Motor monitoring device

Abstract

This monitoring device comprises: a storage unit which stores a first model for an electric motor when the operation state of the electric motor is normal; and a model search unit which generates a second model for the electric motor when the operation state of the electric motor is abnormal. The model search unit adds a model for an additional member to the first model to generate the second model when the difference between a measured temperature from a temperature detector and an estimated temperature of the temperature detector based on the first model deviates from a determination range. The model search unit sets: a model for constituent portions of the electric motor that perform heat exchange with the model for the additional member; and factors pertaining to the heat transfer of the additional member so that the estimated temperature of the temperature detector based on the second model corresponds to the estimated temperature of the temperature detector.

Description

Technical Field

[0001] The present invention relates to a monitoring device for an electric motor.

Background Art

[0002] The operating state of a machine can be obtained by attaching a sensor to the machine. On the other hand, it is known that the operating state of a machine can be estimated by simulation without actually driving the machine (for example, Japanese Patent Laid-Open No. 63-106861).

[0003] An electric motor is arranged in a machine to operate a predetermined member. It is known that the temperature of an electric motor rises when it is driven. If the temperature of the electric motor becomes too high, the electric motor may not operate accurately or the constituent members of the electric motor may be damaged. For this reason, it is preferable that the electric motor be driven within an appropriate temperature range (for example, Japanese Patent Laid-Open No. 2010-268644).

[0004] The actual temperature when the electric motor is driven can be detected by a temperature detector attached to a constituent member of the electric motor. Alternatively, a simulation for estimating the temperature of the electric motor is known. In the simulation, a method using a thermal model that takes into account the heat capacity of the constituent members and the heat transfer between the constituent members is known in order to estimate the temperature of the electric motor (for example, International Publication No. 2020 / 188650 and Japanese Patent Laid-Open No. 2008-109816).

[0005] In the thermal model, a heat transfer coefficient or a thermal resistance is set between each of the constituent members to calculate the heat transfer between the constituent members. In the thermal model, the temperature of each constituent member can be calculated. In an electric motor, it is known to estimate the temperature when the electric motor is driven using a thermal model including a stator core, a coil, a rotor core, and the like.

Prior Art Documents

Patent Documents

[0006] [Patent Document 1] Japanese Patent Laid-Open No. 63-106861 [Patent Document 2] Japanese Patent Laid-Open No. 2010-268644 [Patent Document 3] International Publication No. 2020 / 188650 [Patent Document 4] Japanese Patent Laid-Open No. 2008-109816 [Summary of the Invention] [Problems to be Solved by the Invention]

[0007] When the electric motor is driven, heat is generated by the stator core, the coil fixed to the stator core, the bearings, etc. Further, when a component of the electric motor is damaged, the damaged component generates heat, and the temperature of the electric motor may rise abnormally. In order to identify the part that is generating heat during the period when the electric motor is driven, a temperature sensor can be attached to each component of the electric motor. Then, by monitoring the temperature of the component with the output of the temperature sensor, it is possible to identify the part where abnormal heat generation is occurring.

[0008] However, attaching temperature sensors to each component of the electric motor has the problem that a large number of temperature sensors are required. Also, considering the structure of the electric motor, it may be difficult to attach temperature sensors to internal components or rotating components of the electric motor. [Means for Solving the Problems]

[0009] A monitoring device for an electric motor according to an embodiment of the present disclosure includes a state acquisition unit that acquires the operating state of the electric motor, including the measured temperature detected by a temperature sensor attached to the electric motor. The monitoring device includes a temperature estimation unit that calculates the estimated temperature of the temperature sensor based on a model of the electric motor. The monitoring device includes a storage unit that stores a first model of the electric motor when the operating state of the electric motor is normal. The monitoring device includes a model search unit that generates a second model of the electric motor when the operating state of the electric motor is abnormal. The electric motor model includes a rotor model, a stator core model, a coil model, and a temperature sensor model as models of the components of the electric motor. A heat capacity is set for at least one component model. A coefficient for heat transfer between the component models is set. The model search unit includes a component addition unit that adds a model of an additional component that performs heat exchange with the electric motor component model to the first model to generate a second model when the difference between the measured temperature of the temperature sensor and the estimated temperature of the temperature sensor based on the first model deviates from a predetermined determination range. The model search unit includes a setting unit that sets a model of the additional member, a model of the components of the electric motor that perform heat exchange, and a coefficient related to the heat transfer of the additional member, so that the estimated temperature of the temperature sensor based on the second model corresponds to the measured temperature of the temperature sensor. [Effects of the Invention]

[0010] According to the monitoring device for an electric motor in an aspect of this disclosure, it is possible to provide information for estimating the part where an abnormality is occurring. [Brief explanation of the drawing]

[0011] [Figure 1] This is a block diagram of the monitoring device for the machine and electric motor in an embodiment. [Figure 2] This is a schematic cross-sectional view of the first electric motor in the embodiment. [Figure 3] This is a first model of the first electric motor in the embodiment. [Figure 4] This graph illustrates the motor operation patterns used to set the parameters in the first model of the electric motor. [Figure 5] This is the first model of the second electric motor in the embodiment. [Figure 6] This graph shows the results of a simulation using the parameters set in the parameter calculation unit. [Figure 7] This graph shows the temperature change of the temperature sensor when the electric motor is operating normally. [Figure 8] This graph shows the temperature change of the temperature detector when the electric motor is operating abnormally. [Figure 9] This is the second model, which is an additional component added to the first model of the first electric motor. [Figure 10] This is a second model in which the additional components exchange heat with multiple components of the electric motor. [Figure 11] This is a second model of the first electric motor. [Modes for carrying out the invention]

[0012] The motor monitoring device in this embodiment will be described with reference to Figures 1 to 11. The motor monitoring device in this embodiment has the function of estimating the temperature of predetermined components of the motor using a model of the motor. In particular, the motor monitoring device estimates the temperature output by temperature sensors attached to the components of the motor.

[0013] In this embodiment, the temperature actually measured by the temperature detector is referred to as the measured temperature. The temperature of the components estimated using the electric motor model is referred to as the estimated temperature. In this embodiment, an example of estimating the temperature of a temperature detector that detects the temperature of the stator coil, one of the components of the electric motor, is described.

[0014] Furthermore, the motor monitoring device of this embodiment provides information to the operator or other devices for estimating the location of the malfunction. For example, the operator estimates the location of the malfunction by looking at the information displayed on the display unit of the motor monitoring device.

[0015] Figure 1 is a block diagram of the machine and electric motor monitoring device in this embodiment. The machine 1 in this embodiment comprises an electric motor 10 that drives the components of the machine 1 and a machine control device 41 that controls the machine 1. The machine control device 41 in this embodiment is composed of a processing unit (computer). The machine control device 41 includes a CPU (Central Processing Unit) as a processor. The machine control device 41 has RAM (Random Access Memory) and ROM (Read Only Memory), etc., connected to the CPU via a bus.

[0016] The machine 1 in this embodiment is a numerically controlled machine. The machine 1 is driven based on command statements described in a pre-created operation program 45. The machine control device 41 includes a storage unit 42 that stores the operation program 45 and an operation control unit 43 that generates operation commands for the electric motor 10 based on the operation program 45. The machine 1 includes a drive device 44 that includes an electrical circuit that supplies electricity to the electric motor 10 based on the operation commands generated by the operation control unit 43. The electric motor 10 is driven by the power supplied by the drive device 44.

[0017] The memory unit 42 can be configured with a non-temporary storage medium capable of storing information. For example, the memory unit 42 can be configured with a storage medium such as a volatile memory, a non-volatile memory, a magnetic storage medium, or an optical storage medium. The operation control unit 43 corresponds to a processor that operates according to an operation program 45. The processor functions as the operation control unit 43 by reading the operation program 45 and executing the controls defined in the operation program 45.

[0018] Such a machine 1 can be any machine equipped with an electric motor 10. For example, machine 1 can be a machine tool that processes a workpiece. The electric motor 10 can be a spindle motor that rotates a tool, or a feed axis motor that moves the table or spindle head along a predetermined coordinate axis.

