MONITORING DEVICE FOR AN ELECTRIC MOTOR

Abstract

In the present invention, a monitoring device comprises a storage unit that stores a first model of an electric motor when the 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. If a difference between a measured temperature of a temperature detector and an estimated temperature of the temperature detector based on the first model deviates from a defined range, the model search unit generates the second model, in which a coefficient relating to the heat transfer between a model of a stator core and a model of the ambient air of the first model is used, 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] The present invention relates to a monitoring device for an electric motor. State of the art

[0002] It is known that electric motors are arranged in a machine to drive individual components. When the electric motor is driven, a stator core, a coil attached to the stator core, a bearing, and the like generate heat, and the temperature of the electric motor rises. If the temperature of the electric motor becomes too high, it may no longer operate accurately, its components may be damaged, or its service life may be shortened. For this purpose, the electric motor is preferably cooled during operation. Cooling of the electric motor can be achieved by natural airflow. Alternatively, the electric motor can be cooled by supplying air with a cooling machine or similar device (e.g., Japanese Patent Publication No. JP 2015-167436A).

[0003] The actual operating temperature of the electric motor can be determined by a temperature detector attached to a component of the electric motor. Alternatively, a simulation for estimating the temperature of an electric motor is known (e.g., Japanese unpublished patent application no. JP 11-262102A). In particular, a method for estimating the temperature of the electric motor is known in which a thermal model is used, taking into account the heat capacity of the components of the electric motor and the heat transfer between the components (e.g., International Publication no. WO 2022 / 085543 A). In the thermal model, a coefficient for heat transfer or a thermal resistance between the respective components can be set to calculate the heat transfer between the components. The temperature of each component can be calculated in the thermal model. Citation listPatent literature PTL 1: Unexamined Japanese patent publication no. JP 2015-167436A PTL 2: Uncontested Japanese patent publication no. JP 11-262102A PTL 3: International Publication No. WO 2022 / 085543A PTL 4: Unverified Japanese patent publication no. JP 2010-287971A PTL 5: Japanese uncontested patent publication no. JP 2006-311735A Summary of the inventionTechnical problem

[0004] During operation, an electric motor is cooled by a fan. However, the cooling performance of this motor can deteriorate over time. Examples include a broken fan, a blocked air vent (such as dust), or a buildup of sludge on the motor's surface. If the motor continues to operate with reduced cooling capacity, its temperature can rise.

[0005] To detect a blower failure, a temperature detector can be attached to the blower, or a magnetic sensor can be attached to measure the blower's rotational speed. To detect a blockage of the blower's air inlet, a flow sensor can be attached to measure the volume of air flowing through the blower.

[0006] To detect blower failure, a blocked blower air inlet, mud buildup, etc., the operator can regularly perform a visual inspection of the electric motor. If any abnormalities are found, the blower can be repaired or replaced. If foreign objects become lodged in the air inlet or mud accumulates, the electric motor can be cleaned.

[0007] However, if a sensor such as a magnetic sensor, temperature sensor, or flow sensor is used to detect the fan speed, the problem arises that a new sensor must be installed on the electric motor to detect any deterioration in its cooling performance. Alternatively, the method where the operator visually inspects the electric motor regularly requires disassembling the unit, including the electric motor, which is time-consuming and labor-intensive for the operator. Solution to the problem

[0008] A monitoring device for an electric motor according to one aspect of the present disclosure comprises a state-sensing unit configured to detect the 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 configured to calculate an estimated temperature of the temperature detector based on the model for the electric motor. The monitoring device includes a memory configured to store a first model for the electric motor when the operating state of the electric motor is normal. The monitoring device includes a model search unit configured to generate a second model for the electric motor when the operating state of the electric motor is abnormal.The monitoring device includes a power estimation unit configured to estimate the cooling capacity of the electric motor. The electric motor model includes models for its components, including the stator core, and a model for the ambient air. A heat capacity is defined in at least one of the component models. A coefficient for heat transfer is defined between the component models and between the stator core model and the ambient air model.The model search unit generates a second model in which a coefficient related to the heat transfer between the stator core model and the ambient air model of the first model is modified such that an estimated temperature of the temperature detector based on the second model corresponds to the measured temperature when a 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 range.The power estimation unit calculates a variable related to the cooling power of the electric motor, based on the coefficient related to the heat transfer between the stator core model and the ambient air model in the first model, and the coefficient related to the heat transfer between the stator core model and the ambient air model in the second model. Advantageous effects of the invention

[0009] According to a monitoring device for an electric motor as described in one aspect of the present disclosure, information relating to the cooling performance of the electric motor can be provided. Brief description of the drawings [ Fig. 1] Fig. Figure 1 is a block diagram of a machine and a monitoring device for an electric motor according to one embodiment. [ Fig. 2] Fig.Figure 2 is a schematic cross-sectional view of a first electric motor according to one embodiment. [ Fig. 3] Fig. 3 is a first model for a first electric motor according to the embodiment. [ Fig. 4] Fig. Figure 4 is a diagram illustrating the running pattern of an electric motor for setting a parameter in the first model for the electric motor. [ Fig. 5] Fig. 5 is a first model for a second electric motor according to the embodiment. [ Fig. 6] Fig. Figure 6 is a graphical representation of a simulation result using parameters defined by a parameter calculation unit. [ Fig. 7] Fig. Figure 7 is a diagram showing the temperature change of the temperature detector during normal operating conditions of the electric motor. [ Fig. 8] Fig.Figure 8 is a diagram showing a temperature change of the temperature detector during an abnormal operating condition of the electric motor. [ Fig. 9] Fig. 9 is a second model for a first electric motor according to the embodiment. Description of the embodiments

[0010] A monitoring device for an electric motor according to one embodiment is described with reference to the Fig. 1 to Fig. 9 described. The monitoring device for an electric motor according to the present embodiment has the function of estimating a temperature of a predetermined component of an electric motor using a thermal model for the electric motor.

[0011] In the present embodiment, the temperature actually measured by the temperature detector is referred to as the measured temperature. Furthermore, the temperature of the component estimated using the model for the electric motor is referred to as the estimated temperature. The present embodiment describes an example of temperature estimation using a temperature detector attached to a coil of a stator among the components of an electric motor. The monitoring device for an electric motor according to the present embodiment calculates a variable relating to the cooling power required to cool the electric motor. The monitoring device then displays information about the electric motor's cooling power on the display unit or notifies another device of this information.

[0012] Fig.Figure 1 is a block diagram of a machine and a monitoring device for an electric motor according to the present embodiment. A machine 1 of the present embodiment comprises an electric motor 10 for driving a component of the machine 1 and a machine controller 41 for controlling the machine 1. The machine controller 41 of the present embodiment comprises an arithmetic processing device (computer). The machine controller 41 comprises a central processing unit (CPU) that serves as a processor. The machine controller 41 comprises random access memory (RAM), read-only memory (ROM), or the like, which is connected to the CPU via a bus.

[0013] The machine 1 according to the present embodiment is a numerically controlled machine. The machine 1 is driven based on a set of instructions described in a pre-defined operating program 45. The machine control 41 comprises a memory 42 for storing the operating program 45 and an operating control unit 43 for generating an operating command for the electric motor 10 based on the operating program 45. The machine 1 includes a drive device 44 with a circuit that supplies current to the electric motor 10 based on the operating command generated in the operating control unit 43. The electric motor 10 is driven by the drive device 44, which supplies current.

[0014] Memory 42 can be a non-volatile storage medium capable of storing information. Memory 42 can, for example, contain a storage medium such as volatile memory, non-volatile memory, magnetic storage medium, or optical storage medium. The operating control unit 43 corresponds to a processor that is controlled according to the operating program 45. The processor reads the operating program 45 and executes the controls specified in the operating program 45 to function as the operating control unit 43.

[0015] Such a machine 1 can be any machine that contains an electric motor 10. An example of a machine 1 is a machine tool for machining a workpiece. Examples of the electric motor 10 are a main spindle motor that rotates a tool and a feed shaft motor for moving a table or a main spindle head along a predefined coordinate axis.

[0016] Fig. Figure 2 is a cross-sectional view of a first electric motor according to the present embodiment. Referring to the Fig. 1 and Fig. 2 is a first electric motor 10, a synchronous electric motor in which a rotor 11 contains a magnet 18. The electric motor 10 comprises the rotor 11 and a stator 12. The stator 12 comprises a stator core 20, which is made of a magnetic material, and a coil 16, which is attached to the stator core 20. The stator core 20 consists, for example, of a plurality of magnetic steel plates stacked in the axial direction of the shaft 13. The coil 16 consists, for example, of a winding wound around the stator core 20 and a resin component for securing the winding.

