Motor monitoring device

Abstract

In the present invention, a monitoring device comprises a storage unit that stores a first model of an electric motor when an operating state of the electric motor is normal, and a model search unit that generates a second model of the electric motor when the operating state of the electric motor is abnormal. When a difference between a measured temperature of a temperature detector and an estimated temperature of the temperature detector based on the the first model deviates from a determination range, the model search unit generates the second model in which a coefficient related to heat transfer between a model of a stator core and a model of the outside air of the first model, such that an estimated temperature of the temperature detector based on the second model corresponds to the measured temperature of the temperature detector.

Description

[Technical Field]

[0001] This invention relates to a monitoring device for electric motors. [Background technology]

[0002] It is known that electric motors are installed in machines to operate their components. When an electric motor is driven, the stator core, coils fixed to the stator core, bearings, etc., generate heat, causing the temperature of the electric motor to rise. If the temperature of the electric motor becomes too high, it may not operate correctly, its components may be damaged, or its lifespan may be shortened. For this reason, it is preferable to cool the electric motor while it is in operation. Electric motors can be cooled by natural airflow. Alternatively, they can be cooled by supplying air using a cooling machine or the like (for example, Japanese Patent Application Publication No. 2015-167436).

[0003] The actual temperature of an electric motor when it is driven can be detected by temperature sensors attached to the components of the motor. Alternatively, simulations for estimating the temperature of an electric motor are known (e.g., Japanese Patent Publication No. 11-262102). In particular, a method is known for estimating the temperature of an electric motor using a thermal model that takes into account the heat capacity of the components of the motor and the heat transfer between the components (e.g., International Publication No. 2022 / 085543). In a thermal model, a heat transfer coefficient or thermal resistance can be set between each component to calculate the heat transfer between the components. The thermal model can calculate the temperature of each component. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2015-167436 [Patent Document 2] Japanese Patent Application Publication No. 11-262102 [Patent Document 3] International Publication No. 2022 / 085543 [Patent Document 4] Japanese Patent Publication No. 2010-287971 [Patent Document 5] Japanese Patent Publication No. 2006-311735 [Overview of the project] [Problems that the invention aims to solve]

[0005] During the operation of an electric motor, it can be effectively cooled by supplying air to it using a blower. However, prolonged use of an electric motor may reduce its cooling performance. For example, the blower may malfunction, the air vents may become clogged with dust or other foreign matter, or sludge may accumulate on the surface of the motor. Continuing to operate an electric motor with reduced cooling performance may cause it to overheat.

[0006] Methods for detecting a blower malfunction include attaching a temperature sensor to the blower or attaching a magnetic sensor to the fan to detect the fan's rotation speed. To detect blockages in the blower's air vents, a flow sensor can be attached to detect the airflow rate inside the blower.

[0007] Alternatively, workers can periodically visually inspect the motor to detect blower malfunctions, clogged air vents, and sludge buildup. If a blower malfunction is found, the blower can be repaired or replaced. If foreign matter is found in the air vents or sludge has accumulated, the motor can be cleaned.

[0008] However, when adopting sensors such as a magnetic sensor, a temperature sensor, or a flow rate sensor that detect the rotational speed of the fan of the blower, there is a problem that a new sensor must be attached to the electric motor in order to detect a decrease in the cooling performance of the electric motor. Alternatively, in the method of visually inspecting the electric motor by an operator regularly, it is necessary to disassemble the device including the electric motor, which is troublesome for the operator.

Means for Solving the Problems

[0009] The monitoring device for an electric motor according to an aspect of the present disclosure includes a state acquisition unit that acquires an operating state of the electric motor including a measured temperature detected by a temperature detector attached to the electric motor. The monitoring device includes a temperature estimation unit that calculates an estimated temperature of the temperature detector based on the model of the electric motor. The monitoring device includes a storage unit that stores a first model of the electric motor when the operating state of the electric motor is normal. The monitoring device includes a model search unit that generates a second model of the electric motor when the operating state of the electric motor is abnormal. The monitoring device includes a performance estimation unit that estimates the cooling performance of the electric motor. The model of the electric motor includes a model of a component of the electric motor including a stator core and a model of outside air. A heat capacity is set for at least one of the models of the components. A coefficient related to heat transfer is set between the models of the components of the electric motor and between the model of the stator core and the model of outside air. The model search unit generates a second model in which the coefficient related to heat transfer between the model of the stator core and the model of outside air of the first model is changed so that the estimated temperature of the temperature detector based on the second model corresponds to the measured temperature when the difference between the measured temperature of the temperature detector and the estimated temperature of the temperature detector based on the first model deviates from a predetermined determination range. The performance estimation unit calculates a variable related to the cooling performance of the electric motor based on the coefficient related to heat transfer between the model of the stator core and the model of outside air in the first model and the coefficient related to heat transfer between the model of the stator core and the model of outside air in the second model. multiple The model search unit generates a second model without changing any parameters other than the coefficients related to heat transfer between the stator core model and the ambient air model in the first model.

Advantages of the Invention

[0010] ​According to an embodiment of the present disclosure, a motor monitoring device can provide information regarding the cooling performance of the motor. [Brief explanation of the drawing]

[0011] [Figure 1] This is a block diagram of the monitoring device for the machine and electric motor in an embodiment. [Figure 2] This is a schematic cross-sectional view of the first electric motor in the embodiment. [Figure 3] This is a first model of the first electric motor in the embodiment. [Figure 4] This graph illustrates the motor operation patterns used to set the parameters in the first model of the electric motor. [Figure 5] This is the first model of the second electric motor in the embodiment. [Figure 6] This graph shows the results of a simulation using the parameters set in the parameter calculation unit. [Figure 7] This graph shows the temperature change of the temperature sensor when the electric motor is operating normally. [Figure 8] This graph shows the temperature change of the temperature detector when the electric motor is operating abnormally. [Figure 9] This is a second model of the first electric motor in the embodiment. [Modes for carrying out the invention]

[0012] The motor monitoring device in this embodiment will be described with reference to Figures 1 to 9. The motor monitoring device in this embodiment has the function of estimating the temperature of predetermined components of the motor using a thermal model of the motor.

[0013] In this embodiment, the temperature actually measured by the temperature sensor is referred to as the measured temperature. The temperature of the components estimated using the electric motor model is referred to as the estimated temperature. In this embodiment, an example of estimating the temperature of a temperature sensor attached to the stator coil, one of the components of the electric motor, is described. The electric motor monitoring device of this embodiment calculates variables related to the cooling performance of the electric motor. The monitoring device then displays information regarding the electric motor's cooling performance on a display unit or notifies other devices.

[0014] Figure 1 is a block diagram of the machine and electric motor monitoring device in this embodiment. The machine 1 in this embodiment includes an electric motor 10 that drives the components of the machine 1 and a machine control device 41 that controls the machine 1. The machine control device 41 in this embodiment includes an arithmetic processing unit (computer). The machine control device 41 includes a CPU (Central Processing Unit) as a processor. The machine control device 41 has RAM (Random Access Memory) and ROM (Read Only Memory), etc., connected to the CPU via a bus.

[0015] The machine 1 in this embodiment is a numerically controlled machine. The machine 1 is driven based on command statements described in a pre-created operation program 45. The machine control device 41 includes a storage unit 42 that stores the operation program 45 and an operation control unit 43 that generates operation commands for the electric motor 10 based on the operation program 45. The machine 1 includes a drive device 44 that includes an electrical circuit that supplies electricity to the electric motor 10 based on the operation commands generated by the operation control unit 43. The electric motor 10 is driven by the power supplied by the drive device 44.

[0016] The memory unit 42 can be configured with a non-temporary storage medium capable of storing information. For example, the memory unit 42 can be configured with a storage medium such as a volatile memory, a non-volatile memory, a magnetic storage medium, or an optical storage medium. The operation control unit 43 corresponds to a processor that operates according to an operation program 45. The processor functions as the operation control unit 43 by reading the operation program 45 and executing the controls defined in the operation program 45.

[0017] Such a machine 1 can be any machine equipped with an electric motor 10. For example, machine 1 can be a machine tool that processes a workpiece. The electric motor 10 can be a spindle motor that rotates a tool, or a feed axis motor that moves the table or spindle head along a predetermined coordinate axis.