[0019] Figure 2 is a cross-sectional view of the first electric motor of this embodiment. Referring to Figures 1 and 2, the first electric motor 10 is a synchronous electric motor in which the rotor 11 has magnets 18. The electric motor 10 comprises the rotor 11 and the stator 12. The stator 12 includes a stator core 20 made of a magnetic material and coils 16 fixed to the stator core 20. The stator core 20 is formed, for example, of a plurality of magnetic steel plates stacked in the axial direction of the shaft 13. The coils 16 include, for example, windings wound around the stator core 20 and resin parts that fix the windings.

[0020] The rotor 11 is fixed to a rod-shaped shaft 13. The rotor 11 includes a rotor core 17 fixed to the outer surface of the shaft 13 and made of a magnetic material, and a plurality of magnets 18 fixed to the rotor core 17. The magnets 18 in this embodiment are permanent magnets.

[0021] The shaft 13 is connected to other members to transmit rotational force. The shaft 13 rotates around the rotation axis RA. The shaft 13 is supported by bearings 14 and 15, which act as bearings. In this embodiment, in the electric motor 10, the side on which the shaft 13 is connected to other members is referred to as the front side. The side opposite the front side is referred to as the rear side. In the example shown in Figure 2, arrow 91 indicates the front side of the electric motor 10.

[0022] The electric motor 10 includes a front housing 21 and a rear housing 22. The stator core 20 of the stator 12 is supported by the housings 21 and 22. Housing 21 supports a bearing 14. A bearing support member 24 that supports a bearing 15 is fixed to housing 22. Housings 21 and 22 rotatably support the shaft 13 via the bearings 14 and 15. A rear cover 23 that closes off the internal space of housing 22 is fixed to the rear end of housing 22.

[0023] A rotational position detector 32 for detecting the rotational position or rotational speed of the shaft 13 is located at the rear end of the shaft 13. In this embodiment, the rotational position detector 32 is configured as an encoder. A temperature detector 31 for detecting the temperature of the coil 16 is fixed to the coil 16 of the stator 12. In this embodiment, the temperature detector 31 is configured as a thermistor. The outputs of the temperature detector 31 and the rotational position detector 32 are input to the machine control device 41.

[0024] Examples of components of the electric motor 10 include a rotor 11, rotor core 17, magnet 18, stator 12, stator core 20, coil 16, housing 21, 22, shaft 13, rear cover 23, bearing support member 24, bearings 14, 15, temperature sensor 31, and rotational position sensor 32. The components of the electric motor 10 are not limited to this form, and any part of the electric motor 10 can be used. For example, a case that covers the stator may be used.

[0025] In this embodiment, the motor monitoring device 2 calculates an estimated temperature based on the temperature output of a temperature sensor 31 located on the motor 10. In this embodiment, the temperature output of a temperature sensor 31 located on the coil 16 of the stator 12 is estimated. In particular, the motor monitoring device 2 estimates the change in temperature of the temperature sensor 31 over time.

[0026] The motor monitoring device 2 is comprised of a processing unit (computer) including a CPU as a processor. The monitoring device 2 includes a storage unit 51 that stores information related to the monitoring of the motor 10. The storage unit 51 can be comprised of a non-temporary storage medium capable of storing information. For example, the storage unit 51 can be comprised of a storage medium such as volatile memory, non-volatile memory, magnetic storage medium, or optical storage medium. The monitoring device 2 includes a display unit 52 that displays information related to the motor 10. The display unit 52 can be comprised of any display panel such as a liquid crystal display panel or an organic EL (Electro-Luminescence) display panel.

[0027] The monitoring device 2 includes a temperature estimation unit 53 that calculates the estimated temperature of the temperature detector 31. The temperature estimation unit 53 includes a loss calculation unit 54 that calculates the amount of heat generated due to the primary copper loss of the coil 16 and the amount of heat generated due to the iron loss of the stator core 20 based on the operation command of the electric motor 10. The temperature estimation unit 53 also includes a temperature calculation unit 55 that estimates the temperature of the temperature detector 31 using an electric motor model (thermal model). The temperature calculation unit 55 calculates the estimated temperature of the temperature detector 31 based on the amount of heat generated due to the primary copper loss and iron loss, the heat capacity of the models of each component, and coefficients relating to heat transfer between the models of the components.

[0028] The monitoring device 2 in this embodiment has a function to calculate parameters included in the model of the electric motor. The parameters include the heat capacity set in the model of the component part of the electric motor 10 and coefficients relating to heat transfer between the component model parts. The monitoring device 2 includes a parameter calculation unit 63 that calculates the parameters included in the first model of the electric motor when the electric motor is operating normally.

[0029] The monitoring device 2 includes a state acquisition unit 62 that acquires the operating state of the electric motor 10 when it is actually driven. The operating state of the electric motor 10 includes the measured temperature detected by the temperature detector 31 attached to the electric motor 10. The operating state of the electric motor 10 includes the operation command of the electric motor 10 generated by actually driving the electric motor 10 and the rotational speed output from the rotational position detector 32. The operation command of the electric motor 10 can be acquired from the operation control unit 43. The state acquisition unit 62 can also acquire the ambient temperature from an ambient temperature detector 33 that detects the temperature of the environment in which the machine 1 is located. The ambient temperature detector 33 is, for example, positioned to detect the temperature around the machine 1.

[0030] The parameter calculation unit 63 in this embodiment calculates parameters so that the change in the estimated temperature of the temperature sensor, calculated by the motor model, corresponds to the change in the actual measured temperature. The parameter calculation unit 63 in this embodiment can set the parameters of the motor model using machine learning.

[0031] The loss calculation unit 54 of the temperature estimation unit 53 calculates the amount of heat generated by the coil 16 and the stator core 20 based on the operation command generated by the operation control unit 43 and the rotational speed detected by the rotational position detector 32. Furthermore, the temperature calculation unit 55 calculates the estimated temperature of the temperature detector based on the amount of heat generated by the coil 16 and the stator core 20.

[0032] The parameter calculation unit 63 calculates the estimated temperature of the temperature detector 31 using the temperature estimation unit 53. The parameter calculation unit 63 includes an evaluation unit 66 that evaluates the estimated temperature of the temperature detector by comparing the estimated temperature of the temperature detector 31 with the measured temperature of the temperature detector 31 obtained by the state acquisition unit 62. The parameter calculation unit 63 includes a parameter modification unit 67 that changes the parameter values ​​based on the evaluation results of the evaluation unit 66.

[0033] Each of the above units—temperature estimation unit 53, loss calculation unit 54, and temperature calculation unit 55—corresponds to a processor that operates according to a program. Each of the units—state acquisition unit 62, parameter calculation unit 63, evaluation unit 66, and parameter modification unit 67—corresponds to a processor that operates according to a program. The processor functions as each unit by performing the control defined in the program.

[0034] Figure 3 shows a first model that models the heat transfer of the first electric motor in this embodiment. The first model of the electric motor is a thermal model that simulates the normal operating state of the electric motor. The thermal model of the electric motor includes models of multiple components. The thermal model includes parameters such as the heat capacity of the components and coefficients related to heat transfer between the components. The first model 10a of the electric motor in this embodiment includes models of the main components that make up the first electric motor 10. Model 10a of the electric motor includes a rotor model 11a, a stator core model 20a, and a coil model 16a wound around the stator core. Model 10a of the electric motor also includes a temperature sensor model 31a for detecting the temperature of the coil 16.

[0035] Referring to Figure 2, an air layer is interposed between the rotor 11 and the stator core 20. Furthermore, an air layer is interposed between the rotor 11 and the coil 16. Model 10a of the electric motor in this embodiment includes model 35a of the air layer. Also, model 10a of the electric motor includes model 36a of the ambient air as a model of the air around the electric motor 10. Thus, in the model of the electric motor in this embodiment, the air layer and ambient air are generated as models of the components of the electric motor.

[0036] The temperature detected by the temperature sensor 31 is approximately equal to the temperature of the coil 16. However, under certain conditions, due to the small heat capacity of the temperature sensor 31, the temperature detected by the temperature sensor 31 may differ from the temperature of the coil 16. For this reason, in this embodiment, a model 31a of the temperature sensor 31 is generated as one of the component models. Alternatively, the heat capacity of the temperature sensor 31 may be set to zero, and the calculation may be performed assuming that the temperature of the temperature sensor model is the same as the temperature of the component model to which the temperature sensor is attached.