[0017] The rotor 11 is attached to the shaft 13 and has the shape of a rod. The rotor 11 comprises a rotor core 17, which is attached to the outer circumferential surface of the shaft 13 and is made of a magnetic material, as well as a plurality of magnets 18 attached to the rotor core 17. The magnet 18 according to the present embodiment is a permanent magnet.

[0018] The shaft 13 is coupled to another element to transmit a rotational force. The shaft 13 rotates about an axis of rotation RA. The shaft 13 is supported by bearings 14 and 15, which serve as bearings. According to the present embodiment, in the electric motor 10, the side where the shaft 13 is connected to another element is referred to as the front. The side opposite the front is referred to as the back. In the Fig. In the example shown in Figure 2, an arrow 91 indicates the front of the electric motor 10.

[0019] The electric motor 10 comprises a housing 21 on the front and a housing 22 on the rear. The stator core 20 of the stator 12 is supported by the housings 21 and 22. The housing 21 carries a bearing 14. A bearing support element 24, which carries a bearing 15, is attached to the housing 22. The housings 21 and 22 rotatably support the shaft 13 via the bearings 14 and 15. A rear cover 23 is attached to the end section on the rear of the housing 22 to close the space inside the housing 22.

[0020] A rotational position detector 32 is arranged at the end section on the rear side of the shaft 13 to detect the rotational position or rotational speed of the shaft 13. According to the present embodiment, the rotational position detector 32 includes an encoder. The temperature detector 31, which detects the temperature of the coil 16, is attached to the coil 16 of the stator 12. According to the present embodiment, the temperature detector 31 is formed by a thermistor. The outputs of the temperature detector 31 and the rotational position detector 32 are fed into the machine control 41.

[0021] The components of the electric motor 10 include, for example, the rotor 11, the stator core 17, the magnet 18, the stator 12, the stator core 20, the coil 16, the housings 21, 22, the shaft 13, the rear cover 23, the bearing support element 24, the bearings 14, 15, a temperature detector 31, and the rotation position detector 32. The components of the electric motor 10 are not limited to this form, and any part that forms the electric motor 10 can be used. An example is a housing that covers a stator as a component of the electric motor 10.

[0022] The electric motor 10 according to the present embodiment comprises a cooling unit that cools a main body of the electric motor. The cooling unit of the present embodiment includes a fan 29 that supplies cooling air. The fan 29 of the present embodiment is attached to the stator core 20 via a tubular element 25. The tubular element 25 is attached to the stator core 20. The interior of the tubular element 25 forms an airflow path.

[0023] The blower 29 comprises a cooling fan 27, a housing 28, and an electric motor for controlling the cooling fan 27. In the present embodiment, the blower 29 is arranged such that its axis of rotation coincides with the axis of rotation RA of the shaft 13. In other words, 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 desired location to blow air against the main body of the electric motor.

[0024] The stator core 20 of the present embodiment has a through-hole 26a through which cooling air circulates. 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 air circulation. The through-hole 26b is connected to the through-hole 26a. An air opening 28a is formed in the housing 28 of the blower 29 through which air circulates.

[0025] When the blower 29 is driven, the cooling fan 27 rotates and the air flows in the direction indicated by arrow 91. The cooling air flows through the air opening 28a of the housing 28 into the housing 28. The cooling air circulates through the interior of the housing 28 and the interior of the tubular element 25. The cooling air circulates through the airflow path between the housing 22 and the tubular element 25. As indicated by arrow 92, the cooling air circulates through the through-opening 26a of the stator core 20 and the through-opening 26b of the housing 21. 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 restricted to this direction but can also flow in the opposite direction to arrow 91.

[0026] The cooling machine for cooling the main body of the electric motor according to the present embodiment includes a fan, but is not limited to this mode. The cooling machine can use any device for cooling the main body of the electric motor. An example is a cooling machine for cooling the electric motor with cooling water or an electronic cooling machine with a Peltier element or similar device.

[0027] A monitoring device for an electric motor 2 according to the present embodiment calculates an estimated temperature obtained by estimating a temperature output from the temperature detector 31 arranged in the electric motor 10. In the present embodiment, the temperature output of the temperature detector 31, which is arranged on the coil 16 of the stator 12, is estimated. In particular, the monitoring device for an electric motor 2 estimates a change in the temperature of the temperature detector 31 as a function of time.

[0028] The monitoring device for an electric motor 2 includes a computing unit (computer) with a CPU as its processor. The monitoring device 2 includes a memory 51 for storing information about the monitoring of the electric motor 10. The memory 51 can comprise a non-transferable storage medium capable of storing information. For example, the memory 51 can comprise 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 for displaying information about the electric motor 10. The display unit 52 can consist of any display field, such as a liquid crystal display field or an organic electroluminescent (EL) display field.

[0029] The monitoring device 2 includes a temperature calculation unit 53 for calculating an estimated temperature of the temperature detector 31. The temperature estimation unit 53 comprises a loss calculation unit 54 for calculating 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 an operating command of the electric motor 10. The temperature estimation unit 53 includes a temperature calculation unit 55 for estimating 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 respective models for the components, and the coefficient of heat transfer between the models for the components.

[0030] The monitoring device 2 according to the present embodiment has the function of calculating a parameter contained in a model for an electric motor. The parameter comprises a heat capacity specified in the models for the components of the electric motor 10 and a coefficient relating to the heat transfer between the models for the components. The monitoring device 2 includes a parameter calculation unit 63 for calculating a parameter contained in the first model for the electric motor when the operating condition of the electric motor is normal.

[0031] The monitoring device 2 includes a state detection unit 62 for detecting the operating state of the electric motor 10 when the electric motor 10 is actually being operated. The operating state of the electric motor 10 includes a measured temperature, which is detected by the temperature detector 31 attached to the electric motor 10. The operating state of the electric motor 10 includes an operating command of the electric motor 10, which is generated by the actual driving of the electric motor 10, and a rotational speed output by the rotational position detector 32. The operating command of the electric motor 10 can be detected by the operating control unit 43. In addition, the state detection unit 62 can detect the ambient air temperature from a temperature detector 33, which detects the temperature of the environment in which the machine 1 is located.The ambient air temperature detector 33, for example, is arranged so that it detects the temperature around the machine 1.

[0032] The parameter calculation unit 63 of the present embodiment calculates the parameters such that the change in the estimated temperature of the temperature detector, calculated by the electric motor model, corresponds to the change in the actually measured temperature. The parameter calculation unit 63 of the present embodiment can adjust the parameters of the electric motor model by machine learning.

[0033] The loss calculation unit 54 of the temperature estimation unit 53 calculates the heat generation quantity of the coil 16 and the stator core 20 based on the operating command generated by the operating control unit 43 and the rotational speed detected by the rotary position detector 32. Furthermore, the temperature calculation unit 55 calculates the estimated temperature of the temperature detector based on the heat generation quantity of the coil 16 and the stator core 20.

[0034] 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 for evaluating 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 as recorded by the state detection unit 62. The parameter calculation unit 63 includes a parameter modification unit 67 for changing the parameter values ​​based on the evaluation results of the evaluation unit 66.

[0035] The temperature estimation unit 53, the loss calculation unit 54, and the temperature calculation unit 55 each correspond to a program-controlled processor. Each unit of the state detection unit 62, the parameter calculation unit 63, the evaluation unit 66, and the parameter change unit 67 corresponds to a processor controlled by a program. The processor executes the control defined in the program and operates as a separate unit.

[0036] Fig.Figure 1 shows a first model obtained by modeling the thermal motion of the first electric motor according to the present embodiment. The first model for the electric motor is a thermal model that simulates a normal operating condition of the electric motor. The thermal model for the electric motor includes models for a variety of components. The thermal model contains parameters such as the heat capacity of the components and coefficients for heat transfer between the components. The first model 10a for the electric motor of the present embodiment includes models for the main components that constitute the first electric motor 10. The model 10a for the electric motor includes a model 11a for the rotor, a model 20a for the stator core, and a model 16a for the coil wound around the stator core. The model 10a of the electric motor also includes a model 31a for the temperature detector for detecting the temperature of the coil 16.