[0018] Figure 2 is a cross-sectional view of the first electric motor of this embodiment. Referring to Figures 1 and 2, the first electric motor 10 is a synchronous electric motor in which the rotor 11 has magnets 18. The electric motor 10 comprises the rotor 11 and the stator 12. The stator 12 includes a stator core 20 made of a magnetic material and coils 16 fixed to the stator core 20. The stator core 20 is formed, for example, of a plurality of magnetic steel plates stacked in the axial direction of the shaft 13. The coils 16 include, for example, windings wound around the stator core 20 and resin parts that fix the windings.

[0019] The rotor 11 is fixed to a rod-shaped shaft 13. The rotor 11 includes a rotor core 17 fixed to the outer surface of the shaft 13 and made of a magnetic material, and a plurality of magnets 18 fixed to the rotor core 17. The magnets 18 in this embodiment are permanent magnets.

[0020] The shaft 13 is connected to other members to transmit rotational force. The shaft 13 rotates around the rotation axis RA. The shaft 13 is supported by bearings 14 and 15, which act as bearings. In this embodiment, in the electric motor 10, the side on which the shaft 13 is connected to other members is referred to as the front side. The side opposite the front side is referred to as the rear side. In the example shown in Figure 2, arrow 91 indicates the front side of the electric motor 10.

[0021] The electric motor 10 includes a front housing 21 and a rear housing 22. The stator core 20 of the stator 12 is supported by the housings 21 and 22. Housing 21 supports a bearing 14. A bearing support member 24 that supports a bearing 15 is fixed to housing 22. Housings 21 and 22 rotatably support the shaft 13 via the bearings 14 and 15. A rear cover 23 that closes off the internal space of housing 22 is fixed to the rear end of housing 22.

[0022] A rotational position detector 32 for detecting the rotational position or rotational speed of the shaft 13 is located at the rear end of the shaft 13. In this embodiment, the rotational position detector 32 is configured as an encoder. A temperature detector 31 for detecting the temperature of the coil 16 is fixed to the coil 16 of the stator 12. In this embodiment, the temperature detector 31 is configured as a thermistor. The outputs of the temperature detector 31 and the rotational position detector 32 are input to the machine control device 41.

[0023] Examples of components of the electric motor 10 include a rotor 11, rotor core 17, magnet 18, stator 12, stator core 20, coil 16, housing 21, 22, shaft 13, rear cover 23, bearing support member 24, bearings 14, 15, temperature sensor 31, and rotational position sensor 32. The components of the electric motor 10 are not limited to this configuration, and any part of the electric motor 10 can be used. For example, a case covering the stator may be used as a component of the electric motor 10.

[0024] The electric motor 10 of this embodiment includes a cooler for cooling the body of the electric motor. The cooler of this embodiment includes a blower 29 for supplying cooling air. The blower 29 of this embodiment is fixed to the stator core 20 via a cylindrical member 25. The cylindrical member 25 is fixed to the stator core 20. The inside of the cylindrical member 25 constitutes an air passage.

[0025] The blower 29 includes a cooling fan 27, a case 28, and an electric motor that rotates the cooling fan 27. In this embodiment, the blower 29 is positioned such that the rotation axis of the cooling fan 27 coincides with the rotation axis RA of the shaft 13. That is, the rotor 11 and the cooling fan 27 are arranged coaxially. The cooling fan 27 is not limited to this configuration and can be positioned at any location to blow air onto the body of the electric motor.

[0026] The stator core 20 of this embodiment has a through hole 26a for the circulation of cooling air. The through hole 26a extends from one end face to the other end face of the stator core 20 along the axial direction of the rotor 11. The housing 21 has a through hole 26b for the circulation of air. The through hole 26b communicates with the through hole 26a. In addition, the case 28 of the blower 29 has an air hole 28a through which air flows.

[0027] When the blower 29 is driven, the cooling fan 27 rotates, causing air to flow in the direction indicated by arrow 91. The cooling air flows into the case 28 through the air hole 28a of the case 28. The cooling air circulates inside the case 28 and inside the cylindrical member 25. The cooling air circulates through the air passage between the housing 22 and the cylindrical member 25. The cooling air circulates through the through hole 26a of the stator core 20 and the through hole 26b of the housing 21, as indicated by arrow 92. The cooling air cools the rear housing 22, the stator core 20, and the front housing 21. The flow of the cooling air is not limited to this configuration, and the cooling air may flow in the opposite direction to arrow 91.

[0028] The cooling unit for the motor body in this embodiment consists of a blower, but is not limited to this configuration. Any device for cooling the motor body can be used as the cooling unit. For example, a cooling unit that cools the motor with cooling water or an electronic cooling unit including a Peltier element may be used.

[0029] In this embodiment, the motor monitoring device 2 calculates an estimated temperature based on the temperature output of a temperature sensor 31 located on the motor 10. In this embodiment, the temperature output of a temperature sensor 31 located on the coil 16 of the stator 12 is estimated. In particular, the motor monitoring device 2 estimates the change in temperature of the temperature sensor 31 over time.

[0030] The motor monitoring device 2 includes a processing unit (computer) including a CPU as a processor. The monitoring device 2 includes a storage unit 51 that stores information related to the monitoring of the motor 10. The storage unit 51 can be made of a non-temporary storage medium capable of storing information. For example, the storage unit 51 can be made of a storage medium such as volatile memory, non-volatile memory, magnetic storage medium, or optical storage medium. The monitoring device 2 includes a display unit 52 that displays information related to the motor 10. The display unit 52 can be made of any display panel such as a liquid crystal display panel or an organic EL (Electro-Luminescence) display panel.

[0031] The monitoring device 2 includes a temperature estimation unit 53 that calculates the estimated temperature of the temperature detector 31. The temperature estimation unit 53 includes a loss calculation unit 54 that calculates the amount of heat generated due to the primary copper loss of the coil 16 and the amount of heat generated due to the iron loss of the stator core 20 based on the operation command of the electric motor 10. The temperature estimation unit 53 also includes a temperature calculation unit 55 that estimates the temperature of the temperature detector 31 using an electric motor model (thermal model). The temperature calculation unit 55 calculates the estimated temperature of the temperature detector 31 based on the amount of heat generated due to the primary copper loss and iron loss, the heat capacity of the models of each component, and coefficients relating to heat transfer between the models of the components.

[0032] The monitoring device 2 in this embodiment has a function to calculate parameters included in the model of the electric motor. The parameters include the heat capacity set in the model of the component part of the electric motor 10 and coefficients relating to heat transfer between the component model parts. The monitoring device 2 includes a parameter calculation unit 63 that calculates the parameters included in the first model of the electric motor when the electric motor is operating normally.

[0033] The monitoring device 2 includes a state acquisition unit 62 that acquires the operating state of the electric motor 10 when it is actually driven. The operating state of the electric motor 10 includes the measured temperature detected by the temperature detector 31 attached to the electric motor 10. The operating state of the electric motor 10 includes the operation command of the electric motor 10 generated by actually driving the electric motor 10 and the rotational speed output from the rotational position detector 32. The operation command of the electric motor 10 can be acquired from the operation control unit 43. The state acquisition unit 62 can also acquire the ambient temperature from an ambient temperature detector 33 that detects the temperature of the environment in which the machine 1 is located. The ambient temperature detector 33 is, for example, positioned to detect the temperature around the machine 1.

[0034] The parameter calculation unit 63 in this embodiment calculates parameters so that the change in the estimated temperature of the temperature sensor, calculated by the motor model, corresponds to the change in the actual measured temperature. The parameter calculation unit 63 in this embodiment can set the parameters of the motor model using machine learning.

[0035] The loss calculation unit 54 of the temperature estimation unit 53 calculates the amount of heat generated by the coil 16 and the stator core 20 based on the operation command generated by the operation control unit 43 and the rotational speed detected by the rotational position detector 32. Furthermore, the temperature calculation unit 55 calculates the estimated temperature of the temperature detector based on the amount of heat generated by the coil 16 and the stator core 20.

[0036] The parameter calculation unit 63 calculates the estimated temperature of the temperature detector 31 using the temperature estimation unit 53. The parameter calculation unit 63 includes an evaluation unit 66 that evaluates the estimated temperature of the temperature detector by comparing the estimated temperature of the temperature detector 31 with the measured temperature of the temperature detector 31 obtained by the state acquisition unit 62. The parameter calculation unit 63 includes a parameter modification unit 67 that changes the parameter values ​​based on the evaluation results of the evaluation unit 66.