[0037] In the electric motor model 10a, several parameters are set, including coefficients related to heat capacity and heat transfer. At least one component model has a set heat capacity. The coil model 16a, the stator core model 20a, the air layer model 35a, the rotor model 11a, and the temperature sensor model 31a each have variables for temperature T1, T2, T3, T4, T5 and constants for heat capacity C1, C2, C3, C4, C5. Additionally, the ambient air model 36a has a variable for temperature T r It is set.

[0038] Heat from one component of the electric motor 10 is transferred to other components. A coefficient relating to heat transfer is set between the models of each component of the electric motor 10. As the coefficient relating to heat transfer, a heat transfer coefficient or a coefficient obtained by multiplying the heat transfer coefficient by the contact area between the components can be used. In this example, a coefficient obtained by multiplying the heat transfer coefficient by the contact area is defined.

[0039] A heat transfer coefficient ha is set between the stator core model 20a and the coil model 16a. A heat transfer coefficient hc1 is set between the air layer model 35a and the coil model 16a. A heat transfer coefficient hc2 is set between the air layer model 35a and the stator core model 20a. A heat transfer coefficient hc3 is set between the air layer model 35a and the rotor model 11a. A heat transfer coefficient hd is set between the coil model 16a and the temperature sensor model 31a. Furthermore, to simulate the release of heat from the stator core 20 to the outside air, a heat transfer coefficient hb is set between the stator core model 20a and the outside air model 36a.

[0040] In the electric motor model 10a of this embodiment, the heat generated by the components is the primary copper loss P generated in the coil 16 of the stator 12. c1 This is taken into consideration. The coil model 16a is input with the amount of heat generated due to primary copper loss. In addition, the iron loss P of the stator core 20 caused by the magnetic force of the magnet 18 of the rotor 11 is taken into consideration. i This is taken into consideration. The stator core model 20a is input with the amount of heat generated due to iron loss.

[0041] Heat transfers between components such as the coil and stator core, depending on the magnitude of the heat transfer coefficient. Furthermore, the temperature of each component rises or falls based on the difference between the heat input and heat output. The temperature change rates of each component of the first model 10a of the first electric motor shown in Figure 3 can be expressed by the following equations (1) to (5). The temperature change rate for each component can be calculated by dividing the difference between the heat input and heat output by the heat capacity.

[0042]

number

[0043] The heat capacities C1, C2, C3, C4, and C5 of the components are constants and can be determined in advance. The coefficients ha, hb, hc1, hc2, hc3, and hd related to heat transfer are coefficients obtained by multiplying the heat transfer coefficient by the contact area. The coefficients ha, hb, hc1, hc2, hc3, and hd are constants and can be determined in advance. The loss calculation unit 54 of the temperature estimation unit 53 calculates the primary copper loss P c1 in the coil 16 and the iron loss P i in the stator core as described later. The temperature calculation unit 55 of the temperature estimation unit 53 can calculate the amount of change in temperature in the minute time dt based on the above equations (1) to (5).

[0044] Next, the calculation methods for the primary copper loss P c1 and the iron loss P i included in the equations (1) and (2) will be described. The rotational speed of the motor 10 and the load factor (ratio to the maximum load) of the motor 10 can be preset by the operator according to the work performed by the machine. The loss calculation unit 54 of the temperature estimation unit 53 calculates the primary copper loss P c1 and the iron loss P i . Table 1 shows a loss map for calculating the losses.

[0045]

Table 1

[0046] Table 1 shows the loss at maximum output with respect to the rotational speed (number of revolutions) of the motor 10, the loss at no load, and the current at maximum output. The loss P m at maximum output is the loss when the load factor of the motor is 100% and is a value determined by the rotational speed of the motor. The loss P n at no load is the loss when the load factor of the motor is zero and depends on the rotational speed of the motor. The current I m at maximum output is the current value when the load factor is 100% at each rotational speed. The loss map shown in Table 1 can be created by actually driving the motor. This loss map can be stored, for example, in the storage unit 51 of the monitoring device 2.

[0047] The loss calculation unit 54 calculates the primary copper loss P c1 and iron loss P i Total loss P including t Calculate the total loss P. t This can be calculated using the following equations (6) and (7).

[0048]

number

[0049] Total loss P t The loss P at maximum output is m , loss P under no load n , and the motor load factor LF can be used to calculate the loss P at maximum output. Since the motor rotation speed and load factor are fixed, Table 1 shows that m and loss P under no load n The following is calculated. The constants k1 and k2 can be predetermined by the operator. Next, the primary copper loss P c1 This can be calculated using the following equations (8) and (9).

[0050]

number

[0051] Primary copper loss P c1 This corresponds to the Joule heat generated by the current flowing through coil 16. Furthermore, the current I flowing through coil 16 is the current I at maximum output. m It can be calculated by multiplying by the motor's load factor LF. The current I at maximum output m This can be obtained from Table 1. Here, the primary resistance r1 of coil 16 has been measured in advance. Next, iron loss P i This can be calculated using the following formula (10): Iron loss P i This is the total loss P t From primary copper loss P c1 It can be calculated by subtracting [a certain value].

[0052]

number

[0053] The temperature estimation unit 53 acquires the motor operation pattern, including the rotational speed and load factor for driving the machine 1, from the state acquisition unit 62. The temperature calculation unit 55 of the temperature estimation unit 53 can initially set the temperatures T1 to T5 of each component to any temperature. For example, the temperature calculation unit 55 sets the temperatures T1 to T5 of the components to the normal ambient temperature T r Set to the outside temperature T. r This can be predetermined depending on the location where machine 1 is to be placed.

[0054] The loss calculation unit 54 of the temperature estimation unit 53 calculates primary copper loss and iron loss based on the rotational speed and motor load factor in the operating pattern. Next, the temperature calculation unit 55 can calculate the change in temperature T5 of the temperature detector 31 in a small time interval dt by solving the above equations (1) to (5). In this way, the operator can determine the motor operating pattern and estimate the change in the temperature of the temperature detector over time when the motor is operated according to the operating pattern.

[0055] By the way, in the electric motor model 10a of this embodiment, it is sufficient if the temperature of one of the multiple components of the electric motor can be estimated with accuracy. The temperatures of the other components do not need to be accurate. In this example, it is sufficient if the temperature T5 of the temperature sensor model 31a can be estimated with accuracy. The temperature T1 of the coil model 16a, the temperature T2 of the stator core model 20a, the temperature T3 of the air layer model 35a, and the temperature T4 of the rotor model 11a do not need to be accurate.

[0056] Furthermore, the heat capacities C1 to C5 set in the electric motor model 10a, and the heat transfer coefficients ha, hb, hc1, hc2, hc3, hd set between the components, have unique values ​​depending on the material, shape, and arrangement of the components. However, in the electric motor model 10a in this embodiment, at least some of the parameters among the multiple heat capacities and multiple heat transfer coefficients may be set to values ​​that deviate from the actual heat capacities or actual heat transfer coefficients.

[0057] Each parameter is set so that the change in temperature T5 of the temperature detector model 31a corresponds to the change in the actual temperature. For example, even if the temperatures of the coil and stator core are far from the actual temperature, the parameters of the motor model can be set so that the temperature of the temperature detector shows a value close to the actual temperature. Furthermore, it is acceptable if, as a result of calculating the coefficients related to heat capacity and heat transfer, the heat capacity and heat transfer coefficients of all components correspond accurately to the actual heat capacity and heat transfer coefficients. And, when the temperature estimation unit estimates the temperature of the components, it is acceptable if the temperatures of all components correspond accurately to the actual temperatures of the components.

[0058] The motor monitoring device 2 of this embodiment is configured to switch between a normal model creation mode, which calculates the parameters of a first model of the motor when the motor is operating normally, and an abnormal model creation mode, which generates a second model of the motor when the motor is operating abnormally. In the normal model creation mode, parameters including coefficients related to heat transfer and the heat capacity of the components are set in the first model 10a of the motor.