[0037] As in Fig. As shown in Figure 2, an air layer is located between the rotor 11 and the stator core 20. An air layer is also arranged between the rotor 11 and the coil 16. The model 10a for the electric motor according to the present embodiment includes a model 35a for the air layer. The model 10a for the electric motor also includes a model 36a for the ambient air as a model for the air around the electric motor 10. Thus, in the model for the electric motor according to the present embodiment, the air layer and the ambient air are generated as models for the components of the electric motor.

[0038] The temperature detected by the temperature detector 31 is almost identical to the temperature of the coil 16. However, under certain conditions, the temperature detected by the temperature detector 31 may deviate from the temperature of the coil 16 due to the low heat capacity of the temperature detector 31. Therefore, in the present embodiment, model 31a for the temperature detector 31 is also generated as a model for the constituent part. The heat capacity of the temperature detector 31 can be set to zero, and the calculation can be performed assuming that the temperature of the model for the temperature detector is the same as the temperature of the model for the component to which the temperature detector is attached.

[0039] In model 10a for the electric motor, a variety of parameters are defined, including the heat capacity and the coefficient of heat transfer. The heat capacity is specified for at least one component in the model. In model 16a for the coil, model 20a for the stator core, model 35a for the air layer, model 11a for the rotor, and model 31a for the temperature detector, the temperatures T1, T2, T3, T4, and T5 are set as variables, and the heat capacities C1, C2, C3, C4, and C5 are set as constants. Additionally, in model 36a, a temperature T is specified for the ambient air. r set as a variable.

[0040] Heat from one component of the electric motor 10 is transferred to the other components. The coefficient of heat transfer is defined between the models for the respective components of the electric motor 10. The coefficient of heat transfer can be the coefficient of heat transfer itself, the coefficient resulting from multiplying the coefficient of heat transfer by the contact area between the components, or something similar. In the present example, a coefficient is defined by multiplying the coefficient of heat transfer by a contact area.

[0041] A coefficient ha for heat transfer is set between model 20a for the stator core and model 16a for the coil. A coefficient hc1 for heat transfer is set between model 35a for the air layer and model 16a for the coil. A coefficient hc2 for heat transfer is set between model 35a for the air layer and model 20a for the stator core. A coefficient hc3 for heat transfer is set between model 35a for the air layer and model 11a for the rotor. A coefficient hd for heat transfer is set between model 16a for the coil and model 31a for the temperature detector. To simulate the heat dissipation of the stator core 20 to the ambient air, a coefficient hb for heat transfer is also set between model 20a for the stator core and model 36a for the ambient air.

[0042] In model 10a for the electric motor according to the present embodiment, a primary copper loss P c1 The heat generated in the coil 16 of the stator 12 is considered as heat generated by the constituent part. The amount of heat generated due to the primary copper loss is entered into model 16a for the coil. Additionally, an iron loss P is taken into account. i The heat generated by the magnetic force of the magnet 18 in the rotor 11 is taken into account in the model 20a for the stator core. The amount of heat generated due to iron loss is entered into the model 20a for the stator core.

[0043] Heat moves between the individual components, such as the coil and the stator core, depending on the coefficient of heat transfer. The temperature of each component rises and falls depending on the difference between the amount of heat supplied and the amount of heat removed. The temperature change rate of each component of the first model 10a of the in Fig. The temperature of the first electric motor shown in Figure 3 can be expressed by equations (1) to (5) below. The rate of temperature change in each component can be calculated by dividing the difference between the amount of heat input and the amount of heat output by the heat capacity. [Math. 1] dT1dt=Pc1+ha(T2−T1)+hc1(T3−T1)C1 dT2dt=Pi+ha(T1−T2)+hb(Tr−T2)+hc2(T3−T2)C2 dT3dt=hc1(T1−T3)+hc2(T2−T3)+hc3(T4−T3)C3 dT4dt=hc3(T3−T4)C4 dT5dt=hd(T1−T5)C5 T1: Coil temperature C1: Heat capacity of the coil T2: Stator core temperature C2: Heat capacity of the stator core T3: Temperature of the air layer C3: Heat capacity of the air layer T4: Rotor temperature C4: Heat capacity of the rotor T 5 : Temperature of the temperature detector C5: Heat capacity of the temperature detector T r : Ambient air temperature ha, hb, hc1, hc2, hc3, hd: coefficients for heat transfer P c1 primary copper loss P i Iron loss

[0044] The heat capacities C1, C2, C3, C4, and C5 of the respective components are constant and can be predetermined. Each of the coefficients relevant for heat transfer, ha, hb, hc1, hc2, hc3, and hd, is the coefficient obtained by multiplying the heat transfer coefficient by the contact area. The coefficients ha, hb, hc1, hc2, hc3, and hd are constant and can be predetermined. The loss calculation unit 54 of the temperature estimation unit 53 calculates the primary copper loss P. c1 in coil 16 and the iron loss P i in the stator core as described below. The temperature calculation unit 55 of the temperature estimation unit 53 can calculate the amount of temperature change in one minute dt based on equations (1) to (5) described above.

[0045] The following describes the calculation method for the primary copper loss P. c1 and the iron loss Pi described in equations (1) and (2). The speed and load factor of electric motor 10 (percentage relative to the maximum load) can be preset by the operator depending on 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 diagram for loss calculation. [Table 1] Table 1 Loss Overview Rotational speed N [rpm] Loss P m at maximum power [kW] Loss P n at idle [kW] Electricity I m at maximum power [A] 0 3.12 0.001 166.8 1000 4.01 0.0239 166.8 2000 2.57 0.064 166.4 3000 2.7 0.065 165.7 4000 2.73 0.086 165.3 5000 2.72 0.179 165.4 6000 4.49 0.2 165.4 7000 4.03 0.227 158 8000 3.066 0.27 150.3 9000 4.38 0.256 145.8 10000 3.55 0.282 142 11000 5.68 0.291 137 12000 3.5 0.348 135

[0046] Table 1 shows the loss at maximum power, the loss at idle, and the current at maximum power as a function of the speed (revolutions per minute) of the electric motor 10. A loss P m At maximum power, the loss, when the load factor of the electric motor is 100%, is a value determined by the speed of the electric motor. The loss Pn At idle, the loss when the load factor of the electric motor is zero is a value that depends on the speed of the electric motor. A current I m The current value at maximum power is given when the load factor is 100% at every speed. The loss diagram shown in Table 1 can be generated through the actual operation of an electric motor. This loss characteristic map can be stored, for example, in memory 51 of the monitoring device 2.

[0047] The loss calculation unit 54 calculates the total loss Pt including the primary copper loss P c1 and iron loss P i The total loss Pt can be calculated using equations (6) and (7) below. [Math. 2] Pt=k2⋅LF2+k1⋅LF+Pn k1=Pm−Pn−k2 P t Total loss P m : Loss at maximum power P n : Loss at no load LF: Load factor of the electric motor k1, k2: constant

[0048] The total loss P t can be determined by the loss P m at maximum power, the loss P n Calculate the idle speed and load factor LF of the electric motor. Since the speed and load factor of the electric motor are defined, the loss P can be determined. m at maximum power and the loss P n The values ​​for idle power loss are calculated from Table 1. The constants k1 and k2 can be predefined by the operator. The primary copper loss P c1 can then be calculated using equations (8) and (9). [Math. 3] Pc1=r1⋅I2 I=Im⋅LF P c1 Primary copper loss I: Electricity rl: Primary resistance I m Current at maximum power

[0049] The primary copper loss P c1This corresponds to the Joule heating of the current flowing through coil 16. Furthermore, the current I flowing through coil 16 can be calculated by multiplying the current I m The current I is calculated at maximum power using the load factor LF of the electric motor. m The maximum power output can be found in Table 1. In this context, the primary resistance r1 of coil 16 is measured beforehand. Then the iron loss P can be determined. i can be calculated using equation (10) below. The iron loss P i can be calculated by subtracting the primary copper loss P c1 of the total loss P t will be calculated. [Math. 4] Pi=Pt−Pc1 P i Iron loss

[0050] The temperature estimation unit 53 receives the operating pattern of the electric motor, including the speed and load factor for driving machine 1, from the state detection unit 62. The temperature calculation unit 55 of the temperature estimation unit 53 can initially set the temperatures T1 to T5 of the respective components to any desired temperatures. For example, the temperature calculation unit 55 sets the temperatures T1 to T5 of the components to the normal ambient air temperature T. r The temperature T r The ambient air can be determined in advance depending on the installation location of machine 1.