[0037] Each of the above units—temperature estimation unit 53, loss calculation unit 54, and temperature calculation unit 55—corresponds to a processor that operates according to a program. Each of the units—state acquisition unit 62, parameter calculation unit 63, evaluation unit 66, and parameter modification unit 67—corresponds to a processor that operates according to a program. The processor functions as each unit by performing the control defined in the program.

[0038] Figure 3 shows a first model that models the heat transfer of the first electric motor in this embodiment. The first model of the electric motor is a thermal model that simulates the normal operating state of the electric motor. The thermal model of the electric motor includes models of multiple components. The thermal model includes parameters such as the heat capacity of the components and coefficients related to heat transfer between the components. The first model 10a of the electric motor in this embodiment includes models of the main components that make up the first electric motor 10. Model 10a of the electric motor includes a rotor model 11a, a stator core model 20a, and a coil model 16a wound around the stator core. Model 10a of the electric motor also includes a temperature sensor model 31a for detecting the temperature of the coil 16.

[0039] Referring to Figure 2, an air layer is interposed between the rotor 11 and the stator core 20. Furthermore, an air layer is interposed between the rotor 11 and the coil 16. Model 10a of the electric motor in this embodiment includes model 35a of the air layer. Also, model 10a of the electric motor includes model 36a of the ambient air as a model of the air around the electric motor 10. Thus, in the model of the electric motor in this embodiment, the air layer and ambient air are generated as models of the components of the electric motor.

[0040] The temperature detected by the temperature sensor 31 is approximately equal to the temperature of the coil 16. However, under certain conditions, due to the small heat capacity of the temperature sensor 31, the temperature detected by the temperature sensor 31 may differ from the temperature of the coil 16. For this reason, in this embodiment, a model 31a of the temperature sensor 31 is generated as one of the component models. Alternatively, the heat capacity of the temperature sensor 31 may be set to zero, and the calculation may be performed assuming that the temperature of the temperature sensor model is the same as the temperature of the component model to which the temperature sensor is attached.

[0041] In the electric motor model 10a, several parameters are set, including coefficients related to heat capacity and heat transfer. At least one component model has a set heat capacity. The coil model 16a, the stator core model 20a, the air layer model 35a, the rotor model 11a, and the temperature sensor model 31a each have variables for temperature T1, T2, T3, T4, T5 and constants for heat capacity C1, C2, C3, C4, C5. Additionally, the ambient air model 36a has a variable for temperature T r It is set.

[0042] Heat from one component of the electric motor 10 is transferred to other components. A coefficient relating to heat transfer is set between the models of each component of the electric motor 10. As the coefficient relating to heat transfer, a heat transfer coefficient or a coefficient obtained by multiplying the heat transfer coefficient by the contact area between the components can be used. In this example, a coefficient obtained by multiplying the heat transfer coefficient by the contact area is defined.

[0043] A heat transfer coefficient ha is set between the stator core model 20a and the coil model 16a. A heat transfer coefficient hc1 is set between the air layer model 35a and the coil model 16a. A heat transfer coefficient hc2 is set between the air layer model 35a and the stator core model 20a. A heat transfer coefficient hc3 is set between the air layer model 35a and the rotor model 11a. A heat transfer coefficient hd is set between the coil model 16a and the temperature sensor model 31a. Furthermore, to simulate the release of heat from the stator core 20 to the outside air, a heat transfer coefficient hb is set between the stator core model 20a and the outside air model 36a.

[0044] In the electric motor model 10a of this embodiment, the heat generated by the components is the primary copper loss P generated in the coil 16 of the stator 12. c1 This is taken into consideration. The coil model 16a is input with the amount of heat generated due to primary copper loss. In addition, the iron loss P of the stator core 20 caused by the magnetic force of the magnet 18 of the rotor 11 is taken into consideration. i This is taken into consideration. The stator core model 20a is input with the amount of heat generated due to iron loss.

[0045] Heat transfers between components such as the coil and stator core, depending on the magnitude of the heat transfer coefficient. Furthermore, the temperature of each component rises or falls based on the difference between the heat input and heat output. The temperature change rates of each component of the first model 10a of the first electric motor shown in Figure 3 can be expressed by the following equations (1) to (5). The temperature change rate for each component can be calculated by dividing the difference between the heat input and heat output by the heat capacity.

[0046]

number

[0047] The heat capacities C1, C2, C3, C4, and C5 of the components are constants and can be determined in advance. The coefficients ha, hb, hc1, hc2, hc3, and hd related to heat transfer are coefficients obtained by multiplying the heat transfer coefficient by the contact area. The coefficients ha, hb, hc1, hc2, hc3, and hd are constants and can be determined in advance. The loss calculation unit 54 of the temperature estimation unit 53 calculates the primary copper loss P c1 in the coil 16 and the iron loss P i in the stator core as described later. The temperature calculation unit 55 of the temperature estimation unit 53 can calculate the amount of change in temperature in the minute time dt based on the above equations (1) to (5).

[0048] Next, the calculation methods for the primary copper loss P c1 and the iron loss P i included in the equations (1) and (2) will be described. The rotational speed of the motor 10 and the load factor (ratio to the maximum load) of the motor 10 can be preset by the operator according to the work performed by the machine. The loss calculation unit 54 of the temperature estimation unit 53 calculates the primary copper loss P c1 and the iron loss P i . Table 1 shows a loss map for calculating the losses.

[0049]

Table 1

[0050] Table 1 shows the loss at maximum output with respect to the rotational speed (number of revolutions) of the motor 10, the loss at no load, and the current at maximum output. The loss P m at maximum output is the loss when the load factor of the motor is 100% and is a value determined by the rotational speed of the motor. The loss P n at no load is the loss when the load factor of the motor is zero and depends on the rotational speed of the motor. The current I m at maximum output is the current value when the load factor is 100% at each rotational speed. The loss map shown in Table 1 can be created by actually driving the motor. This loss map can be stored, for example, in the storage unit 51 of the monitoring device 2.

[0051] The loss calculation unit 54 calculates the primary copper loss P c1 and iron loss P i Total loss P including t Calculate the total loss P. t This can be calculated using the following equations (6) and (7).

[0052]

number

[0053] Total loss P t The loss P at maximum output is m , loss P under no load n , and the motor load factor LF can be used to calculate the loss P at maximum output. Since the motor rotation speed and load factor are fixed, Table 1 shows that m and loss P under no load n The following is calculated. The constants k1 and k2 can be predetermined by the operator. Next, the primary copper loss P c1 This can be calculated using the following equations (8) and (9).

[0054]

number

[0055] Primary copper loss P c1 This corresponds to the Joule heat generated by the current flowing through coil 16. Furthermore, the current I flowing through coil 16 is the current I at maximum output. m It can be calculated by multiplying by the motor's load factor LF. The current I at maximum output m This can be obtained from Table 1. Here, the primary resistance r1 of coil 16 has been measured in advance. Next, iron loss P i This can be calculated using the following formula (10): Iron loss P i This is the total loss P t From primary copper loss P c1 It can be calculated by subtracting [a certain value].

[0056]

number

[0057] The temperature estimation unit 53 acquires the motor operation pattern, including the rotational speed and load factor for driving the machine 1, from the state acquisition unit 62. The temperature calculation unit 55 of the temperature estimation unit 53 can initially set the temperatures T1 to T5 of each component to any temperature. For example, the temperature calculation unit 55 sets the temperatures T1 to T5 of the components to the normal ambient temperature T r Set to the outside temperature T. r This can be predetermined depending on the location where machine 1 is to be placed.

[0058] The loss calculation unit 54 of the temperature estimation unit 53 calculates primary copper loss and iron loss based on the rotational speed and motor load factor in the operating pattern. Next, the temperature calculation unit 55 can calculate the change in temperature T5 of the temperature detector 31 in a small time interval dt by solving the above equations (1) to (5). In this way, the operator can determine the motor operating pattern and estimate the change in the temperature of the temperature detector over time when the motor is operated according to the operating pattern.