[0059] Referring to Figure 1, the parameter calculation unit 63 of this embodiment generates a first model of the electric motor in normal model creation mode. The parameter calculation unit 63 sets the heat capacity, heat transfer coefficients, and constants k1 and k2 in equations (6) and (7) included in the model 10a of the electric motor. The operator actually drives the electric motor 10 according to a predetermined operating pattern. The state acquisition unit 62 acquires the load factor of the electric motor 10, the rotational speed of the electric motor 10, and the temperature output from the temperature detector 31 as the state of the electric motor 10. Furthermore, the state acquisition unit 62 acquires the ambient temperature from the ambient temperature detector 33.

[0060] Figure 4 shows a graph of the operating pattern when driving the motor to set the parameters included in the first model of the motor in this embodiment. Figure 4 shows the operating pattern under no load. In this operating pattern, the rotational speed of the motor 10 is gradually increased without applying a load to the motor 10. The rotational speed of the motor 10 is increased by temporarily increasing the load factor of the motor at predetermined time intervals.

[0061] The temperature detected by the temperature sensor 31 is gradually increasing. Between times t1 and t7, the rotational speed of the motor 10 is increased by temporarily increasing the load factor of the motor 10. The state acquisition unit 62 acquires the operating state of the motor 10 and the temperature output from the temperature sensor 31 during the period when the rotational speed of the motor 10 is gradually increasing. More specifically, the state acquisition unit 62 acquires the load factor of the motor 10, the rotational speed of the motor 10, and the temperature output from the temperature sensor 31 at predetermined minute intervals and stores them in the storage unit 51. In this embodiment, a constant ambient temperature is used, but the system is not limited to this configuration. The state acquisition unit 62 may also acquire the ambient temperature from the ambient temperature sensor 33 at minute intervals.

[0062] Referring to Figure 1, the state acquisition unit 62 acquires the torque command included in the operation command generated by the operation control unit 43 of the machine control device 41. Since the torque command corresponds to the load factor of the electric motor 10, the state acquisition unit 62 can calculate the load factor from the torque command.

[0063] The parameter calculation unit 63 calculates the parameters of the electric motor model 10a based on the variables acquired by the state acquisition unit 62. In this embodiment, the parameter calculation unit 63 calculates parameters including heat capacities C1, C2, C3, C4, C5 and heat transfer coefficients ha, hb, hc1, hc2, hc3, hd based on the amount of heat generated in the coil 16 and stator core 20 and the temperature detected by the temperature detector 31. The parameter calculation unit 63 also calculates the constants k1 and k2 in equations (6) and (7) as parameters. The parameter calculation unit 63 calculates the parameters so that the change in the estimated temperature of the temperature detector model 31a during the simulation approaches the change in the actual measured temperature.

[0064] The parameter calculation unit 63 sets the initial values ​​for each parameter. The initial values ​​of the parameters can be set by any method. The parameter calculation unit 63 uses the loss calculation unit 54 to calculate the amount of heat generated due to the primary copper loss of the coil 16 and the amount of heat generated due to the iron loss of the stator core 20. Based on the rotational speed of the motor 10 and the load factor of the motor 10 acquired by the state acquisition unit 62, the loss calculation unit 54 uses Table 1 and equations (6) to (10) to calculate the primary copper loss P c1 and iron loss P i Calculate.

[0065] Primary copper loss P c1 and iron loss P i Equations (6) and (7) used to calculate the primary copper loss P include constants k1 and k2. Furthermore, the loss calculation unit 54 calculates the loss in a predetermined minute time dt, i.e., the amount of heat generated in that minute time. In this way, the loss calculation unit 54 calculates the primary copper loss P in equations (1) and (2) based on measured values ​​including the motor operation command (load factor) and the output of the rotation position detector 32. c1and iron loss P i Calculate.

[0066] The parameter calculation unit 63 uses the temperature calculation unit 55 to estimate the temperature of the components. The temperature calculation unit 55 uses the respective parameters and the losses calculated by the loss calculation unit 54 to calculate the estimated temperature of the temperature sensor 31 based on the first model 10a of the electric motor. In other words, the temperature of the model 31a of the temperature sensor is estimated by simulation.

[0067] The temperature calculation unit 55 can calculate the change in estimated temperature over time detected by the temperature detector 31 after the motor 10 has started to operate, based on the parameters that have been set. The model temperature of each component of the motor 10 can be calculated using the differential equations (1) to (5) above. The initial value of the model temperature of each component can be set, for example, to the ambient temperature when the motor 10 is started to operate, i.e., room temperature.

[0068] The evaluation unit 66 of the parameter calculation unit 63 evaluates the parameters provisionally set in the first model 10a of the electric motor by comparing the temperature (estimated temperature) of the model 31a of the temperature detector calculated by the temperature calculation unit 55 with the measured temperature actually measured by the temperature detector 31. In this example, the evaluation unit 66 evaluates only the temperature of the model 31a of the temperature detector, without evaluating any variables other than the temperature of the model 31a of the temperature detector. It is sufficient that the change in the temperature of the model 31a of the temperature detector is close to the actual change in temperature, and the temperatures of at least some of the other components are not evaluated.

[0069] Next, the parameter modification unit 67 of the parameter calculation unit 63 modifies the parameters based on the evaluation results of the evaluation unit 66. Then, based on the modified parameters, the same calculations as above are repeated: the loss calculation unit 54 calculates the loss, the temperature calculation unit 55 calculates the estimated temperature of the temperature detector model, the evaluation unit 66 performs the evaluation, and the parameter modification unit 67 modifies the parameters. When the evaluation by the evaluation unit satisfies predetermined conditions, the final parameters can be determined.

[0070] Here, the number of possible combinations of parameters in Model 10a of the electric motor is very large. These parameters can be determined using machine learning methods. For example, multiple parameters can be set using Bayesian optimization. In Bayesian optimization, an objective function to be evaluated is generated for explanatory variables that include the input parameters. Then, the parameters that are predicted to result in the minimum or maximum of the objective function are searched for and set. By repeating this parameter search, the optimal values ​​for the parameters can be set. Furthermore, the range in which each parameter can be set can be predetermined.

[0071] In this example, the objective function for the temperature of the temperature detector 31 is set to the difference between the temperature of the model 31a of the temperature detector estimated by the model 10a of the electric motor (estimated temperature) and the actual measured temperature detected by the temperature detector 31. That is, the objective function can be the difference between the predicted value calculated from equations (1) to (5) based on the hypothetically set parameters for the temperature of the temperature detector 31 and the actual measured value detected by the temperature detector 31. For example, the average value of the difference over a small time interval can be adopted as the objective function. The parameter changing unit 67 then searches for the next parameters so that the objective function becomes smaller.

[0072] In Bayesian optimization, the process of searching for and evaluating parameters can be repeated. The evaluation unit 66 can adopt the parameter values ​​at the time if the objective function is within a predetermined range. On the other hand, if the objective function deviates from the predetermined range, the next parameter search can be performed. In the Bayesian optimization method, the amount of computation can be reduced because the search is performed while predicting the region in which a solution exists.

[0073] The parameters included in the electric motor model 10a can be set by any method other than parameter setting by Bayesian optimization. For example, the range in which each parameter can be set can be predetermined. The parameter change unit 67 of the parameter calculation unit 63 sets multiple parameters randomly within the parameter range. The temperature calculation unit 55 estimates the temperature of the temperature detector model 31a based on the set parameters. The evaluation unit 66 can evaluate the set parameters based on the measured temperature values ​​obtained from the temperature detector 31. This method of setting parameters is called the random search method.

[0074] Alternatively, the parameter modification unit 67 can set parameters at predetermined intervals within the range in which the parameters are set. The temperature calculation unit 55 estimates the temperature of the temperature detector model 31a using the set parameters. The evaluation unit 66 evaluates all combinations of discretely set parameters. This method is called the grid search method.

[0075] In both the random search method and the grid search method, similar to the Bayesian optimization method, the evaluation unit 66 can evaluate the temperature of the temperature detector 31. The evaluation unit 66 can adopt the parameter values ​​at the time if the objective function is within a predetermined range. Alternatively, the evaluation unit 66 can adopt the parameters that best perform the objective function. The evaluation unit 66 can determine the parameters in the motor model 10a that best match the estimated temperature of the temperature detector 31 to the actual measured temperature detected by the temperature detector 31.