[0051] The loss calculation unit 54 of the temperature estimation unit 53 calculates the primary copper loss and the iron loss based on the rotational speed and load factor of the electric motor in its operating pattern. Next, the temperature calculation unit 55 can calculate the change in temperature T5 of the temperature detector 31 for a time interval dt by solving equations (1) to (5) above. Thus, the operator can determine the operating pattern of the electric motor and estimate the change in temperature of the temperature detector over time while the electric motor is operating in that pattern.

[0052] In model 10a for the electric motor according to the present embodiment, only the temperature of one component among a plurality of components of the electric motor needs to be accurately estimated. The temperatures of the other components may deviate from the actual temperatures. In this example, only the temperature T5 of model 31a for the temperature detector needs to be accurately estimated. The temperature T1 of coil model 16a, the temperature T2 of stator core model 20a, the temperature T3 of air layer model 35a, and the temperature T4 of rotor model 11a may deviate from the actual temperatures.

[0053] Furthermore, the heat capacities C1 to C5 set in Model 10a for the electric motor, and the coefficients ha, hb, hc1, hc2, hc3, and hd set for heat transfer between the components, have unique values ​​that depend on the material, shape, arrangement, and the like of the components. However, in Model 10a for the electric motor according to the present embodiment, at least some of the parameters of the plurality of heat capacities and the plurality of heat transfer coefficients are set to values ​​that deviate from the actual heat capacities or the actual heat transfer coefficients.

[0054] Each parameter is set so that the temperature change T5 of the model 31a temperature detector corresponds to the actual temperature change. For example, even if the temperatures of the coil, stator core, and the like deviate significantly from the actual temperatures, the parameters of the electric motor model are set so that the temperature detector displays a value close to the actual temperature. It should be noted that, as a result of the calculation of the heat capacity and the coefficient of heat transfer, all heat capacities of the components and all coefficients of heat transfer can correspond exactly to the actual heat capacity and coefficient of heat transfer.If the temperature estimation unit estimates the temperatures of the components, the temperatures of all components can correspond exactly to the actual temperatures of the components.

[0055] The monitoring device for an electric motor 2 according to the present embodiment is configured to switch between a normal model generation mode for calculating a parameter of a first model of the electric motor when the operating state of the electric motor is normal, and an abnormal model generation mode for generating a second model of the electric motor when the operating state of the electric motor is abnormal. In the normal model generation mode, parameters including a coefficient relating to heat transfer and a heat capacity of a component are set in the first model 10a for the electric motor.

[0056] Referring to Fig. In the present embodiment, the parameter calculation unit 63 generates a first model for an electric motor in a normal modeling mode. The parameter calculation unit 63 defines the heat capacity, the coefficient of heat transfer, and the constants k1 and k2 in equations (6) and (7), which are included in the model 10a for the electric motor. The operator actually drives the electric motor 10 according to a predetermined operating pattern. The state detection unit 62 detects the load factor of the electric motor 10, the rotational speed of the electric motor 10, and the temperature output by the temperature detector 31 as a state of the electric motor 10. In addition, the state detection unit 62 detects the ambient air temperature from the temperature detector 33 for the outside air.

[0057] Fig.shows a diagram of a running pattern when the electric motor is driven, in order to determine the parameters of the first model for the electric motor according to the present embodiment. Fig. This shows the operating pattern at no load. In this operating pattern, the speed of electric motor 10 is gradually increased without loading the electric motor 10. The speed of electric motor 10 is increased by temporarily increasing the load factor of the electric motor at predetermined time intervals.

[0058] The temperature detected by the temperature detector 31 gradually increases. At times t1 to t7, the rotational speed of the electric motor 10 is increased by temporarily increasing its load factor. The state detection unit 62 detects the operating state of the electric motor 10 and the temperature output by the temperature detector 31 during the period in which the rotational speed of the electric motor 10 is gradually increased. More precisely, the state detection unit 62 detects the load factor of the electric motor 10, its rotational speed, and the temperature output by the temperature detector 31 for each predefined minute and stores this information in the memory 51. In the present embodiment, a constant ambient air temperature is assumed, but the embodiment is not limited to this.The condition monitoring unit 62 can detect the ambient air temperature for each minute from the ambient air temperature detector 33.

[0059] Referring to Fig. 1. The state detection unit 62 detects a torque command contained in the operating command generated by the operating control unit 43 of the machine control 41. The torque command corresponds to the load factor of the electric motor 10, so that the state detection unit 62 can calculate the load factor from the torque command.

[0060] The parameter calculation unit 63 calculates the parameters of model 10a for the electric motor based on the variables detected by the state detection unit 62. According to the present embodiment, the parameter calculation unit 63 calculates parameters including the heat capacities C1, C2, C3, C4, and C5 and the coefficients ha, hb, hc1, hc2, hc3, and hd for heat transfer based on the amount of heat generated in the coil 16 and the stator core 20 and the temperature detected by the temperature detector 31. Furthermore, the parameter calculation unit 63 calculates the constants k1 and k2 in equations (6) and (7) as parameters. The parameter calculation unit 63 calculates the parameters such that the change in the estimated temperature of model 31a for the temperature detector during the simulation approximates the change in the actually measured temperature.

[0061] The parameter calculation unit 63 sets an initial value for each parameter. These initial parameter values ​​can be set in any way. Using the loss calculation unit 54, the parameter calculation unit 63 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. The loss calculation unit 54 calculates the primary copper loss P. c1 and the iron loss P i using Table 1 and equations (6) to (10) based on the speed of electric motor 10 and the load factor of electric motor 10, which are detected by the state detection unit 62.

[0062] Equations (6) and (7) for calculating the primary copper loss P c1 and iron loss P iThe constants k1 and k2 are included. Furthermore, the loss calculation unit 54 calculates the loss for the given minute time dt, i.e., the amount of heat generated for one minute. Thus, the loss calculation unit 54 calculates the primary copper loss P. c1 and the iron loss P i in equations (1) and (2) based on the actual measured values, including the operating command (load factor) for the electric motor and the output of the rotation position detector 32.

[0063] The parameter calculation unit 63 estimates the component's temperature using the temperature calculation unit 55. The temperature calculation unit 55 calculates the estimated temperature of the temperature detector 31 based on the first model 10a for the electric motor, using the respective parameters and the losses calculated by the loss calculation unit 54. In other words, the temperature of model 31a for the temperature detector is estimated by simulation.

[0064] The temperature calculation unit 55 can calculate the change in the estimated temperature, as detected by the temperature detector 31 over time after the electric motor 10 is started, based on the experimentally set parameters. The model temperature for each component of the electric motor 10 can be calculated using the differential equations (1) to (5) above. The initial temperature of the model for each component can be set, for example, to the ambient air temperature, i.e., the room temperature at the start of the electric motor 10.

[0065] The evaluation unit 66 of the parameter calculation unit 63 compares the temperature (estimated temperature) of model 31a of the temperature detector calculated by the temperature calculation unit 55 with the temperature actually measured by temperature detector 31, and thus evaluates the parameter provisionally set in the first model 10a for the electric motor. In this example, the evaluation unit 66 only evaluates the temperature of model 31a of the temperature detector, without evaluating any other variables. The temperature change of model 31a of the temperature detector only needs to be close to the change in the actual temperature, and the temperature of at least some of the other components is not evaluated.

[0066] Subsequently, the parameter change unit 67 of the parameter calculation unit 63 modifies the parameter based on the result of the evaluation in the evaluation unit 66. Then, based on the modified parameter, the calculation of the loss by the loss calculation unit 54, the calculation of the estimated temperature of the model for the temperature detector by the temperature calculation unit 55, the evaluation by the evaluation unit 66, and the modification of the parameter by the parameter change unit 67 are repeated using the same calculation as above. If the evaluation by the evaluation unit meets the specified conditions, the determination can be made as the final parameter.

[0067] In this respect, the number of possible combinations of parameters in model 10a for the electric motor is very large. This multitude of parameters can be defined using a machine learning method. For example, the number of parameters can be determined using Bayesian optimization. In Bayesian optimization, an objective function to be evaluated is created for explanatory variables, including the input parameters. Then, the parameters for which a minimum or maximum of the objective function is predicted are identified and determined. By repeating this search for parameters, the optimal values ​​can be determined. Furthermore, the range within which each parameter is set can be predefined.

[0068] In this example, with respect to the temperature of temperature detector 31, the objective function is defined as the difference between the temperature (estimated temperature) of model 31a of the temperature detector, estimated by the first model 10a for the electric motor, and the actual measured temperature recorded by temperature detector 31. In other words, with respect to the temperature of temperature detector 31, the objective function can use a difference between the predicted value calculated from equations (1) to (5) based on the experimentally determined parameters and the actual measured value recorded by temperature detector 31. For example, the average value of the differences within a short period of time can be used as the objective function. The parameter change unit 67 then searches for the next parameter to reduce the objective function.

