressure at each location detected by a pressure detector attached to the pump system, the flow rate detected by a flow rate detector attached to the pump system, the total head coefficient used when diagnosing an abnormality, the shaft power coefficient, and the flow resistance at each location in the piping. For example, SHAP (SHapley Additive exPlanations), which is a method of quantifying the influence of each explanatory variable and the degree of influence, can be applied as a method for extracting explanatory variables that led to a prediction failure. If the explanatory variables can be extracted in step S803, it is possible to deal with abnormalities that have previously failed to be detected by defining normal ranges in step S804. Note that a machine learning model with training data may be applied to this process.

[0095] As described above, according to the fifth embodiment, The abnormality types that the diagnostic device could not detect are set as the objective variables, By extracting explanatory variables using the current, voltage, and drive frequency stored in the memory section of the diagnostic device, and the performance of the motor load equipment and piping system calculated by the calculation section, the abnormality can be determined using the explanatory variables when an abnormality reappears, making it possible to respond to an abnormality that the diagnostic device failed to detect.

[0096] The diagnostic device 30 according to the first to fifth embodiments includes a processor 1000 and a storage device 1010, as shown in Fig. 22, which is an example of hardware. The storage device 1010 includes a volatile storage device such as a random access memory and a non-volatile auxiliary storage device such as a flash memory, both of which are not shown. Alternatively, the diagnostic device 30 may include a hard disk auxiliary storage device instead of the flash memory. The processor 1000 executes a program input from the storage device 1010. In this case, the program is input from the auxiliary storage device to the processor 1000 via the volatile storage device. The processor 1000 may also output data such as calculation results to the volatile storage device of the storage device 1010, or may store the data in the auxiliary storage device via the volatile storage device.

[0097] Embodiment 6 In the above embodiment, a pump has been described as an example of a fluid machine that constitutes the motor load equipment. In this embodiment, it will be explained that the fluid machine constituting the electric motor load equipment of the present disclosure can be applied not only to a pump but also to a fan or a compressor.

[0098] FIG. 23 is a diagram showing a comparison of the performance curves of a pump, a fan, and a compressor. In FIG. 23, P1 and P2 indicate the performance curves of the pump, FA indicates the performance curve of the fan, and COM indicates the performance curve of the compressor. As shown in Figure 23, the performance curves of pumps, fans, and compressors are defined by the pressure versus flow rate. However, the fluid handled is liquid in the case of pumps, and gas in the case of fans and compressors. The output of the electric motor is defined in accordance with the relationship between flow rate and pressure in Figure 23. Also, the pressure in Figure 23 is referred to as the total head H shown in Figure 3 for pumps, as static pressure for fans, and as pressure ratio for compressors, but the essential meaning remains the same.

[0099] When considering a fan or compressor instead of a pump, the same equations as for a pump can essentially be used. However, when the type of fluid handled is a gas, the fluid density ρ becomes extremely small when using a fan or compressor. Due to this effect, it is common to ignore terms related to height. Specifically, the applicable equation is changed as follows. The other formulas are the same as for a pump. That is, the above-mentioned formula (11) is changed to the following formula (16), the above-mentioned formula (12) is changed to the following formula (17), and the above-mentioned formula (15) is changed to the following formula (18).

[0100]

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[0101]

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[0102]

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[0103] Although the present disclosure describes various exemplary embodiments and examples, the various features, aspects, and functions described in one or more embodiments are not limited to application to a particular embodiment, but may be applied to the embodiments alone or in various combinations. Therefore, countless variations not exemplified are conceivable within the scope of the technology disclosed in this specification, including, for example, cases where at least one component is modified, added, or omitted, and cases where at least one component is extracted and combined with components of another embodiment. [Explanation of symbols]

[0104] 10 AC power supply, 20 power conversion device, 21 main circuit, 21A converter, 21B smoothing capacitor, 21C inverter, 22 control circuit, 30 diagnostic device, 31 detection unit, 32 analysis unit, 33 input unit, 34 memory unit, 35 calculation unit, 36 judgment unit, 37 output unit, 41 current detector, 42 voltage detector, 43 pressure detector, 43a first pressure detector, 43b second pressure detector, 44 flow rate detector, 50 electric motor, 60 power transmission mechanism, 70 pump, 81 discharge side piping, 82 suction side piping, 91 first tank, 92 second tank, 1000 processor, 1010 storage device.