[0059] By the way, in the electric motor model 10a of this embodiment, it is sufficient if the temperature of one of the multiple components of the electric motor can be estimated with accuracy. The temperatures of the other components do not need to be accurate. In this example, it is sufficient if the temperature T5 of the temperature sensor model 31a can be estimated with accuracy. The temperature T1 of the coil model 16a, the temperature T2 of the stator core model 20a, the temperature T3 of the air layer model 35a, and the temperature T4 of the rotor model 11a do not need to be accurate.

[0060] Furthermore, the heat capacities C1 to C5 set in the electric motor model 10a, and the heat transfer coefficients ha, hb, hc1, hc2, hc3, hd set between the components, have unique values ​​depending on the material, shape, and arrangement of the components. However, in the electric motor model 10a in this embodiment, at least some of the parameters among the multiple heat capacities and multiple heat transfer coefficients may be set to values ​​that deviate from the actual heat capacities or actual heat transfer coefficients.

[0061] Each parameter is set so that the change in temperature T5 of the temperature detector model 31a corresponds to the change in the actual temperature. For example, even if the temperatures of the coil and stator core are far from the actual temperature, the parameters of the motor model can be set so that the temperature of the temperature detector shows a value close to the actual temperature. Furthermore, it is acceptable if, as a result of calculating the coefficients related to heat capacity and heat transfer, the heat capacity and heat transfer coefficients of all components correspond accurately to the actual heat capacity and heat transfer coefficients. And, when the temperature estimation unit estimates the temperature of the components, it is acceptable if the temperatures of all components correspond accurately to the actual temperatures of the components.

[0062] The motor monitoring device 2 of this embodiment is configured to switch between a normal model creation mode, which calculates the parameters of a first model of the motor when the motor is operating normally, and an abnormal model creation mode, which generates a second model of the motor when the motor is operating abnormally. In the normal model creation mode, parameters including coefficients related to heat transfer and the heat capacity of the components are set in the first model 10a of the motor.

[0063] Referring to Figure 1, the parameter calculation unit 63 of this embodiment generates a first model of the electric motor in normal model creation mode. The parameter calculation unit 63 sets the heat capacity, heat transfer coefficients, and constants k1 and k2 in equations (6) and (7) included in the model 10a of the electric motor. The operator actually drives the electric motor 10 according to a predetermined operating pattern. The state acquisition unit 62 acquires the load factor of the electric motor 10, the rotational speed of the electric motor 10, and the temperature output from the temperature detector 31 as the state of the electric motor 10. Furthermore, the state acquisition unit 62 acquires the ambient temperature from the ambient temperature detector 33.

[0064] Figure 4 shows a graph of the operating pattern when driving the motor to set the parameters included in the first model of the motor in this embodiment. Figure 4 shows the operating pattern under no load. In this operating pattern, the rotational speed of the motor 10 is gradually increased without applying a load to the motor 10. The rotational speed of the motor 10 is increased by temporarily increasing the load factor of the motor at predetermined time intervals.

[0065] The temperature detected by the temperature sensor 31 is gradually increasing. Between times t1 and t7, the rotational speed of the motor 10 is increased by temporarily increasing the load factor of the motor 10. The state acquisition unit 62 acquires the operating state of the motor 10 and the temperature output from the temperature sensor 31 during the period when the rotational speed of the motor 10 is gradually increasing. More specifically, the state acquisition unit 62 acquires the load factor of the motor 10, the rotational speed of the motor 10, and the temperature output from the temperature sensor 31 at predetermined minute intervals and stores them in the storage unit 51. In this embodiment, a constant ambient temperature is used, but the system is not limited to this configuration. The state acquisition unit 62 may also acquire the ambient temperature from the ambient temperature sensor 33 at minute intervals.

[0066] Referring to Figure 1, the state acquisition unit 62 acquires the torque command included in the operation command generated by the operation control unit 43 of the machine control device 41. Since the torque command corresponds to the load factor of the electric motor 10, the state acquisition unit 62 can calculate the load factor from the torque command.

[0067] The parameter calculation unit 63 calculates the parameters of the electric motor model 10a based on the variables acquired by the state acquisition unit 62. In this embodiment, the parameter calculation unit 63 calculates parameters including heat capacities C1, C2, C3, C4, C5 and heat transfer coefficients ha, hb, hc1, hc2, hc3, hd based on the amount of heat generated in the coil 16 and stator core 20 and the temperature detected by the temperature detector 31. The parameter calculation unit 63 also calculates the constants k1 and k2 in equations (6) and (7) as parameters. The parameter calculation unit 63 calculates the parameters so that the change in the estimated temperature of the temperature detector model 31a during the simulation approaches the change in the actual measured temperature.

[0068] The parameter calculation unit 63 sets the initial values ​​for each parameter. The initial values ​​of the parameters can be set by any method. The parameter calculation unit 63 uses the loss calculation unit 54 to calculate the amount of heat generated due to the primary copper loss of the coil 16 and the amount of heat generated due to the iron loss of the stator core 20. Based on the rotational speed of the motor 10 and the load factor of the motor 10 acquired by the state acquisition unit 62, the loss calculation unit 54 uses Table 1 and equations (6) to (10) to calculate the primary copper loss P c1 and iron loss P i Calculate.

[0069] Primary copper loss P c1 and iron loss P i Equations (6) and (7) used to calculate the primary copper loss P include constants k1 and k2. Furthermore, the loss calculation unit 54 calculates the loss in a predetermined minute time dt, i.e., the amount of heat generated in that minute time. In this way, the loss calculation unit 54 calculates the primary copper loss P in equations (1) and (2) based on measured values ​​including the motor operation command (load factor) and the output of the rotation position detector 32. c1and iron loss P i Calculate.

[0070] The parameter calculation unit 63 uses the temperature calculation unit 55 to estimate the temperature of the components. The temperature calculation unit 55 uses the respective parameters and the losses calculated by the loss calculation unit 54 to calculate the estimated temperature of the temperature sensor 31 based on the first model 10a of the electric motor. In other words, the temperature of the model 31a of the temperature sensor is estimated by simulation.

[0071] The temperature calculation unit 55 can calculate the change in estimated temperature over time detected by the temperature detector 31 after the motor 10 has started to operate, based on the parameters that have been set. The model temperature of each component of the motor 10 can be calculated using the differential equations (1) to (5) above. The initial value of the model temperature of each component can be set, for example, to the ambient temperature when the motor 10 is started to operate, i.e., room temperature.

[0072] The evaluation unit 66 of the parameter calculation unit 63 evaluates the parameters provisionally set in the first model 10a of the electric motor by comparing the temperature (estimated temperature) of the model 31a of the temperature detector calculated by the temperature calculation unit 55 with the measured temperature actually measured by the temperature detector 31. In this example, the evaluation unit 66 evaluates only the temperature of the model 31a of the temperature detector, without evaluating any variables other than the temperature of the model 31a of the temperature detector. It is sufficient that the change in the temperature of the model 31a of the temperature detector is close to the actual change in temperature, and the temperatures of at least some of the other components are not evaluated.

[0073] Next, the parameter modification unit 67 of the parameter calculation unit 63 modifies the parameters based on the evaluation results of the evaluation unit 66. Then, based on the modified parameters, the same calculations as above are repeated: the loss calculation unit 54 calculates the loss, the temperature calculation unit 55 calculates the estimated temperature of the temperature detector model, the evaluation unit 66 performs the evaluation, and the parameter modification unit 67 modifies the parameters. When the evaluation by the evaluation unit satisfies predetermined conditions, the final parameters can be determined.

[0074] Here, the number of possible combinations of parameters in Model 10a of the electric motor is very large. These parameters can be determined using machine learning methods. For example, multiple parameters can be set using Bayesian optimization. In Bayesian optimization, an objective function to be evaluated is generated for explanatory variables that include the input parameters. Then, the parameters that are predicted to result in the minimum or maximum of the objective function are searched for and set. By repeating this parameter search, the optimal values ​​for the parameters can be set. Furthermore, the range in which each parameter can be set can be predetermined.