[0076] In this embodiment, parameters are set so that the temperature change detected by the temperature detector 31 can be estimated with high accuracy. In this embodiment, the temperatures of components other than the temperature detector 31 do not need to be different from the actual temperatures, so in the parameter evaluation, only the temperature of the temperature detector that detects the coil temperature can be evaluated. For this reason, parameters can be set in a short time with a small amount of computation. The storage unit 51 can store the first model 10a of the generated electric motor.

[0077] In the above embodiment, the operation pattern for driving the motor 10 to set the parameters of the first model 10a of the motor was shown as no-load operation, but the embodiment is not limited to this. When determining the parameters of the first model 10a of the motor, it is preferable to operate the motor 10 under various operating patterns to obtain the operating state of the motor 10. For example, an operating pattern that repeatedly increases and decreases the load factor of the motor 10 can be adopted. The rotational speed of the motor can be changed by greatly changing the load factor of the motor 10. The temperature detected by the temperature sensor 31 will rise or fall rapidly. In this way, an operating pattern that includes abrupt temperature changes of the motor can be adopted.

[0078] In the above embodiment, a coil including windings was used as an example of a component of the motor for estimating temperature, but the embodiment is not limited to this. Any component of the motor can be used as the component for calculating the estimated temperature. Furthermore, a temperature sensor can be attached to the component for calculating the estimated temperature.

[0079] Furthermore, although the above embodiment describes a synchronous motor with a rotor having permanent magnets, it is not limited to this configuration. The monitoring system in this embodiment can be applied to any motor. For example, the motor model in this embodiment can also be applied to an induction motor that does not have a rotor with permanent magnets.

[0080] Figure 5 shows a model of the second motor in this embodiment. Figure 5 is the first model 30a when the second motor is operating normally. Here, the second motor is an induction motor. The rotor of the induction motor includes a cage-shaped conductor made of stainless steel or copper. The cage-shaped conductor is fixed to the shaft and rotates integrally with the shaft. In an induction motor, an induced current flows inside the cage-shaped conductor due to the magnetic force generated by the coils of the stator. A magnetic field is generated around the cage-shaped conductor, causing the rotor to rotate.

[0081] In induction motors, current flows through the rotor's cage-shaped conductors, resulting in secondary copper losses P as secondary losses. c2 This occurs. The secondary loss corresponds to Joule heating due to the current flowing through the cage-type conductor. In the second motor model 30a, heat is generated in the rotor due to secondary copper loss. The heat capacity of the components of the second motor and the coefficients for heat transfer between the components are the same as those for the first motor model 10a.

[0082] The differential equation for the temperature change of the components in Model 30a of the second electric motor differs from that of Model 10a of the first electric motor in that the differential equation for calculating the temperature change of the rotor is given by equation (11) below.

[0083]

number

[0084] In equation (11), the secondary copper loss P is added to equation (4) of model 11a of the rotor of the first motor. c2 The heat generated is added. The differential equations representing the temperature changes of the other coils, stator core, air layer, and temperature detector are the same as those in the thermal model of the first electric motor.

[0085] The loss calculation unit 54 calculates the amount of heat generated due to secondary copper loss in the rotor conductors. The loss calculation unit 54 estimates the current flowing through the cage-type conductor. The loss calculation unit 54 can calculate the secondary copper loss from the current flowing through the conductor, the secondary resistance of the conductor, the inductance of the conductor, and the mutual inductance between the conductor, the stator, and the coil. The inductance of the conductor, the mutual inductance, and the secondary resistance of the conductor can be predetermined.

[0086] Total loss P in an induction motor t and primary copper loss P c1 This can be calculated in the same way as the total loss and primary copper loss in a synchronous motor. And the iron loss P i Secondary copper loss P c2 Taking this into consideration, it can be calculated using the following formula (12).

[0087]

number

[0088] In this way, primary copper loss, iron loss, and secondary copper loss are calculated in the first model 30a of the second motor. The parameter calculation unit 63 can calculate the parameters included in the first model 30a of the second motor when the motor is operating normally, using the same control as the first model 10a of the first motor. The temperature estimation unit 53 can then use the first model 30a of the second motor to calculate the estimated temperature of the components when the motor is operating normally.

[0089] Figure 6 shows a graph of the estimated temperature of the temperature detector, estimated by the temperature estimation unit using the parameters calculated by the parameter calculation unit of this embodiment. Here, an example of a second electric motor is shown. Figure 6 shows graphs when simulations are performed with parameter groups A and B, which have different values ​​from each other. Parameter groups A and B are calculated by the parameter calculation unit 63. The parameters included in parameter groups A and B are shown in Table 2.

[0090] [Table 2]

[0091] Parameter groups A and B were obtained by driving the second electric motor with different operating patterns. Table 2 shows the coefficients related to heat transfer, which are obtained by multiplying the heat transfer coefficient between each component of the electric motor by the contact area. The heat capacity is calculated by multiplying the specific heat of the material of each component by its mass. Since the specific heat of each material can be predetermined, Table 2 shows the mass m of the component used to calculate the heat capacity. Comparing parameter groups A and B, it can be seen that some parameters, such as the heat transfer coefficients hc2, hd and the rotor mass m4, have significantly different values ​​between the two parameter groups A and B.

[0092] On the other hand, referring to Figure 6, it can be seen that the estimated temperature of the temperature detector calculated using parameter group B is in good agreement with the estimated temperature of the temperature detector calculated using parameter group A. In particular, the temperature changes are in good agreement both during the period when the temperature is rising and during the period when the temperature is fluctuating within a predetermined range. Furthermore, the temperature changes shown in Figure 6, estimated by the temperature estimation unit 53, are in good agreement with the temperature changes detected by the temperature detector 31 when the electric motor 10 is actually driven.

[0093] There are parameters whose values ​​differ significantly between parameter group A and parameter group B. Therefore, it can be seen that at least one of the parameter groups, A or B, has different values ​​from the parameter groups in an actual electric motor. In particular, it can be seen that at least some of the parameters, including the heat capacity and heat transfer coefficients, are set to values ​​different from the actual heat capacity or heat transfer coefficients. For example, it can be seen that at least one of the heat transfer coefficients, hc2 in parameter group A and hc2 in parameter group B, deviates from the actual heat transfer coefficient.

[0094] Thus, in the motor monitoring device of this embodiment, the temperature of the temperature sensor can be estimated with high accuracy even if at least some of the multiple parameters differ from the actual values. Furthermore, the parameter calculation unit of this embodiment can set the parameters of such a motor model.

[0095] In this embodiment, one temperature sensor is attached to the motor, but the system is not limited to this configuration. Multiple temperature sensors may be attached to multiple components of the motor. The evaluation unit of the parameter calculation unit can compare the measured temperatures of the multiple temperature sensors with the estimated temperatures obtained through simulation. The parameter modification unit can set the parameters of the first model of the motor so that the estimated temperatures of the multiple components are close to the measured temperatures detected by the actual temperature sensors.

[0096] The more temperature sensors attached to an electric motor, the closer the values ​​of each of the parameters in the first model of the motor can be to the actual values. Furthermore, the estimated temperature of each component of the motor can be closer to the actual measured temperature. As a result of attaching multiple temperature sensors, it is acceptable for all heat capacity and heat transfer coefficients to be approximately identical to the actual heat capacity and heat transfer coefficients. In this case, when the temperature estimation unit estimates the temperature of the components, the temperatures of all components will correspond accurately to the actual temperatures of the components.

[0097] Next, we will describe the abnormality model creation mode for generating a second model of the motor when its operating state is abnormal. Furthermore, the motor monitoring device 2 of this embodiment provides the operator or other devices with information to estimate the part where the abnormality is occurring, based on the second model of the motor.

[0098] Referring to Figure 1, the motor monitoring device 2 of this embodiment includes a model search unit 71 that generates a second model of the motor when the motor's operating state is abnormal. The model search unit 71 includes a temperature determination unit 72 that determines whether the difference between the temperature measured by the temperature detector 31 and the estimated temperature of the temperature detector 31 based on the first model is within a predetermined determination range.