[0069] In Bayesian optimization, the search for and evaluation of parameters can be repeated. The evaluation unit 66 can accept the parameter values ​​at that point, as long as the objective function lies within a predefined domain. If, however, the objective function deviates from the predefined domain, the next parameter search can be performed. The Bayesian optimization method reduces computational effort because the search is conducted with a prediction of the range in which the solution exists.

[0070] The parameters included in model 10a for the electric motor can be determined by a method of choice in addition to setting them by Bayesian optimization. For example, a range within which each parameter is set can be defined in advance. The parameter change unit 67 of the parameter calculation unit 63 randomly sets a variety of parameters within a parameter range. The temperature calculation unit 55 estimates the temperature of model 31a for the temperature detector based on the set parameters. The evaluation unit 66 can evaluate the set parameters based on the actual temperature readings from the temperature detector 31. This method of parameter setting is called a random search method.

[0071] Alternatively, the parameter change unit 67 can adjust parameters at predefined intervals within the range in which the parameters are set. The temperature calculation unit 55 estimates the temperature of model 31a for the temperature detector based on the set parameters. The evaluation unit 66 performs an evaluation for all combinations of discretely set parameters. This procedure is called the grid search method.

[0072] In the random search method or the grid search method, the evaluation unit 66, as in the Bayesian optimization method, evaluates the temperature of the temperature detector 31. The evaluation unit 66 can accept the values ​​of the parameters at this time, as long as the objective function lies within a predefined range. Alternatively, the evaluation unit 66 can also accept the parameters for which the objective function is best. The evaluation unit 66 can determine a parameter in model 10a for the electric motor for which the estimated temperature of the temperature detector 31 corresponds well with the actual measured temperature recorded by the temperature detector 31.

[0073] In the present embodiment, the parameters are set such that a temperature change detected by the temperature detector 31 can be accurately estimated. In this embodiment, only the temperature of the temperature detector that measures the coil temperature can be evaluated during parameter assessment, since the temperatures of components not belonging to the temperature detector 31 may differ from the actual temperatures. In this way, the parameters can be set quickly and with minimal computational effort. The generated first model 10a of the electric motor can be stored in memory 51.

[0074] In the embodiment described above, operation in the no-load state was used as the operating pattern for driving the electric motor 10 to determine the parameters of the first model 10a for the electric motor, but the embodiment is not limited to this. When determining the parameters of the first model 10a of the electric motor, it is advantageous to operate the electric motor 10 in various operating patterns to capture its operating state. For example, an operating pattern can be assumed in which the load factor of the electric motor 10 repeatedly increases and decreases. The load factor of the electric motor 10 can be varied significantly to change the speed of the electric motor. The temperature detected by the temperature detector 31 rises or falls rapidly. As described above, it is possible to assume an operating pattern with a steep temperature change of the electric motor.

[0075] In the embodiment described above, the coil, including the windings, which serves as a component of the electric motor for temperature estimation, was described as an example, but the embodiment is not limited to this. Any component of the electric motor can be used as a component for calculating the estimated temperature. A temperature detector can then be attached to any component to calculate the estimated temperature.

[0076] The embodiment described above is a synchronous motor in which the rotor has a permanent magnet, but the embodiments are not limited to this configuration. The entire monitoring device according to the present embodiment can be applied to any electric motor. For example, the model for the electric motor in the present embodiment can also be applied to an induction motor in which the rotor does not have a permanent magnet.

[0077] Fig. Figure 5 shows a model for a second electric motor according to the present embodiment. Fig. Figure 5 shows a first model 30a when the operating condition of the second electric motor is normal. In this case, the second electric motor is an induction motor. The rotor of an induction motor contains a cage-shaped conductor made of stainless steel, copper, or a similar material. The cage-shaped conductor is attached to the shaft and rotates with the shaft. In an induction motor, the magnetic force generated by the stator coil causes an induced current to flow into the interior of the cage-shaped conductor. This creates a magnetic field around the cage-shaped conductor, and the rotor rotates.

[0078] In an asynchronous motor, a secondary copper loss P occurs. (c2A secondary loss occurs when the current flows through the cage-shaped conductor of the rotor. This secondary loss corresponds to the Joule heating caused by the current flowing through the cage-shaped conductor. In model 30a for the second electric motor, the heat in the rotor is generated by the secondary copper loss. The heat capacities of the components and the coefficients of heat transfer between the components of the second electric motor are similar to those of the first model 10a for the first electric motor.

[0079] In the differential equation for the temperature change of the constituent part in model 30a of the second electric motor, the differential equation for calculating the temperature change of the rotor differs from that of the first model 10a of the first electric motor. The differential equation for expressing the temperature change of the rotor is represented by equation (11) below. [Math. 5] dT4dt=Pc2+hc3(T3−T4)C4 P c2 secondary copper loss

[0080] In equation (11) the heat generation quantity of the secondary copper loss P is c2 Equation (4) of model 11a for the rotor of the first electric motor was added. The differential equations representing the temperature changes in the other components—the coil, the stator core, the air layer, and the temperature detector—are the same as the differential equations in a thermal model for the first electric motor.

[0081] The Loss Calculation Unit 54 calculates the amount of heat generated due to secondary copper loss occurring in the rotor conductor. The Loss Calculation Unit 54 estimates the current flowing through the cage-shaped conductor. The Loss Calculation Unit 54 can calculate the secondary copper loss from the current flowing through the conductor, the conductor's secondary resistance, the conductor's inductance, and the mutual inductance between the conductor, the stator, and the coil. The conductor's inductance, mutual inductance, and secondary resistance can be predetermined.

[0082] The total loss P t and the primary copper loss P c1 The total loss and primary copper loss in an asynchronous motor can be calculated similarly to those in a synchronous motor. Then the iron loss P can be determined. i taking into account the secondary copper loss P(c2 ) can be calculated according to equation (12). [Math. 6] Pi = Pt − Pc1 − Pc2 P i Iron loss

[0083] In this way, the primary copper loss, iron loss, and secondary copper loss are calculated in the first model 30a of the second electric motor. Also in the first model 30a of the second electric motor, the parameter calculation unit 63 can calculate parameters contained in the first model 30a of the second electric motor when the operating state of the first electric motor is normal, using the same control as that of the first model 10a of the first electric motor. Then, the temperature estimation unit 53 can calculate the estimated temperature of the component when the operating state of the electric motor is normal, using the first model 30a of the second electric motor.

[0084] Fig.Figure 6 shows a diagram of the estimated temperature of the temperature detector, which is estimated by the temperature estimation unit using the parameters calculated by the parameter calculation unit of the present embodiment. In this case, an example for a second electric motor is shown. Fig. Figure 6 shows a diagram when the simulation is performed with parameter set A and parameter set B, whose values ​​differ from each other. Parameter set A and parameter set B are calculated by the parameter calculation unit 63. The parameters contained in parameter set A and parameter set B are listed in Table 2. [Table 2] Table 2 Parameters Unit of heat transfer coefficient: [W / K] Unit of mass: [kg] Parameter set A Parameter set B heat transfer coefficient ha 5.48 6.72 heat transfer coefficient hb 10.82 13.78 heat transfer coefficient hc1 2.82 1.49 heat transfer coefficient hc2 1.056 13.35 heat transfer coefficient hc3 7.5 7.26 heat transfer coefficient hd 14.7 4.88 mass m1 of the coil 1.37 1.37 Mass m2 of the stator core 23.19 24.2 mass m3 of the air layer 0.00347 0.000353 Mass m4 of the rotor 3.43 4.7 mass m5 of the temperature detector 0.072 0.02

[0085] Parameter sets A and B are determined by operating the second electric motor with different running patterns. Table 2 describes the heat transfer coefficients, which result from multiplying the heat transfer coefficients between the respective components of the electric motor by their contact areas. In addition, the heat capacity is also calculated by multiplying the specific heat of the material of each component by its mass. The specific heat of each material can be predetermined, so Table 2 lists the mass m of each component for calculating the heat capacity. Comparing parameter sets A and B, it is observed that some parameters, such as the heat transfer coefficients hc2 and hd and the rotor mass m4, exhibit very different values ​​between the two sets.