[0075] In this example, the objective function for the temperature of the temperature detector 31 is set to the difference between the temperature of the model 31a of the temperature detector (estimated temperature) estimated by the first model 10a of the electric motor and the actual measured temperature detected by the temperature detector 31. That is, the objective function can be the difference between the predicted value calculated from equations (1) to (5) based on the provisionally set parameters for the temperature of the temperature detector 31 and the actual measured value detected by the temperature detector 31. For example, the average value of the difference over a small time interval can be adopted as the objective function. The parameter changing unit 67 then searches for the next parameters so that the objective function becomes smaller.

[0076] In Bayesian optimization, the process of searching for and evaluating parameters can be repeated. The evaluation unit 66 can adopt the parameter values ​​at the time if the objective function is within a predetermined range. On the other hand, if the objective function deviates from the predetermined range, the next parameter search can be performed. In the Bayesian optimization method, the amount of computation can be reduced because the search is performed while predicting the region in which a solution exists.

[0077] The parameters included in the electric motor model 10a can be set by any method other than parameter setting by Bayesian optimization. For example, the range in which each parameter can be set can be predetermined. The parameter change unit 67 of the parameter calculation unit 63 sets multiple parameters randomly within the parameter range. The temperature calculation unit 55 estimates the temperature of the temperature detector model 31a based on the set parameters. The evaluation unit 66 can evaluate the set parameters based on the measured temperature values ​​obtained from the temperature detector 31. This method of setting parameters is called the random search method.

[0078] Alternatively, the parameter modification unit 67 can set parameters at predetermined intervals within the range in which the parameters are set. The temperature calculation unit 55 estimates the temperature of the temperature detector model 31a using the set parameters. The evaluation unit 66 evaluates all combinations of discretely set parameters. This method is called the grid search method.

[0079] In both the random search method and the grid search method, similar to the Bayesian optimization method, the evaluation unit 66 can evaluate the temperature of the temperature detector 31. The evaluation unit 66 can adopt the parameter values ​​at the time if the objective function is within a predetermined range. Alternatively, the evaluation unit 66 can adopt the parameters that best perform the objective function. The evaluation unit 66 can determine the parameters in the motor model 10a that best match the estimated temperature of the temperature detector 31 to the actual measured temperature detected by the temperature detector 31.

[0080] In this embodiment, parameters are set so that the temperature change detected by the temperature detector 31 can be estimated with high accuracy. In this embodiment, the temperatures of components other than the temperature detector 31 do not need to be different from the actual temperatures, so in the parameter evaluation, only the temperature of the temperature detector that detects the coil temperature can be evaluated. For this reason, parameters can be set in a short time with a small amount of computation. The storage unit 51 can store the first model 10a of the generated electric motor.

[0081] In the above embodiment, the driving pattern for setting the parameters of the first model 10a of the electric motor was shown as no-load operation, but the embodiment is not limited to this. When determining the parameters of the first model 10a of the electric motor, it is preferable to operate the electric motor 10 under various driving patterns to obtain the operating state of the electric motor 10. For example, an operating pattern that repeatedly increases and decreases the load factor of the electric motor 10 can be adopted. The rotational speed of the electric motor can be changed by greatly changing the load factor of the electric motor 10. The temperature detected by the temperature sensor 31 will rise or fall rapidly. In this way, an operating pattern that includes abrupt temperature changes of the electric motor can be adopted.

[0082] In the above embodiment, a coil including windings was used as an example of a component of the motor for estimating temperature, but the embodiment is not limited to this. Any component of the motor can be used as the component for calculating the estimated temperature. Furthermore, a temperature sensor can be attached to the component for calculating the estimated temperature.

[0083] In the above embodiment, a synchronous motor having a permanent magnet rotor was described, but the embodiment is not limited to this configuration. The monitoring system in this embodiment can be applied to any motor. For example, the motor model in this embodiment can also be applied to an induction motor that does not have a permanent magnet rotor.

[0084] Figure 5 shows a model of the second motor in this embodiment. Figure 5 is the first model 30a when the second motor is operating normally. Here, the second motor is an induction motor. The rotor of the induction motor includes a cage-shaped conductor made of stainless steel or copper. The cage-shaped conductor is fixed to the shaft and rotates integrally with the shaft. In an induction motor, an induced current flows inside the cage-shaped conductor due to the magnetic force generated by the coils of the stator. A magnetic field is generated around the cage-shaped conductor, causing the rotor to rotate.

[0085] In induction motors, current flows through the cage-shaped conductors of the rotor, resulting in secondary copper losses P as secondary losses. c2 This occurs. The secondary loss corresponds to Joule heating due to the current flowing through the cage-type conductor. In the second motor model 30a, heat is generated in the rotor due to secondary copper loss. The heat capacity of the components of the second motor and the coefficients for heat transfer between the components are the same as those for the first motor model 10a.

[0086] The differential equation for the temperature change of the components in Model 30a of the second electric motor differs from that of Model 10a of the first electric motor in that the differential equation for calculating the temperature change of the rotor is given by equation (11) below.

[0087]

number

[0088] In equation (11), the secondary copper loss P is added to equation (4) of model 11a of the rotor of the first motor. c2 The heat generated is added. The differential equations representing the temperature changes of the other coils, stator core, air layer, and temperature detector are the same as those in the thermal model of the first electric motor.

[0089] The loss calculation unit 54 calculates the amount of heat generated due to secondary copper loss in the rotor conductors. The loss calculation unit 54 estimates the current flowing through the cage-type conductor. The loss calculation unit 54 can calculate the secondary copper loss from the current flowing through the conductor, the secondary resistance of the conductor, the inductance of the conductor, and the mutual inductance between the conductor, the stator, and the coil. The inductance of the conductor, the mutual inductance, and the secondary resistance of the conductor can be predetermined.

[0090] Total loss P in an induction motor t and primary copper loss P c1 This can be calculated in the same way as the total loss and primary copper loss in a synchronous motor. And the iron loss P i Secondary copper loss P c2 Taking this into consideration, it can be calculated using the following formula (12).

[0091]

number

[0092] In this way, primary copper loss, iron loss, and secondary copper loss are calculated in the first model 30a of the second motor. The parameter calculation unit 63 can calculate the parameters included in the first model 30a of the second motor when the motor is operating normally, using the same control as the first model 10a of the first motor. The temperature estimation unit 53 can then use the first model 30a of the second motor to calculate the estimated temperature of the components when the motor is operating normally.

[0093] Figure 6 shows a graph of the estimated temperature of the temperature detector, estimated by the temperature estimation unit using the parameters calculated by the parameter calculation unit of this embodiment. Here, an example of a second electric motor is shown. Figure 6 shows graphs when simulations are performed with parameter groups A and B, which have different values ​​from each other. Parameter groups A and B are calculated by the parameter calculation unit 63. The parameters included in parameter groups A and B are shown in Table 2.

[0094] [Table 2]

[0095] Parameter groups A and B were obtained by driving the second electric motor with different operating patterns. Table 2 shows the coefficients related to heat transfer, which are obtained by multiplying the heat transfer coefficient between each component of the electric motor by the contact area. The heat capacity is calculated by multiplying the specific heat of the material of each component by its mass. Since the specific heat of each material can be predetermined, Table 2 shows the mass m of the component used to calculate the heat capacity. Comparing parameter groups A and B, it can be seen that some parameters, such as the heat transfer coefficients hc2, hd and the rotor mass m4, have significantly different values ​​between the two parameter groups A and B.

[0096] On the other hand, referring to Figure 6, it can be seen that the estimated temperature of the temperature detector calculated using parameter group B is in good agreement with the estimated temperature of the temperature detector calculated using parameter group A. In particular, the temperature changes are in good agreement both during the period when the temperature is rising and during the period when the temperature is fluctuating within a predetermined range. Furthermore, the temperature changes shown in Figure 6, estimated by the temperature estimation unit 53, are in good agreement with the temperature changes detected by the temperature detector 31 when the electric motor 10 is actually driven.

[0097] There are parameters whose values ​​differ significantly between parameter group A and parameter group B. Therefore, it can be seen that at least one of the parameter groups, A or B, has different values ​​from the parameter groups in an actual electric motor. In particular, it can be seen that at least some of the parameters, including the heat capacity and heat transfer coefficients, are set to values ​​different from the actual heat capacity or heat transfer coefficients. For example, it can be seen that at least one of the heat transfer coefficients, hc2 in parameter group A and hc2 in parameter group B, deviates from the actual heat transfer coefficient.