Claims

1. A diagnostic device for electric motor load equipment that diagnoses an abnormality in electric motor load equipment including an electric motor driven by power converted by a power conversion device and a fluid machine connected to the electric motor, The diagnostic device comprises: an input unit for inputting specifications of the electric motor and specifications of the electric motor load equipment; a detection unit that detects and inputs the current and voltage supplied from the power conversion device to the electric motor; an analysis unit that performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit, and acquires a drive frequency of the electric motor; a memory unit that stores data from the input unit, the detection unit, and the analysis unit; a calculation unit that calculates the rotation speed and output of the electric motor based on the data stored in the memory unit, and calculates the performance of the electric motor load equipment based on the calculated relationship between the rotation speed and output of the electric motor; a determination unit that determines an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit; an output unit that outputs the diagnosis result determined by the determination unit, The diagnostic device is a diagnostic device for electric motor load equipment that, when diagnosing an abnormality in the electric motor load equipment, determines an abnormality in the electric motor load equipment based on the specifications of the electric motor load equipment, as well as data on the rotation speed of the electric motor and the output of the electric motor.

2. A diagnostic device for electric motor load equipment that diagnoses an abnormality in electric motor load equipment including an electric motor driven by power converted by a power conversion device and a fluid machine connected to the electric motor, The diagnostic device comprises: an input unit for inputting specifications of the motor, specifications of the motor load equipment, a discharge pressure of the motor load equipment, and a flow rate of the motor load equipment; a detection unit that detects and inputs the current and voltage supplied from the power conversion device to the electric motor; an analysis unit that performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit, and acquires a drive frequency of the electric motor; a memory unit that stores data from the input unit, the detection unit, and the analysis unit; a calculation unit that calculates the rotation speed and output of the electric motor based on the data stored in the memory unit, and calculates the performance of the electric motor load equipment based on the pressure and flow rate of the electric motor load equipment, and the output and rotation speed of the electric motor; a determination unit that determines an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit; an output unit that outputs the diagnosis result determined by the determination unit, Before the diagnostic device diagnoses an abnormality, the calculated performance of the electric motor load equipment is stored as normal data, The diagnostic device for electric motor load equipment determines whether an abnormality has occurred by comparing the calculated performance of the electric motor load equipment with the normal data when diagnosing an abnormality.

3. A diagnostic device for electric motor load equipment as described in claim 2, wherein the diagnostic device calculates the performance of the electric motor load equipment by estimating changes in the total head and shaft power of the electric motor load equipment based on the pressure and flow rate of the electric motor load equipment, and the output and rotation speed of the electric motor.

4. A diagnostic device for electric motor load equipment that diagnoses an abnormality in electric motor load equipment including an electric motor driven by power converted by a power conversion device and a fluid machine connected to the electric motor, The diagnostic device comprises: an input unit for inputting the specifications of the motor, the specifications of the motor load equipment, the discharge pressure of the motor load equipment, the flow rate of the motor load equipment, and pressures at a plurality of locations in a piping system of the motor load equipment; a detection unit that detects and inputs the current and voltage supplied from the power conversion device to the electric motor; an analysis unit that performs frequency analysis on at least one of the time series data of the current and the time series data of the voltage detected by the detection unit, and acquires a drive frequency of the electric motor; a memory unit that stores data from the input unit, the detection unit, and the analysis unit; a calculation unit that calculates the rotation speed and output of the electric motor based on the data stored in the memory unit, and calculates the performance of the electric motor load equipment and the performance of the piping system based on the pressure and flow rate of the electric motor load equipment, the rotation speed and output of the electric motor, and the pressure of the piping system of the electric motor load equipment; a determination unit that determines an abnormality in the electric motor load equipment based on the performance of the electric motor load equipment calculated by the calculation unit; an output unit that outputs the diagnosis result determined by the determination unit, Before the diagnostic device diagnoses an abnormality, the calculated performance of the motor load equipment and the performance of the piping system are stored as normal data; When diagnosing an abnormality, the diagnostic device determines whether there is an abnormality by comparing the calculated performance of the motor load equipment and the performance of the piping system with the normal data.

5. The diagnostic device for electric motor load equipment described in claim 4 calculates the performance of the electric motor load equipment by estimating changes in the total head and shaft power of the electric motor load equipment and changes in the flow resistance of the piping system based on the pressure and flow rate of the electric motor load equipment, the output and rotation speed of the electric motor, and the pressure of the piping system of the electric motor load equipment, and calculates the pressure of the piping system of the electric motor load equipment to diagnose abnormalities in the electric motor load equipment and the piping system.

6. 6. A diagnostic device for electric motor load equipment according to claim 1, wherein the output of the electric motor is calculated by calculating the torque and slip of the electric motor based on the current and voltage data detected by the detection unit and the specifications of the electric motor, and calculating the output of the electric motor based on the torque, the slip and the drive frequency.

7. The abnormality type that the diagnostic device could not detect is set as a response variable; 6. A diagnostic device for electric motor load equipment according to claim 1, wherein explanatory variables are extracted using the current, voltage, and drive frequency stored in the memory unit of the diagnostic device, and the performance of the electric motor load equipment and piping system calculated by the calculation unit, and when the abnormality type reappears, an abnormality is determined using the explanatory variables.

8. 6. The diagnostic device for electric motor loaded equipment according to claim 1, wherein the fluid machinery constituting the electric motor loaded equipment is one of a pump, a fan, and a compressor.

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