[0099] The model search unit 71 includes a component addition unit 73 that generates a second model by adding a model of the motor components and a model of an additional component that performs heat exchange to the first model when the difference between the measured temperature of the temperature detector 31 and the estimated temperature of the temperature detector 31 based on the first model deviates from a predetermined judgment range. In other words, the component addition unit 73 adds a model of an additional component to the model for when the motor is operating normally when the difference between the estimated temperature and the measured temperature is large.

[0100] The model search unit 71 includes a setting unit 74 that sets the model of the additional member and the model of the components of the electric motor that perform heat exchange. The setting unit 74 also sets the heat capacity of the additional member and a coefficient related to the heat transfer of the additional member. The coefficient related to the heat transfer of the additional member includes a coefficient related to the heat transfer between the model of the components of the electric motor that perform heat exchange and the additional member. Furthermore, the monitoring device 2 in this embodiment includes an abnormality location estimation unit 75 that estimates abnormalities occurring in or around the electric motor 10. The monitoring device 2 includes a notification unit 76 that notifies other devices of information regarding the second model of the electric motor.

[0101] Each of the above-mentioned units—the model search unit 71, the temperature determination unit 72, the component addition unit 73, and the setting unit 74—corresponds to processors that operate according to a pre-created program. Similarly, the anomaly location estimation unit 75 and the notification unit 76 also correspond to processors that operate according to a pre-created program. Each unit functions by reading the program and executing the control defined within it.

[0102] The following explanation will focus on the first motor shown in Figure 2 and the first model of the first motor shown in Figure 3, but the system is not limited to these configurations. The second motor and the model of the second motor shown in Figure 5 can also be controlled in the same way as the first motor. Furthermore, similar to the normal model creation mode, the explanation will use the temperature detected by the temperature sensor 31 attached to the coil 16 as an example.

[0103] When an abnormality occurs in an electric motor, the motor's temperature may rise. In this embodiment, if the temperature measured by a temperature sensor attached to the motor deviates from a predetermined judgment range based on the estimated temperature when the motor is functioning normally, it is determined that an abnormality has occurred in the motor or around the motor. In other words, it is determined that the motor's operating state is abnormal.

[0104] Figure 7 shows a graph illustrating the temperature change when the first electric motor is operating normally. Referring to Figures 1 and 7, the vertical axis of the graph represents the temperature of the temperature sensor 31 attached to the coil 16. The horizontal axis represents elapsed time. The solid line shows the measured temperature actually detected by the temperature sensor 31, that is, the measured temperature acquired by the state acquisition unit 62.

[0105] In this operating example, power is supplied at time t0, and the load on the motor 10 increases rapidly. As the current increases, the temperature of the temperature sensor 31 increases rapidly. At time t1, the current supplied to the motor 10 is reduced. The rotational speed of the motor 10 remains almost constant under no load. After time t1, the temperature of the temperature sensor 31 gradually decreases over time.

[0106] Figure 7 shows the estimated temperature of the temperature detector 31, estimated using the first model 10a of the first electric motor, as indicated by the dashed line. The first model 10a of the electric motor is pre-generated in normal model creation mode. When the electric motor is operating normally, it can be seen that the estimated temperature of the temperature detector 31, estimated using the first model 10a of the electric motor, agrees well with the measured temperature detected by the temperature detector 31.

[0107] Figure 8 shows a graph of temperature change when an abnormality occurs in the operating state of the first motor. The operating conditions in Figure 8 are the same as those in Figure 7. Referring to Figures 1, 2, and 8, the load is increased from time t0 to time t1. At time t1, the load is reduced to zero. In the interval from time t0 to time t2, the motor is operating normally. The measured temperature detected by the temperature sensor 31 corresponds well to the estimated temperature calculated by the first model 10a of the motor.

[0108] However, in the interval EP from time t2 to time t3, an abnormality occurs in the motor 10, causing the measured temperature to temporarily rise. This example shows a temporary deterioration in the lubrication state of the bearings 14 and 15 of the motor 10. As a result of the deteriorated lubrication of the bearings 14 and 15, the bearings 14 and 15 overheat, causing their temperature to rise. The temperature of the temperature sensor 31 attached to the coil 16 also rises. After time t3, the motor returns to a normal operating state.

[0109] The model search unit 71 of the monitoring device 2 detects that the operating state of the motor 10 is abnormal in section EP. The model search unit 71 generates a second model of the motor when the operating state of the motor 10 is abnormal. The model search unit 71 creates the second model by adding additional components to the first model. Then, in section EP, the model search unit 71 sets the model of the motor components that exchange heat with the additional components and the parameters of the second model so as to correspond well to the actual changes in the measured temperature.

[0110] Next, the monitoring device 2 displays information about the second model of the electric motor on its display unit and notifies the operator. The operator can look at the information about the second model of the electric motor and estimate the location where the malfunction is occurring in the electric motor. Alternatively, the malfunction location estimation unit 75 estimates the location where the malfunction is occurring based on the second model of the electric motor.

[0111] During the period when the electric motor is running, the status acquisition unit 62 of the monitoring device 2 acquires the operating status of the electric motor at predetermined time intervals. The temperature estimation unit 53 calculates the estimated temperature of each component of the electric motor based on the operating status acquired by the status acquisition unit 62 and the first model of the electric motor. In particular, in this embodiment, the estimated temperature of the temperature detector 31 is calculated at predetermined time intervals.

[0112] The state acquisition unit 62 acquires the measured temperature output from the temperature detector 31 at predetermined time intervals. The storage unit 51 stores the time, the estimated temperature of the temperature detector 31 calculated using the first model, and the measured temperature of the temperature detector 31. This sampling of the operating state can be performed, for example, at intervals of 1 second.

[0113] The temperature determination unit 72 determines whether the difference between the estimated temperature of the temperature detector 31 and the measured temperature of the temperature detector 31 deviates from a predetermined determination range. For example, the temperature determination unit 72 can calculate a moving average of the difference between the measured temperature and the moving temperature. For example, the temperature determination unit 72 can calculate a moving average over the past minute. When the moving average deviates from a predetermined determination range, it can determine that the difference between the measured temperature and the determination temperature has deviated from the determination range. The temperature determination unit 72 can determine that an abnormality has occurred at the time in the middle of the moving average interval.

[0114] The temperature determination unit can determine, by arbitrary control, whether the difference between the measured temperature and the estimated temperature exceeds the determination range. For example, the temperature determination unit determines whether the absolute value of the difference between the measured temperature and the estimated temperature is greater than a predetermined determination value. If this absolute value is greater than the determination value, it can be determined that the difference between the measured temperature and the actual temperature exceeds the determination range.

[0115] Referring to Figure 8, the temperature determination unit 72 determines that in the interval EP from time t2 to time t3, the difference between the measured temperature and the estimated temperature exceeds the determination range. That is, the temperature determination unit 72 determines that the measured temperature is higher than the determination value based on the estimated temperature. The temperature determination unit 72 can identify the start time and end time of the interval EP in which the measured temperature of the temperature detector 31 is abnormal.

[0116] Figure 9 shows an example of a second model of the electric motor in this embodiment. Referring to Figures 1, 3, 8, and 9, the model search unit 71 generates a second model 10b of the first electric motor as shown in Figure 9. When a period EP occurs in which the electric motor temperature is abnormal, the component addition unit 73 of the model search unit 71 adds a model 81a of an additional component X to the first model 10a of the first electric motor shown in Figure 3. The component addition unit 73 generates the second model 10b by adding a model 81a of an additional component X that exchanges heat with the model of the electric motor components.

[0117] The setting unit 74 of the model search unit 71 selects a model of the motor component that will exchange heat with the additional member X. In the example shown in Figure 9, priority is set for the components of the first model of the motor. The coil is assigned the highest priority. The setting unit 74 sets the coil model 16a as the model of the component that will exchange heat with the additional member X. The setting unit 74 modifies the formula for calculating the temperature of the motor component. The setting unit 74 sets the temperature T for the additional member X. X and heat capacity C X The setting unit 74 can determine the heat transfer coefficient hx1 between the coil model 16a and the additional member X model 81a. In this example, the setting unit 74 determines the amount of heat generated by the additional member X P X Set it.