[0098] Thus, in the motor monitoring device of this embodiment, the temperature of the temperature sensor can be estimated with high accuracy even if at least some of the multiple parameters differ from the actual values. Furthermore, the parameter calculation unit of this embodiment can set the parameters of such a motor model.

[0099] In this embodiment, one temperature sensor is attached to the motor, but the system is not limited to this configuration. Multiple temperature sensors may be attached to multiple components of the motor. The evaluation unit of the parameter calculation unit can compare the measured temperatures of the multiple temperature sensors with the estimated temperatures calculated by the simulation. The parameter modification unit can set the parameters of the first model of the motor so that the estimated temperatures of the multiple components are close to the measured temperatures detected by the actual temperature sensors.

[0100] The more temperature sensors attached to an electric motor, the closer the values ​​of each parameter in the first model of the motor can be to actual values. Furthermore, the estimated temperature of each component of the motor can be brought closer to the actual measured temperature. As a result of attaching multiple temperature sensors, it is acceptable for all heat capacity and heat transfer coefficients to be approximately identical to the actual heat capacity and heat transfer coefficients. In this case, when the temperature estimation unit estimates the temperature of the components, the temperatures of all components will correspond accurately to the actual temperatures of the components.

[0101] Next, we will describe the abnormal model creation mode, which generates a second model of the motor when its operating state is abnormal. The motor monitoring device 2 of this embodiment creates a second model when the motor's cooling performance changes. Based on the second model of the motor, the monitoring device 2 provides information regarding the motor's cooling performance to the operator or other devices. The monitoring device 2 also determines whether the motor's cooling state has deteriorated.

[0102] In the explanation of the abnormal model creation mode, the first motor 10 shown in Figure 2 and the first model 10a of the first motor shown in Figure 3 are used as examples, but the mode is not limited to these configurations. The second motor and the second model 30a of the second motor shown in Figure 5 can also be controlled in the same way as the first motor. Furthermore, as with the normal model creation mode, the temperature detected by the temperature detector 31 attached to the coil 16 will be used as an example in the explanation.

[0103] Referring to Figure 1, the motor monitoring device 2 of this embodiment includes a model search unit 71 that generates a second model of the motor when the motor's operating state is abnormal. The model search unit 71 includes a temperature determination unit 72 that determines whether the difference between the temperature measured by the temperature detector 31 and the estimated temperature of the temperature detector 31 based on the first model 10a is within a predetermined determination range.

[0104] The model search unit 71 includes a setting unit 73 that creates a second model 10b based on the first model 10a when the difference between the measured temperature of the temperature detector 31 and the estimated temperature of the temperature detector 31 based on the first model 10a deviates from a predetermined judgment range. The setting unit 73 modifies some of the heat transfer coefficients of the first model 10a when the difference between the estimated temperature of the temperature detector and the measured temperature is large, in order to generate a second model 10b in which the estimated temperature accurately corresponds to the measured temperature. In this embodiment, the setting unit 73 generates a second model 10b in which the heat transfer coefficients between the stator core model 20a and the outside air model 36a of the first model 10a are modified so that the estimated temperature of the temperature detector 31 based on the second model 10b corresponds to the measured temperature.

[0105] The monitoring device 2 in this embodiment includes a performance estimation unit 74 that estimates the cooling performance of the electric motor 10. The performance estimation unit 74 calculates a variable CA relating to the cooling performance of the electric motor based on a coefficient hb relating to heat transfer between the stator core model 20a and the outside air model 36a in the first model 10a and a coefficient hbx relating to heat transfer between the stator core model 20a and the outside air model 36a in the second model 10b.

[0106] The monitoring device 2 includes a notification unit 75 that notifies other devices of information regarding the cooling performance of the electric motor. The notification unit 75 notifies other devices of information regarding the second model 10b. In addition, the notification unit 75 in this embodiment notifies other devices of information regarding a decrease in the cooling performance of the electric motor.

[0107] Each of the above-mentioned units—the model search unit 71, the temperature determination unit 72, and the setting unit 73—corresponds to a processor that operates according to a pre-created program. Similarly, each of the performance estimation unit 74 and the notification unit 75 corresponds to a processor that operates according to a pre-created program. The processor functions as each unit by reading the program and executing the control defined in the program.

[0108] If there is an abnormality in the cooling performance of the electric motor, the temperature of the electric motor may rise. For example, the blower 29 may malfunction, the air holes 28a through which air flows may become clogged with foreign matter such as dust, or sludge may accumulate on the surface of the electric motor 10. In this embodiment, if the temperature measured by the temperature detector 31 attached to the electric motor deviates from a predetermined judgment range based on the estimated temperature when the electric motor is operating normally, it is determined that the cooling performance of the electric motor is abnormal. In other words, it is determined that the operating state of the electric motor is abnormal.

[0109] Figure 7 shows a graph of temperature change when the first electric motor is operating normally. Referring to Figures 1, 3, and 7, the vertical axis of the graph represents the temperature of the temperature sensor 31 attached to the coil 16. The horizontal axis represents elapsed time. The solid line shows the measured temperature actually detected by the temperature sensor 31, that is, the measured temperature acquired by the state acquisition unit 62.

[0110] In this operating example, power is supplied at time t0, and the load on the motor 10 increases rapidly. As the current increases, the temperature of the temperature sensor 31 increases rapidly. At time t1, the current supplied to the motor 10 is reduced. The rotational speed of the motor 10 remains almost constant under no load. After time t1, the temperature of the temperature sensor 31 gradually decreases over time.

[0111] Figure 7 shows the estimated temperature of the temperature detector 31, estimated using the first model 10a of the first electric motor, as indicated by a dashed line. The first model 10a of the electric motor is pre-generated in normal model creation mode. When the electric motor is operating normally, it can be seen that the estimated temperature of the temperature detector 31, estimated using the first model 10a of the electric motor, corresponds accurately to the measured temperature detected by the temperature detector 31.

[0112] Figure 8 shows a graph of temperature change when the cooling performance of the first motor deteriorates. The operating conditions in Figure 8 are the same as those in Figure 7. Referring to Figures 1, 3, and 8, the load is increased from time t0 to time t1. At time t1, the load is reduced to zero. In the interval from time t0 to time t2, the cooling performance of the motor is normal. That is, the motor is operating normally. The measured temperature detected by the temperature sensor 31 corresponds accurately to the estimated temperature calculated by the first model 10a of the motor.

[0113] However, after time t2, an abnormality occurs in the cooling state of the motor 10, causing the measured temperature to rise. This example shows a case where the blower 29 malfunctioned and the cooling fan 27 stopped. As a result of the blower 29 malfunction, heat removal from the stator core 20 and housings 21 and 22 becomes insufficient, causing the overall temperature of the motor 10 to rise. The temperature of the temperature sensor 31 attached to the coil 16 also rises.

[0114] The model search unit 71 of the monitoring device 2 detects at time t2 that an abnormality has occurred in the operating state of the electric motor 10. The model search unit 71 sets a section EP in which the abnormality has occurred in the electric motor 10. The model search unit 71 searches for a coefficient hbx obtained by changing the coefficient hb related to heat transfer between the stator core model 20a and the outside air model 36a in the first model 10a so that the estimated temperature in section EP corresponds accurately to the measured temperature. That is, the model search unit 71 generates a second model 10b that includes the coefficient hbx related to heat transfer.

[0115] The performance estimation unit 74 of the monitoring device 2 calculates a variable CA relating to the cooling performance of the electric motor based on the heat transfer coefficient hb in the first model 10a and the heat transfer coefficient hbx in the second model 10b. The monitoring device 2 notifies the operator by displaying information regarding the cooling performance of the electric motor 10 on the display unit 52. Alternatively, the notification unit 75 of the monitoring device 2 transmits information regarding the cooling performance of the electric motor to another device. Next, each process will be described in detail.

[0116] During the period in which the electric motor 10 is operating, the status acquisition unit 62 of the monitoring device 2 acquires the operating status of the electric motor, including the operation command of the electric motor 10 and the rotational speed output from the rotational position detector 32, at predetermined time intervals. The temperature estimation unit 53 calculates the estimated temperature of each component of the electric motor based on the operating status acquired by the status acquisition unit 62 and the first model 10a of the electric motor. In particular, in this embodiment, the temperature estimation unit 53 calculates the estimated temperature of the temperature detector 31 using the first model 10a at predetermined time intervals.