[0086] On the other hand, it is from Fig. 6. It is evident that the estimated temperature of the temperature detector calculated using parameter set B agrees well with the estimated temperature of the temperature detector calculated using parameter set A. In particular, the temperature change agrees well both during the period in which the temperature increases and during the period in which the temperature fluctuates within a given range. Furthermore, the Fig. The temperature change shown in Figure 6, estimated by the temperature estimation unit 53, corresponds well with the temperature change detected by the temperature detector 31 when the electric motor 10 is actually driven.

[0087] There are parameters whose values ​​differ significantly between parameter set A and parameter set B. It can be seen that at least one parameter from both parameter sets A and B has different values ​​than the parameter set of the actual electric motor. In particular, it can be seen that at least some parameters from the multitude of heat capacities and heat transfer coefficients are set to values ​​that differ from the actual heat capacities or heat transfer coefficients. For example, it can be seen that at least one of the heat transfer coefficients, hc2, from parameter set A and hc2 from parameter set B, is far removed from the actual heat transfer coefficient.

[0088] As described above, in the monitoring device for an electric motor of the present embodiment, the temperature of the temperature detector can be accurately estimated even if at least some of the several parameters deviate from the actual values. Furthermore, the parameter calculation unit of the present embodiment can adjust parameters of such a model of electric motor.

[0089] Although in the present embodiment a temperature detector is attached to the electric motor, the embodiment is not limited to this. Multiple temperature detectors can be attached to multiple components of the electric motor. The evaluation unit of the parameter calculation unit can compare the measured temperature of the multiple temperature detectors with the estimated temperature calculated by simulation. The parameter adjustment unit can adjust the parameter of the first model for the electric motor so that the estimated temperature of the multiple components approximates the measured temperature detected by the actual temperature detector.

[0090] The more temperature detectors are attached to the electric motor, the closer the respective values ​​of the various parameters of the first model of the electric motor can be adjusted to the actual values. Furthermore, the estimated temperature of the constituent part of each electric motor can be adjusted to closely approximate the actual measured temperature. As a result of the installation of multiple temperature detectors, all heat capacities and coefficients relating to heat transfer can be essentially identical to the actual heat capacities and coefficients relating to heat transfer. In this case, when the temperature estimation unit estimates the temperatures of the components, the temperatures of all components correspond exactly to the actual temperatures of the components.

[0091] Next, a mode for generating an abnormality model is described, which creates a second model for the electric motor when the operating state of the electric motor is abnormal. The monitoring device for an electric motor 2 according to the present embodiment generates a second model when the cooling performance of the electric motor changes. The monitoring device 2 provides the operator or another device with information about the cooling performance of the electric motor based on the second model. Furthermore, the monitoring device 2 determines whether the cooling condition of the electric motor has deteriorated or not.

[0092] When describing the mode for creating an abnormality model, the following are described: Fig. 2 shown first electric motor 10 and the one in Fig.The first model 10a of the first electric motor shown in section 3 is used as an example, but the embodiment is not limited to it. For the second electric motor and the one shown in Fig. Model 30a of the second electric motor, as shown in Figure 5, can be controlled similarly to the first electric motor. Furthermore, as in the normal model creation mode, the temperature detected by the temperature detector 31 attached to the coil 16 is described using an example.

[0093] Referring to Fig.1 The monitoring device for an electric motor 2 of the present embodiment includes a model search unit 71 for generating a second model for the electric motor when the operating state of the electric motor is abnormal. The model search unit 71 includes a temperature determination unit 72 for determining whether 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 is within a predetermined determination range or not.

[0094] The model search unit 71 includes a setting unit 73, which generates the second model 10b based on the first model 10a if 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 range. If the difference between the estimated temperature and the measured temperature of the temperature detector is large, the setting unit 73 modifies a coefficient relating to the heat transfer of a part of the first model 10a and generates the second model 10b in which the estimated temperature exactly matches the measured temperature.In the present embodiment, the setting unit 73 generates the second model 10b in which the coefficient relating to the heat transfer between the model 20a of the stator core and the model 36a of the ambient air of the first model 10a is changed such that the estimated temperature of the temperature detector 31 based on the second model 10b corresponds to the measured temperature.

[0095] In the present embodiment, the monitoring device 2 comprises a power estimation unit 74, which estimates the cooling power required to cool the electric motor 10. The power estimation unit 74 calculates a variable CA with respect to the cooling power of the electric motor based on the coefficient hb with respect to the heat transfer between model 20a of the stator core and model 36a of the ambient air in the first model 10a, and the coefficient hbx with respect to the heat transfer between model 20a of the stator core in the second model 10b and model 36a of the ambient air.

[0096] The monitoring device 2 comprises a notification unit 75, which notifies another device of information regarding the cooling performance of the electric motor. The notification unit 75 notifies another device of information relating to the second model 10b. Furthermore, according to the present embodiment, the notification unit 75 notifies another device of information relating to a deterioration in the cooling performance of the electric motor.

[0097] Each unit of the model search unit 71, the temperature determination unit 72, and the setting unit 73 corresponds to a processor controlled by a pre-defined program. Each unit of the power estimation unit 74 and the notification unit 75 corresponds to a processor controlled by a pre-defined program. By reading the program and executing the control defined in the program, the processor functions as each of the units.

[0098] If an anomaly occurs in the cooling performance of the electric motor, the temperature of the electric motor can rise. An example is the failure of a fan 29, a blockage of an air opening 28a through which air circulates by a foreign object such as dust, or the accumulation of sludge on the surface of an electric motor 10. In the present embodiment, if the temperature measured by the temperature detector 31 attached to the electric motor deviates from a predetermined range based on the estimated temperature under normal operating conditions, it is determined that the cooling performance for cooling the electric motor is abnormal. In other words, it is determined that the operating conditions of the electric motor are abnormal.

[0099] Fig. Figure 7 is a diagram showing a temperature change during normal operation of the first electric motor. Referring to Fig. 1, Fig. 3 and Fig. Figure 7 represents the vertical axis of the diagram, which shows the temperature of the temperature detector 31 attached to the coil 16. The horizontal axis represents the elapsed time. A solid line indicates the measured temperature actually detected by the temperature detector 31. In other words, the measured temperature detected by the state sensing unit 62 is shown.

[0100] In the present example, the current supply is started at time t0, and the load on the electric motor 10 is rapidly increased. With the increase in current, the temperature of the temperature detector 31 rises rapidly. At time t1, the current supplied to the electric motor 10 is reduced. The speed of the electric motor 10 is maintained at a substantially constant speed without load. After time t1, the temperature of the temperature detector 31 gradually decreases.

[0101] In Fig. The estimated temperature of temperature detector 31, which is estimated based on the first model 10a of the first electric motor, is represented by a dashed line. The first model 10a of the electric motor is generated beforehand in the normal mode of model creation. It can be seen that, under normal operating conditions of the electric motor, the estimated temperature of temperature detector 31, which is estimated based on the first model 10a of the electric motor, corresponds exactly to the temperature measured by temperature detector 31.

[0102] Fig. This is a diagram showing a temperature change as the cooling capacity of the first electric motor decreases. The operating state of Fig. 8 is the same as the operating state of Fig. 7. With reference to Fig. 1, Fig. 3 and Fig.8. The load is increased from time t0 to time t1. At time t1, the load is set to zero. In the section from time t0 to time t2, the cooling capacity for cooling the electric motor is normal. In other words, the operating state of the electric motor is normal. The measured temperature, recorded by the temperature detector 31, corresponds exactly to the estimated temperature calculated by the first model 10a of the electric motor.

[0103] After time t2, however, an anomaly occurs in the cooling state of the electric motor 10, and the measured temperature rises. An example is presented in which the fan 29 fails and the cooling fan 27 stops. Due to the failure of the fan 29, the heat dissipation from the stator core 20 and the housings 21 and 22 becomes insufficient, and the overall temperature of the electric motor 10 rises. The temperature of the temperature detector 31, which is attached to the coil 16, also rises.

[0104] At time t2, the model search unit 71 of the monitoring device 2 detects that an abnormality has occurred in the operating state of the electric motor 10. The model search unit 71 defines a section EP in which an abnormality has occurred in the electric motor 10. The model search unit 71 searches for a coefficient hbx, which is obtained by changing the coefficient hb with respect to heat transfer between model 20a of the stator core and model 36a of the ambient air in the first model 10a, such that the estimated temperature in section EP exactly corresponds to the measured temperature. In other words, the model search unit 71 generates the second model 10b, which contains the coefficient hbx for heat transfer.