[0118] In the second model 10b of the electric motor, the rate of change of the temperature T1 of the coil model 16a is given by the following equation (13). Equation (13) for the rate of change of the temperature of the coil model 16a has the heat transfer from the additional member X added to equation (1). Also, the temperature T of the additional member X X The rate of change of is given by equation (14). In equation (14), the heat generation P X This also includes items concerning heat exchange with the coil model 16a.

[0119]

number

[0120] In section EP, the setting unit 74 sets the heat capacity C in the second model 10b so that the estimated temperature of the model 31a of the temperature detector of the second model 10b corresponds to the measured temperature of the temperature detector 31. X , heat generation P X , and the coefficient hx1 for heat transfer is set. The parameters of the other components defined in the first model 10a are used without modification. In this embodiment, the heat capacity C of the additional member X is set. X , heat generation P X The range of values ​​for the heat transfer coefficient hx1 is predetermined. The setting unit 74 performs a grid search method, changing the parameters at predetermined intervals within the range of each parameter.

[0121] Here, the amount of heat generated P X A constant value independent of the motor load can be adopted. Also, the heat generation P X The parameter may be a fixed value that is not changed within its range. Also, the initial temperature T of the additional member in section EP. X Alternatively, the average temperature of multiple components of the motor that exchange heat with the additional component X can be used. X The temperature of one component of the motor that exchanges heat with the additional components may be used.

[0122] Furthermore, the additional component X may be maintained at a constant temperature. That is, the temperature T of the additional component X. X A predetermined temperature is set. In this case, equation (14) becomes unnecessary, and the heat capacity C of the additional member X is... X That will also become unnecessary.

[0123] The loss calculation unit 54 of the temperature estimation unit 53 obtains the operating state of the motor in section EP from the storage unit 51 and calculates the loss. The temperature calculation unit 55 of the temperature estimation unit 53 calculates the estimated temperature of the temperature detector model 31a in section EP using the second model 10b. For parameters other than those related to the additional member X, the same values ​​as the parameters of the first model 10a are used.

[0124] The temperature determination unit 72 of the model search unit 71 obtains the measured temperature of the temperature detector 31 in interval EP from the storage unit 51. The temperature determination unit 72 calculates the difference between the measured temperature and the estimated temperature of the temperature detector 31. Here, the moving average value over a predetermined time length is calculated as the difference between the measured temperature and the estimated temperature of the temperature detector 31. The temperature determination unit 72 can calculate the difference between the measured temperature and the estimated temperature of the temperature detector 31 for each sampling time interval. The storage unit 51 stores the values ​​of parameters such as the components of the motor in which the additional member X performs heat exchange, the heat capacity of the second model 10b, and the difference between the measured temperature and the estimated temperature of the temperature detector 31.

[0125] Next, the setting unit 74 changes the parameters related to the additional member X within a predetermined range of parameters and repeats the same calculation and storage of the calculation results. The setting unit 74 gradually changes the coefficient hx1 and heat capacity C related to heat transfer at predetermined intervals. X , and heat generation P X The parameters are changed. The model search unit 71 calculates the difference between the measured temperature and the estimated temperature while gradually changing the parameters included in the second model. The model search unit 71 performs calculations for all combinations of parameters. The storage unit 51 stores the difference between the measured temperature and the actual temperature for each combination of parameters.

[0126] Next, the setting unit 74 modifies one of the components of the electric motor that the additional member X heat exchanges with. For example, the setting unit 74 sets the model 35a of the air layer to be the component that heat exchanges with the additional member X. The component of the electric motor that the additional member X heat exchanges with can be determined, for example, based on a predetermined priority order. Then, the setting unit 74 and the temperature determination unit 72 calculate the difference between the measured temperature of the temperature detector 31 and the estimated temperature in section EP while changing parameters such as the heat capacity of the second model 10b. The storage unit 51 stores the calculation result.

[0127] In this way, the setting unit 74 generates a second model and performs calculations so that the additional member X exchanges heat with each component of the electric motor. The setting unit 74 performs calculations so that for all components, the additional member X exchanges heat with one component. In each second model, the difference between the measured temperature and the estimated temperature of the temperature detector 31 in section EP is calculated.

[0128] Next, the model search unit 71 specifies two components for the motor in which the additional member X performs heat exchange, increasing the number of components by one, and repeats the same calculation and storage of the calculation results. The model search unit 71 repeatedly calculates the difference between the measured temperature and the estimated temperature of the temperature detector 31 in section EP when there are two components for the motor that perform heat exchange, and stores the calculation results. The model search unit 71 repeats the calculation and storage for all combinations of the two components. Next, the model search unit 71 specifies three components for the motor in which the additional member X performs heat exchange, increasing the number of components by one, and repeats the same calculation and storage of the calculation results.

[0129] In this way, the model search unit 71 repeatedly calculates and stores the number of motor components on which the additional component X performs heat exchange, one by one. It performs calculations for all combinations of components on which the additional component X can perform heat exchange. In this example, the calculation and storage are repeated until the additional component X performs heat exchange simultaneously with all components of the motor.

[0130] After all calculations are complete, the setting unit 74 of the model search unit 71 selects a second model from among the multiple calculation results recorded in the storage unit 51 that has the smallest difference between the temperature measured by the temperature detector 31 and the estimated temperature. For example, in interval EP, the second model is selected that has the smallest mean square difference between the temperature measured by the temperature detector 31 and the estimated temperature. For this second model, the model search unit 71 obtains parameters from the storage unit 51, including the components of the motor in which the additional member X performs heat exchange, the heat capacity of the additional member X, coefficients related to heat transfer, and the amount of heat generated by the additional member X.

[0131] Figure 10 shows the second model searched by the model search unit when a bearing malfunction occurs. Among the multiple second models searched by the model search unit 71, the second model 10c is shown, which has the smallest difference between the measured temperature and the estimated temperature of the temperature detector 31. For example, Figure 8 shows the temperature change of the temperature detector when a bearing malfunction occurs. Referring to Figure 2, when a bearing malfunction occurs, the temperature of bearings 14 and 15 rises.

[0132] The bearings 14 and 15 are close to the rotor 11, the coil 16, and the air layer between the rotor 11 and the coil 16. The heat generated in the bearings 14 and 15 is easily transferred to the rotor 11, the coil 16, and the air layer between the rotor 11 and the coil 16. For this reason, when the model search unit 71 searches for a second model, a second model 10c is generated such that the heat-generating additional member X exchanges heat with the coil, the rotor, and the air layer between the coil and the rotor. That is, the models of the motor components for which the model 81a of the additional member X exchanges heat are the model 16a of the coil, the model 35a of the air layer, and the model 11a of the rotor.

[0133] In the second model 10c of the electric motor, the rate of change of temperature T1 of the coil model 16a is given by equation (13) above. The rate of change of temperature T3 of the air layer model 35a is given by the following equation (15). The rate of change of temperature T4 of the rotor model 11a is given by the following equation (16). Also, the temperature T of the additional member X X The rate of change is given by equation (17).

[0134]

number

[0135] The setting unit 74 sets the parameters that minimize the difference between the temperature measured by the temperature detector and the estimated temperature in the section EP where the abnormality occurs. In this example, the setting unit 74 sets the coefficient hx1 for heat transfer between the model 81a of the additional member X and the model 16a of the coil, the coefficient hx2 for heat transfer between the model 81a of the additional member X and the model 35a of the air layer, and the coefficient hx3 for heat transfer between the model 81a of the additional member X and the model 11a of the rotor. The setting unit 74 also sets the heat capacity C of the additional member X. X and heat generation P X This is how it is set. In this way, the parameter values ​​for the additional component X are set.

[0136] The display unit 52 of the monitoring device 2 then displays information about the second model 10c created by the model search unit 71. Here, the display unit 52 displays information about the second model 10c that best corresponds to the measured temperature based on the estimated temperature of the temperature detector. The display unit 52 displays the model of the motor component that performs heat exchange with the model 81a of the additional component, and the heat capacity C of the model 81a of the additional component. X , coefficients related to heat transfer hx1, hx2, hx3, and heat generation amount P X Display at least one piece of information from among them.