[0117] Furthermore, the state acquisition unit 62 acquires the measured temperature output from the temperature detector 31 at predetermined time intervals. The storage unit 51 stores the time, the estimated temperature of the temperature detector 31 calculated in the first model 10a, and the measured temperature of the temperature detector 31. Such sampling of the operating state can be performed, for example, at intervals of 1 second.

[0118] The temperature determination unit 72 determines whether the difference between the estimated temperature of the temperature detector 31 and the measured temperature of the temperature detector 31 deviates from a predetermined determination range. For example, the temperature determination unit 72 can calculate a moving average of the difference between the measured temperature and the estimated temperature. For example, the temperature determination unit 72 can calculate a moving average over the past minute. When the moving average deviates from a predetermined determination range, it can determine that the difference between the measured temperature and the determination temperature has deviated from the determination range. The temperature determination unit 72 can determine that there is an abnormality in the operating state of the motor at the time in the middle of the moving average interval. In this case, it is determined that there is an abnormality in the motor at time t2 when the measured temperature is substantially higher than the estimated temperature.

[0119] The control by which the temperature determination unit determines the measured temperature is not limited to this form, and it is possible to determine whether the difference between the measured temperature and the estimated temperature exceeds the determination range using any control method. For example, the temperature determination unit determines whether the absolute value of the difference between the measured temperature and the estimated temperature is greater than a predetermined determination value. If this absolute value is greater than the predetermined determination value, it can be determined that the difference between the measured temperature and the actual temperature exceeds the determination range.

[0120] Referring to Figure 8, the temperature determination unit 72 sets a predetermined time interval EP from the time t2 when the anomaly occurred in order to create a second model 10b from the first model 10a and the measured temperature of the temperature detector 31. In this embodiment, interval EP is the interval in which the motor is not sufficiently cooled. In interval EP, the difference between the measured temperature and the estimated temperature exceeds the determination range.

[0121] Figure 9 shows a second model of the electric motor in this embodiment. In the second model 10b, the coefficient hbx for heat transfer between the stator core model 20a and the ambient air model 36a is changed from the coefficient hb for heat transfer between the stator core model 20a and the ambient air model 36a in the first model 10a. The other parameters of the second model 10b are the same as those of the first model 10a.

[0122] Referring to Figures 1, 3, 8, and 9, the model search unit 71 generates a second model 10b of the first electric motor shown in Figure 9. The setting unit 73 of the model search unit 71 sets the heat transfer coefficient hbx in the second model 10b in section EP so that the estimated temperature of the temperature detector model 31a of the second model 10b corresponds to the measured temperature of the temperature detector 31. In this embodiment, only the heat transfer coefficient hbx is changed. The parameters of the other components defined in the first model 10a are used without change. In this embodiment, the range of values ​​for the heat transfer coefficient hbx is predetermined.

[0123] The initial value of the coefficient hbx relating to heat transfer between the stator core model 20a and the ambient air model 36a can be any value. For example, an initial value obtained by subtracting a predetermined relative from the heat transfer coefficient hb used in the first model can be used. The loss calculation unit 54 of the temperature estimation unit 53 obtains the operating state of the motor in section EP from the storage unit 51 and calculates the loss. The temperature calculation unit 55 of the temperature estimation unit 53 uses the second model 10b to calculate the estimated temperature of the temperature detector model 31a in section EP at predetermined time intervals.

[0124] The setting unit 73 can search for the heat transfer coefficient hbx in the same manner as setting the parameters of the first model 10a. In this embodiment, the setting unit 73 calculates the heat transfer coefficient hbx using the evaluation unit 66 and the parameter modification unit 67 of the parameter calculation unit 63. The setting unit 73 can set the heat transfer coefficient hbx using a machine learning method, similar to setting multiple parameters of the first model 10a.

[0125] The evaluation unit 66 obtains the measured temperature of the temperature detector 31 in section EP from the storage unit 51. The evaluation unit 66 calculates the difference between the measured temperature of the temperature detector 31 and the estimated temperature in section EP. The evaluation unit 66 can calculate the difference between the measured temperature of the temperature detector 31 and the estimated temperature based on the second model 10b for each sampling time interval. The storage unit 51 stores the heat transfer coefficient hbx of the second model 10b, and the difference between the measured temperature of the temperature detector 31 and the estimated temperature in section EP.

[0126] The heat transfer coefficient hbx in the second model 10b can be set using Bayesian optimization. The objective function is set to the difference between the temperature of the temperature sensor model 31a (estimated temperature) estimated by the second model 10b of the electric motor and the measured temperature detected by the actual temperature sensor 31. The evaluation unit 66 evaluates the heat transfer coefficient hbx that was provisionally set in the second model 10b of the electric motor. The parameter change unit 67 searches for the next parameter so that the objective function becomes smaller. The final parameter can be set when the evaluation by the evaluation unit 66 satisfies predetermined conditions.

[0127] Alternatively, the parameter changing unit 67 may perform a grid search method in which it changes the coefficient hbx for heat transfer at predetermined intervals within the range of the coefficient hbx for heat transfer between the stator core model 20a and the outside air model 36a.

[0128] The parameter modification unit 67 generates a second model 10b in which the heat transfer coefficient hbx is set to a single value within a predetermined range. The temperature estimation unit 53 calculates the estimated temperature of the temperature detector model 31a for each predetermined time interval in section EP based on the second model 10b. The evaluation unit 66 then calculates the difference between the measured temperature and the estimated temperature of the temperature detector for each predetermined time interval in section EP. The storage unit 51 stores the heat transfer coefficient hbx and the difference between the measured temperature and the actual temperature.

[0129] Next, the parameter change unit 67 changes the heat transfer coefficient hbx at predetermined intervals. Then, the temperature estimation unit 53 and the evaluation unit 66 repeat the same calculation and storage of the calculation results in section EP. The parameter change unit 67 changes the heat transfer coefficient hbx little by little at predetermined intervals. The evaluation unit 66 calculates the difference between the measured temperature and the estimated temperature after changing the heat transfer coefficient hbx. In this way, calculations are performed for all heat transfer coefficients hbx. The storage unit 51 stores the difference between the measured temperature and the actual temperature in section EP for each heat transfer coefficient hbx.

[0130] The setting unit 73 selects a second model from among the multiple calculation results stored in the storage unit 51 that has the smallest difference between the temperature measured by the temperature detector 31 and the estimated temperature. For example, in the interval EP, the second model 10b is adopted that has the smallest mean square difference between the temperature measured by the temperature detector 31 and the estimated temperature acquired at predetermined time intervals. The setting unit 73 obtains parameters including the heat transfer coefficient hbx of this second model.

[0131] In the grid search method described above, calculations are performed by setting coefficients hbx for all heat transfer at predetermined intervals, but the method is not limited to this form. For example, an acceptable range for the difference between the estimated temperature and the measured temperature of the temperature detector can be predetermined. The setting unit 73 may then terminate the search for the second model when the difference between the estimated temperature and the measured temperature of the temperature detector is within the acceptable range. For example, in interval EP, if the mean square of the difference between the estimated temperature and the measured temperature obtained at predetermined time intervals is less than a predetermined judgment value, the setting unit 73 can determine that the difference between the estimated temperature and the measured temperature of the temperature detector is within the acceptable range. The setting unit 73 can then adopt a second model that includes the coefficients for heat transfer at that time.

[0132] Furthermore, the model search unit 71 can generate a second model in which the estimated temperature of the temperature detector accurately corresponds to the measured temperature through arbitrary control. For example, the coefficient for heat transfer between the stator core model and the model of the ambient air can be calculated using the least squares method so that the error between the estimated temperature and the measured temperature is small.

[0133] Next, the performance estimation unit 74 calculates variables related to the cooling performance of the electric motor 10 based on the first model 10a and the second model 10b generated by the model search unit 71. The performance estimation unit 74 obtains the heat transfer coefficient hb in the first model 10a and the heat transfer coefficient hbx in the second model 10b. From the respective heat transfer coefficients hb and hbx, the performance estimation unit 74 calculates the heat transfer coefficient between the stator core model 20a and the ambient air model 36a.