[0105] The power estimation unit 74 of the monitoring device 2 calculates the variable CA, which relates to the cooling capacity 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 displays information about the cooling capacity of the electric motor 10 on the display unit 52 to notify the operator. Alternatively, the notification unit 75 of the monitoring device 2 transmits information about the cooling capacity of the electric motor to another device. Each step is described in detail below.

[0106] During the period in which the electric motor 10 is driven, the state detection unit 62 of the monitoring device 2 detects the operating state of the electric motor, including the operating command of the electric motor 10 and the rotational speed output by the rotational position detector 32 at each predetermined time interval. The temperature estimation unit 53 calculates an estimated temperature of each component of the electric motor based on the operating state detected by the state detection unit 62 and the first model 10a of the electric motor. In particular, in the present embodiment, the temperature estimation unit 53 calculates the estimated temperature of the temperature detector 31 using the first model 10a at each predetermined time interval.

[0107] Furthermore, the state detection unit 62 records the measured temperature output by the temperature detector 31 at each predetermined time interval. The memory 51 stores the time, the estimated temperature of the temperature detector 31 calculated by 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 every one-second interval.

[0108] 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 range. The temperature determination unit 72 can, for example, calculate a moving average of the difference between the measured and estimated temperatures. The temperature calculation unit 72 can, for example, calculate a moving average over the last minute. If the moving average then deviates from a predetermined range, it can be determined that the difference between the measured and the determined temperature deviates from the range. The temperature determination unit 72 can determine that an anomaly occurred in the operating state of the electric motor at the central point in time of the moving average interval.In this case, an abnormality in the electric motor is found to have occurred at time t2 if the measured temperature is significantly higher than the estimated temperature.

[0109] The control system in which the temperature measuring unit determines the measured temperature is not limited to this mode, and whether the difference between the measured temperature and the estimated temperature deviates from the specified range can be determined by any control system. For example, the temperature measuring unit determines whether the absolute value of the difference between the measured temperature and the estimated temperature is greater than a predefined value. If the absolute value is greater than a predefined value, it can be determined that the difference between the measured temperature and the actual measured temperature deviates from the specified range.

[0110] According to Fig. 8. Starting at time t2, when the anomaly occurred, the temperature determination unit 72 enters a section EP of a predetermined duration to generate the second model 10b from the first model 10a and the temperature measured by the temperature detector 31. The section EP of the present embodiment is a section in which the cooling of the electric motor is insufficient. In section EP, the difference between the measured temperature and the estimated temperature deviates from the determination range.

[0111] Fig.Figure 9 shows a second model for the electric motor according to the present embodiment. In the second model 10b, the coefficient hbx of heat transfer between the model 20a of the stator core and the model 36a of the ambient air is different from the coefficient hb of heat transfer between the model 20a of the stator core and the model 36a of the ambient air in the first model 10a. The other parameters of the second model 10b are the same as those of the first model 10a.

[0112] With reference to Fig. 1, Fig. 3, Fig. 8 and Fig. 9 the model search unit 71 generates a second model 10b of the first electric motor, as in Fig. Figure 9 illustrates this. The setting unit 73 of the model search unit 71 adjusts the heat transfer coefficient hbx in the second model 10b such that the estimated temperature of the temperature detector of the second model 10b corresponds to the measured temperature of the temperature detector 31 in section EP. In the present embodiment, only the heat transfer coefficient hbx is changed. The parameters of the other components of the first model 10a are used unchanged. In the present embodiment, the range of the numerical value of the heat transfer coefficient hbx is predetermined.

[0113] Any value can be assumed as the initial value for the coefficient hbx, which relates to the heat transfer between model 20a of the stator core and model 36a of the ambient air. An example is the use of an initial value obtained by subtracting a predetermined relative value from the coefficient hb used for heat transfer in the first model. The loss calculation unit 54 of the temperature estimation unit 53 acquires the operating state of the electric motor in section EP from memory 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 model 31a of the temperature detector in section EP at predetermined time intervals.

[0114] The setting unit 73 can determine the coefficient hbx with respect to heat transfer using a similar procedure to that used for setting the parameters of the first model 10a. The setting unit 73 of the present embodiment calculates the coefficient hbx for heat transfer using the evaluation unit 66 and the parameter change unit 67 of the parameter calculation unit 63. Similar to the setting of the plurality of parameters of the first model 10a, the setting unit 73 can determine the coefficient hbx with respect to heat transfer using a machine learning method.

[0115] The evaluation unit 66 retrieves the measured temperature of the temperature detector 31 in section EP from the memory 51. The evaluation unit 66 calculates the difference between the measured temperature and the estimated temperature of the temperature detector 31 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 memory 51 stores the heat transfer coefficient hbx of the second model 10b and the difference between the measured temperature and the estimated temperature of the temperature detector 31 in section EP.

[0116] The coefficient hbx for heat transfer in the second model 10b can be determined using Bayesian optimization. The objective function is defined as the difference between the temperature estimated by the second model 10b of the electric motor (estimated temperature) of model 31a of the temperature detector and the actual measured temperature detected by temperature detector 31. The evaluation unit 66 evaluates the coefficient hbx for heat transfer that is provisionally set in the second model 10b of the electric motor. The parameter change unit 67 searches for the next parameter to reduce the objective function. If the evaluation by the evaluation unit 66 satisfies the specified conditions, the final parameter can be determined.

[0117] Alternatively, the parameter change unit 67 can perform a grid search procedure to change the coefficient hbx for heat transfer at predetermined intervals within the range of the coefficient hbx for heat transfer between the model 20a of the stator core and the model 36a for the ambient air.

[0118] The parameter change unit 67 generates the second model 10b, in which the coefficient hbx for heat transfer is set to a value within a predefined range. The temperature estimation unit 53 calculates the estimated temperature of the temperature detector model 31a at each predefined time interval in section EP based on the second model 10b. Subsequently, the evaluation unit 66 in section EP calculates a difference between the measured temperature and the estimated temperature of the temperature detector at each predefined time interval. The memory 51 stores the coefficient hbx for heat transfer and the difference between the measured temperature and the actual measured temperature.

[0119] Next, the parameter change unit 67 modifies the heat transfer coefficient hbx at predefined intervals. Then, the temperature estimation unit 53 and the evaluation unit 66 repeat a similar calculation and store the results in section EP. The parameter change unit 67 incrementally changes the heat transfer coefficient hbx at a predefined interval. The evaluation unit 66 calculates the difference between the measured temperature and the estimated temperature after changing the heat transfer coefficient hbx. This calculation is performed for all heat transfer coefficients hbx. The memory unit 51 stores the difference between the measured temperature and the actual measured temperature in section EP with respect to each heat transfer coefficient hbx.

[0120] The setting unit 73 selects the second model with the smallest difference between the measured temperature and the estimated temperature of the temperature detector 31 from the multitude of calculation results stored in memory 51. In section EP, for example, the second model 10b with the smallest root mean square (RMS) difference between the measured temperature and the estimated temperature of the temperature detector 31, recorded in each predetermined time interval, is selected. The setting unit 73 records a parameter containing a coefficient hbx for the heat transfer of the second model.

[0121] In the grid search method described above, the calculation is performed by setting all coefficients hbx for heat transfer in each predefined interval, but the embodiment is not limited to this. For example, a permissible range of the difference between the estimated temperature and the temperature measured by the temperature detector can be predefined. The setting unit 73 can terminate the search according to the second model if the difference between the estimated temperature and the temperature measured by the temperature detector is within a permissible range.In section EP, the setting unit 73 can, for example, determine that the difference between the estimated temperature and the measured temperature of the temperature detector is within the permissible range if the root mean square of the difference between the estimated temperature and the measured temperature, recorded in each predetermined time interval, is less than a predetermined setpoint. The setting unit 73 can adopt a second model that includes a coefficient relating to the heat transfer at that time.

[0122] It should be noted that the model search unit 71 can generate the second model in which the estimated temperature of the temperature detector corresponds exactly to the measured temperature through any control. For example, the coefficient for heat transfer between the model for the stator core and the model for the ambient air can be calculated using the least squares method, so that the error of the estimated temperature with respect to the measured temperature becomes small.

[0123] Next, the power estimation unit 74 calculates a variable relating to the cooling capacity of the electric motor 10, based on the first model 10a and the second model 10b generated by the model search unit 71. The power estimation unit 74 captures the coefficient hb with respect to heat transfer in the first model 10a and the coefficient hbx with respect to heat transfer in the second model 10b. The power estimation unit 74 calculates a coefficient for heat transfer between model 20a of the stator core and model 36a of the ambient air from the respective coefficients hb and hbx for heat transfer.

[0124] The power estimation unit 74 calculates the variable CA, which relates to the cooling capacity 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. The variable CA of the present embodiment is a ratio obtained by dividing the heat transfer coefficient between the stator core (model 20a) and the ambient air (model 36a) in the second model 10b by the heat transfer coefficient between the stator core (model 20a) and the ambient air (model 36a) in the first model 10a. In other words, the variable CA with respect to cooling capacity is the ratio of the heat transfer coefficient in the second model 10b to the heat transfer coefficient in the first model 10a. It can be observed that the cooling capacity in the second model 10b deteriorates as the ratio decreases.If the ratio of the heat transfer coefficients is smaller than a predetermined value, the power estimation unit 74 determines that the cooling performance of the electric motor has deteriorated.

[0125] Referring to Fig.In this embodiment, the display unit 52 shows information relating to the second model 10b created by the model search unit 71. For example, the display unit 52 shows information about the second model 10b where the estimated temperature of the temperature detector 31 most closely matches the measured temperature. Alternatively, the display unit 52 shows a coefficient hbx relating to the heat transfer between the model 20a of the stator core and the model 36a of the ambient air, a heat transfer coefficient, or a variable CA relating to the cooling performance. The operator can estimate the cooling performance of the electric motor based on the information about the second model 10b. For example, the operator can estimate that the cooling performance deteriorates when the heat transfer coefficient of the second model 10b decreases.Alternatively, the display unit 52 can display information about the deterioration of the cooling performance of the electric motor, as estimated by the power estimation unit 74.

[0126] If the cooling capacity of the electric motor is expected to decrease, the operator can perform maintenance work before the electric motor fails. For example, the operator can clean, repair, or replace the electric motor. Alternatively, the operator can analyze the measures taken up to the point of failure. For example, the machine load can be reduced until the electric motor is replaced to delay its failure.

[0127] The display unit 52 can show information indicating that the electric motor is functioning normally. For example, if the variable CA, which relates to the cooling capacity, is greater than or equal to a predefined value, the display unit can indicate that the electric motor's cooling capacity is sufficient. The display unit can show a measurement result each time the power estimation unit 74 performs a measurement.

[0128] The notification unit 75 of the monitoring device 2 can transmit information about the cooling performance of the electric motor to another device. For example, the notification unit 75 transmits the fact that the cooling performance of the electric motor has deteriorated to the machine control 41. Based on the notification unit 75, the machine control 41 can perform any desired control action. The machine control 41 can then change the operating state of the machine 1. For example, the machine control 41 can reduce the speed at which the electric motor is driven or change the rate of increase and decrease of speed. Alternatively, the machine control 41 can limit the operation of the electric motor, for example, by reducing the upper limit of the electric motor's speed.

[0129] Any variable can be used as the variable for the power estimation unit to determine the cooling capacity based on the coefficient of heat transfer between the stator core and the ambient air. For example, a variable can be used that is obtained by subtracting the coefficient of heat transfer between the stator core model and the ambient air model in the first model from the coefficient of heat transfer between the stator core model and the ambient air model in the second model.

[0130] The first model for the electric motor according to the above embodiment includes the model for the coil, the model for the stator core, the model for the temperature detector, the model for the air layer, the model for the rotor, and the model for the ambient air, but the embodiment is not limited thereto. The first model for the electric motor may also include models for other components. For example, the first model for the electric motor may include a model for the housing for supporting the stator and rotor, a model for a bearing, a model for a shaft for supporting the rotor, and the like. Alternatively, the first model for the electric motor may omit certain models. For example, the model for the electric motor may not include the model for the air layer.

[0131] The monitoring device for an electric motor according to the present embodiment is designed to switch between the normal model creation mode and the abnormal model creation mode, but the monitoring device for an electric motor is not limited to this. The monitoring device for an electric motor cannot have the function of the normal model creation mode. The first model of the electric motor, when the operating state of the electric motor is normal, can be generated by another device.

[0132] Furthermore, in the present embodiment, the monitoring device for an electric motor includes a computing unit that is distinct from the machine control, but the embodiment is not limited to this. The machine control can also function as a monitoring device for an electric motor. In other words, the processor of the machine control can function as a unit of the monitoring device, such as the model search unit, the temperature estimation unit, and the parameter calculation unit.

[0133] In each of the control systems described above, the sequence of steps can be suitably modified, provided that the functions and actions remain unchanged. The embodiments described above can be combined as desired. In each of the drawings described above, identical or equivalent parts are identified by an identical reference numeral. The embodiments described above are examples and do not limit the invention. Furthermore, the embodiments include the modifications of the embodiments defined in the claims. Reference symbol list 2 Monitoring device 10 Electric motor 10a, 10b model for an electric motor 11 Rotor 11a Model for a rotor 16 coil 16a model for one coil 20 stator core 20a Model for a stator core 29 blowers 30a model for an electric motor 31a Model for a temperature detector 35a Model for an air layer 36a model for ambient air 51 storage 52 Display section 53 Temperature estimation unit 54 Loss Calculation Unit 55 Temperature calculation unit 62 Condition monitoring unit 63 Parameter calculation unit 66 evaluation units 67 Parameter change unit 71 Model search unit 72 Temperature determination unit 73 Setting unit 74 Power Estimation Unit 75 notification units hb, hbx coefficient of heat transfer CA Variable QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] JP 2015-167436A [0002, 0003] JP 11-262102A

[0003] WO 2022 / 085543 A

[0003] JP 2010-287971A

[0003] JP 2006-311735A

[0003]

Claims

[1] Monitoring device for an electric motor, comprising: a state detection unit configured to detect an operating state of an electric motor, including a measured temperature detected by a temperature detector attached to the electric motor; a temperature estimation unit configured to calculate an estimated temperature of the temperature detector based on a model for the electric motor; a memory configured to store a first model for the electric motor when the operating state of the electric motor is normal; a model search unit configured to generate a second model for the electric motor when the operating state of the electric motor is abnormal; and a power estimation unit configured to estimate a cooling power of the electric motor, wherein the model for the electric motor includes models for components of the electric motor, including a stator core, and a model for ambient air, a heat capacity is specified in at least one of the models for the components, a coefficient relating to heat transfer is specified between the models for the components of the electric motor and between the model for the stator core and the model for the ambient air, the model search unit generates a second model in which a coefficient related to heat transfer between the model for the stator core and the model for the ambient air of the first model is changed so that an estimated temperature of the temperature detector based on the second model corresponds to the measured temperature when a 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, and the performance estimation unit calculates a variable related to the cooling performance of the electric motor based on the coefficient related to the heat transfer between the stator core model and the ambient air model in the first model and the coefficient related to the heat transfer between the stator core model and the ambient air model in the second model. [2] A monitoring device for an electric motor according to claim 1, wherein the variable related to the cooling performance of the electric motor is a ratio obtained by dividing the coefficient related to the heat transfer between the stator core model and the ambient air model in the second model by the coefficient related to the heat transfer between the stator core model and the ambient air model in the first model, and the performance estimation unit determines that the cooling performance of the electric motor has deteriorated when the ratio is smaller than a predetermined determination value. [3] The monitoring device for an electric motor according to claim 1 or 2, further comprising a notification unit configured to notify another device of information regarding the cooling performance of the electric motor. [4] The monitoring device for an electric motor according to any one of claims 1 to 3 further comprises a display part configured to display the information related to the cooling performance of the motor. [5] A monitoring device for an electric motor according to any one of claims 1 to 4, further comprising a parameter calculation unit configured to calculate a parameter of the first model for the electric motor, wherein the state detection unit detects an operation command of the electric motor generated by the actual driving of the electric motor, the parameter comprises a heat capacity set in the model for the component and a coefficient related to the heat transfer between the models for the components, the temperature estimation unit includes a loss calculation unit configured to calculate a heat generation amount due to primary copper loss of the coil and a heat generation amount due to iron loss of the stator core based on the operation command, and a temperature calculation unit configured to calculate the estimated temperature of the temperature detector using the first model for the electric motor based on the heat generation amount of the coil and the heat generation amount of the stator core, the parameter calculation unit includes an evaluation unit configured to evaluate 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 changing unit configured to change a value of the parameter based on an evaluation result of the evaluation unit so that the estimated temperature of the temperature detector approaches the measured temperature, and a mode in which the parameter of the first model for the electric motor is calculated by the parameter calculation unit and a mode in which the second model for the electric motor is generated by the model searching unit are switchable.

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