[0134] The performance estimation unit 74 calculates a variable CA related to the cooling performance of the electric motor based on the heat transfer coefficient in the first model 10a and the heat transfer coefficient in the second model 10b. In this embodiment, the variable CA is the ratio obtained by dividing the heat transfer coefficient between the stator core model 20a in the second model 10b and the ambient air model 36a by the heat transfer coefficient between the stator core model 20a in the first model 10a and the ambient air model 36a. In other words, the variable CA related to cooling performance is the ratio of the heat transfer coefficient in the second model 10b to the heat transfer coefficient in the first model 10a. The smaller this ratio is, the lower the cooling performance in the second model 10b can be determined to be. The performance estimation unit 74 determines that the cooling performance of the electric motor has decreased if this ratio of heat transfer coefficients is smaller than a predetermined determination value.

[0135] Referring to Figure 1, the display unit 52 in this embodiment displays information about the second model 10b created by the model search unit 71. For example, the display unit 52 displays information about the second model 10b that best corresponds to the measured temperature based on the estimated temperature of the temperature detector 31. Alternatively, the display unit 52 displays the coefficient hbx, the heat transfer coefficient, or the variable CA related to cooling performance, which relates to heat transfer between the stator core model 20a and the ambient air model 36a. By looking at the information about the second model 10b, the operator can estimate the cooling performance of the motor. For example, the operator can estimate that the smaller the heat transfer coefficient of the second model 10b, the lower the cooling performance. Alternatively, the display unit 52 may display information about the decrease in the motor's cooling performance estimated by the performance estimation unit 74.

[0136] If it is suspected that the cooling performance of an electric motor is deteriorating, the operator can perform maintenance before the motor fails. For example, the operator can clean, repair, or replace the motor. Alternatively, the operator can analyze measures to take before the motor fails. For example, the machine load can be reduced until the motor is replaced, delaying the timing of the motor failure.

[0137] The display unit 52 may also display information indicating that the motor is functioning normally. For example, the display unit may indicate that the motor's cooling performance is sufficient if the variable CA related to cooling performance is equal to or greater than a predetermined judgment value. The display unit can display the judgment result each time the performance estimation unit 74 makes a judgment.

[0138] The notification unit 75 of the monitoring device 2 can notify other devices of information regarding the cooling performance of the electric motor. For example, the notification unit 75 transmits to the machine control device 41 that the cooling performance of the electric motor has deteriorated. The machine control device 41 can perform any control based on the notification from the notification unit 75. For example, the machine control device 41 can change the operating state of machine 1. The machine control device 41 can reduce the speed at which the electric motor drives, or change the rate of increase and decrease of the speed. Alternatively, the machine control device 41 can restrict the operation of the electric motor, such as by reducing the upper limit of the rotational speed of the electric motor.

[0139] The variables used by the performance estimation unit to determine cooling performance can be any variables based on coefficients relating to heat transfer between the stator core and the ambient air. For example, a variable can be adopted that is obtained by subtracting the heat transfer coefficient between the stator core model and the ambient air model in the first model from the heat transfer coefficient between the stator core model and the ambient air model in the second model.

[0140] The first model of the electric motor in the above embodiment consists of a coil model, a stator core model, a temperature sensor model, an air layer model, a rotor model, and an ambient air model, but is not limited to this form. The first model of the electric motor may also include models of other components. For example, the first model of the electric motor may include a housing model supporting the stator and rotor, a bearing model, and a shaft model supporting the rotor, etc. Alternatively, the first model of the electric motor may not include some models. For example, the model of the electric motor may not include a model of the air layer.

[0141] The motor monitoring device of this embodiment is configured to allow switching between a normal model creation mode and an abnormal model creation mode, but it is not limited to this configuration. The motor monitoring device does not need to have a function for the normal model creation mode. The first model of the motor when the motor is operating normally may be generated by another device.

[0142] Furthermore, in this embodiment, the motor monitoring device is configured with a separate processing unit from the machine control device, but this is not the only possible configuration. The machine control device may also have the function of a motor monitoring device. That is, the processor of the machine control device may function as a unit of the monitoring device, such as a model search unit, a temperature estimation unit, and a parameter calculation unit.

[0143] In each of the above-described controls, the order of the steps can be changed as appropriate, as long as the function and operation remain unchanged. The above embodiments can be combined as appropriate. In each of the above-described figures, the same or equivalent parts are denoted by the same reference numerals. The above embodiments are illustrative and do not limit the invention. Furthermore, the embodiments include modifications of the embodiments shown in the claims. [Explanation of symbols]

[0144] 2 Monitoring device 10 Electric motor Models of 10a and 10b electric motors 11 rotors 11a Rotor Model 16 coils Model of 16a coil 20 stator cores Model of 20a stator core 29 Blower 30A electric motor model 31a Model of a temperature detector 35a Air layer model 36a Outdoor air model 51 Storage section 52 Display section 53 Temperature estimation section 54 Loss calculation section 55 Temperature calculation section 62 Status acquisition unit 63 Parameter Calculation Unit 66 Evaluation Department 67 Parameter Change Section 71 Model Search Unit 72 Temperature judgment section 73 Settings Section 74 Performance estimation part 75 Notification Department hb, hbx are coefficients related to heat transfer. CA Variables

Claims

1. A state acquisition unit acquires the operating status of the motor, including the measured temperature detected by a temperature sensor attached to the motor, A temperature estimation unit that calculates the estimated temperature of the temperature detector based on the model of the electric motor, A memory unit that stores a first model of the electric motor when the electric motor is operating normally, A model search unit that generates a second model of the electric motor when the electric motor's operating state is abnormal, It includes a performance estimation unit that estimates the cooling performance of the electric motor, The electric motor model includes models of multiple components of the electric motor, including the stator core, and a model of the ambient air. A heat capacity is set for at least one component model. A coefficient for heat transfer is set between the models of the components of the electric motor and between the stator core model and the ambient air model. If the difference between the temperature measured by the temperature detector and the estimated temperature of the temperature detector based on the first model exceeds a predetermined range, the model search unit generates a second model by changing the coefficients relating to heat transfer between the stator core model and the ambient air model of the first model so that the estimated temperature of the temperature detector based on the second model corresponds to the measured temperature. The performance estimation unit calculates variables related to the cooling performance of the electric motor based on the coefficient for heat transfer between the stator core model and the ambient air model in the first model, and the coefficient for heat transfer between the stator core model and the ambient air model in the second model. The aforementioned model search unit generates a second model without changing any parameters other than the coefficients relating to heat transfer between the stator core model and the ambient air model in the first model, and is a monitoring device for an electric motor.

2. The variable relating to the cooling performance of the electric motor is the ratio obtained by dividing the heat transfer coefficient between the stator core model and the ambient air model in the second model by the heat transfer coefficient between the stator core model and the ambient air model in the first model. The motor monitoring device according to claim 1, wherein the performance estimation unit determines that the cooling performance of the motor has deteriorated when the ratio is smaller than a predetermined determination value.

3. A motor monitoring device according to claim 1 or 2, further comprising a notification unit for notifying other devices of information regarding the cooling performance of the motor.

4. A motor monitoring device according to claim 1 or 2, comprising a display unit for displaying information regarding the cooling performance of the motor.

5. It includes a parameter calculation unit that calculates the parameters of a first model of an electric motor, The state acquisition unit acquires the motor operation command generated by actually driving the motor, The aforementioned parameters include the heat capacity set for the model of the component and a coefficient relating to heat transfer between the models of the component, The temperature estimation unit includes a loss calculation unit that calculates the amount of heat generated due to primary copper loss in the coil and the amount of heat generated due to iron loss in the stator core based on the operation command, and a temperature calculation unit that calculates the estimated temperature of the temperature detector using a first model of the electric motor based on the amount of heat generated by the coil and the amount of heat generated by the stator core. The parameter calculation unit includes an evaluation unit that evaluates the estimated temperature of the temperature detector by comparing the estimated temperature of the temperature detector with the measured temperature of the temperature detector, and a parameter modification unit that changes the value of the parameter so that the estimated temperature of the temperature detector approaches the measured temperature based on the evaluation result of the evaluation unit. The motor monitoring device according to claim 1 or 2, wherein the device is configured to switch between a mode in which the parameter calculation unit calculates the parameters of a first model of the motor and a mode in which the model search unit generates a second model of the motor.

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