[0137] Based on the information from the second model, the operator can estimate the location and cause of the anomaly. Based on the components of the motor that exchange heat with the additional component X, the operator can estimate the part that is generating heat. It can be estimated that the part that is generating heat is close to the components of the motor that exchange heat with the additional component X. In addition, the operator can estimate the distance between the components of the motor and the additional component X based on the heat transfer coefficient. For example, if the heat transfer coefficient is large, it can be estimated that the distance between the part that is generating heat and the components of the motor that exchange heat is short.

[0138] Furthermore, the worker can estimate the material and size of the additional component based on its heat capacity. For example, if the heat capacity is large, it can be estimated that the component is made of metal or is a component with a large mass. In addition, the cause of the heat generation can be estimated based on the amount of heat generated by the additional component. For example, if the amount of heat generated is within a predetermined range, it can be estimated that the cause is poor lubrication of the bearing.

[0139] In this way, the operator can estimate the location and cause of the motor malfunction based on the information from the second model searched by the model search unit. The operator can identify the malfunction before the motor fails. The operator can repair or replace the motor before it fails. Alternatively, it becomes possible to analyze countermeasures to take before the motor fails. For example, the machine load can be reduced until the motor is replaced, delaying the timing of the motor failure.

[0140] In the above embodiment, the model search unit 71 performs calculations for all combinations of additional members and components of the electric motor. The model search unit 71 also modifies all parameters of the second model within a predetermined range. It then selects the second model that minimizes the difference between the temperature measured by the temperature detector and the estimated temperature, but the embodiment is not limited to this configuration.

[0141] For example, an acceptable range for the difference between the temperature sensor's estimated temperature and the measured temperature can be predetermined. The setting unit may then terminate the search for the second model when the difference between the temperature sensor's estimated temperature and the measured temperature is within the acceptable range. The setting unit can then adopt the additional components, the components of the motor that perform heat exchange, and the parameters of the second model at that time.

[0142] Alternatively, the memory unit can store a second model that has been generated in the past. Based on past results, the priority order of the motor components for which additional members exchange heat, and the priority order of the parameter values ​​of the second model may be predetermined. The setting unit can perform the search in the order of priority. The search for the second model may be terminated when the difference between the estimated temperature of the temperature sensor and the measured temperature is within an acceptable range.

[0143] Furthermore, in this embodiment, information regarding the second model is displayed on the display unit after generating a second model for when the motor is abnormal, but the embodiment is not limited to this. The monitoring device 2 of this embodiment includes a notification unit 76 that notifies other devices of information regarding the second model. For example, the notification unit 76 can notify other devices of at least one piece of information from among the model of the additional member, the model of the motor component that performs heat exchange, the heat capacity of the additional member, and the coefficient related to heat transfer of the additional member.

[0144] Other devices can perform arbitrary controls upon receiving notifications from the notification unit 76. For example, the machine control device 41 can obtain information about the second model from the monitoring device 2. The machine control device 41 can then change the operating state of machine 1. For example, the machine control device 41 can reduce the speed at which the electric motor drives, or change the rate of increase and decrease of the speed.

[0145] Referring to Figure 1, the monitoring device 2 of this embodiment includes an abnormality location estimation unit 75. The abnormality location estimation unit 75 may estimate the location of the abnormality in the electric motor based on the information of the second model generated by the model search unit 71. The model search unit 71 of the monitoring device 2 creates a second model of the electric motor each time an abnormality occurs in the electric motor. The operator then inputs the location and cause of the abnormality into the monitoring device 2. The storage unit 51 stores the location and cause of the abnormality along with the second model of the electric motor. The storage unit 51 stores second models of electric motors that have been generated in the past, as well as the location and cause of the abnormality input by the operator.

[0146] The abnormality location estimation unit 75 estimates the location and cause of abnormalities in the currently generated second model of the electric motor based on the location and cause of abnormalities in previously generated second models of electric motors. For example, the abnormality location estimation unit 75 selects a second model from among previously acquired second models in which the additional components and the components of the electric motor that perform heat exchange are the same as those in the currently generated second model. Then, the abnormality location estimation unit 75 selects a second model with similar parameters (coefficients related to heat transfer, heat capacity, and heat generation). A judgment range can be set for each parameter. The abnormality location estimation unit 75 can determine whether the parameters are close to the same value based on the judgment range. The abnormality location estimation unit 75 may select multiple second models. The display unit 52 can display the previously selected second models, location and cause of abnormalities, as determined by the abnormality location estimation unit 75.

[0147] The abnormality location estimation unit can estimate the location and cause of an abnormality in the motor by any method. For example, the abnormality location estimation unit can estimate the location and cause of an abnormality in the motor using machine learning. The abnormality location estimation unit generates a learning model that estimates the location and cause of an abnormality in the motor based on a second model of past motors. For example, the abnormality location estimation unit can perform supervised learning. The abnormality location estimation unit generates a learning model using the second model of past motors and the locations and causes of abnormalities as training data. Using the learning model, the abnormality location estimation unit can estimate the location and cause of an abnormality from the second model of the motor generated in the current case. The display unit can display the location and cause of the abnormality in the motor. Furthermore, the location and cause of the abnormality estimated by the abnormality location estimation unit 75 may be transmitted to other devices by the notification unit 76.

[0148] The above embodiment illustrates a case where a malfunction occurs in a component of the electric motor, causing it to overheat. However, a malfunction in an external component of the electric motor can also cause a heat-generating element. For example, abnormal heat generation may occur in a device connected to the electric motor or in a device close to the electric motor. When such abnormal heat generation occurs, heat may be transferred to the electric motor. The transfer of heat from the heat-generating element may cause the temperature of the electric motor to rise.

[0149] Figure 11 shows a second model when abnormal heat generation occurs around the electric motor in this embodiment. The second model 10d, found by the model search unit 71, is shown. The heat-generating element is shown as the additional member Y that generates heat. For example, an electric motor may be included in the spindle head of a machine tool. The electric motor is connected to the spindle that holds the tool. The spindle rotates due to the drive of the electric motor. However, an abnormality may occur in the spindle head, causing the spindle to come into contact with the housing of the spindle head. As a result, the spindle may generate heat, and this heat may be transferred to the electric motor.

[0150] When a heat source is generated around such an electric motor, the temperature determination unit 72 of the model search unit 71 identifies the section EP where the abnormality is occurring. The component addition unit 73 adds the model 82a of the additional component Y to the first model 10a to generate the second model 10d. The model search unit 71 sets the model of the component that exchanges heat with the model 82a of the additional component Y and the parameters of the second model 10d so that the estimated temperature of the temperature detector 31 corresponds to the measured temperature. The model 20a of the stator core is set as the component that exchanges heat with the model 82a of the additional component Y. In addition, the heat capacity C related to the additional component Y Y , heat generation P Y Parameters are set, including a coefficient hy related to heat transfer.

[0151] The operator can estimate the location and cause of the anomaly by referring to the second model 10d and the parameters of the second model 10d. Alternatively, the anomaly location estimation unit can estimate the location and cause of the anomaly. The monitoring device of this embodiment can also generate a second model relating to anomalies occurring outside the electric motor.

[0152] The above embodiment shows a case where a malfunctioning component generates heat, but it is not limited to this embodiment. When a malfunction occurs in the motor or its surroundings, the motor's temperature may decrease. For example, heat-absorbing foreign matter may adhere to the motor from its components.

[0153] If a liquid such as coolant comes into contact with the outer surface of the electric motor 10, a component of the electric motor 10 that is at a lower temperature than the component of the electric motor 10 will come into contact with the stator core 20, causing the temperature of the stator core 20 to decrease. The temperature determination unit 72 detects that the measured temperature detected by the temperature detector 31 is less than a determination value based on the estimated temperature of the temperature detector 31 when the electric motor is operating normally. For example, the temperature determination unit 72 detects that the measured temperature is lower than a predetermined temperature range relative to the estimated temperature.

[0154] The component addition unit 73 generates a second model by adding a model of an additional component with zero heat generation to the first model 10a. The model of the additional component is set to have a heat capacity. The initial temperature of the additional component can be predetermined. Through the search of the second model by the model search unit 71, the model of the additional component is set to perform heat exchange with the stator core model. In addition, the parameters of the second model are set so that the estimated temperature of the temperature detector corresponds well with the measured temperature.

[0155] On the other hand, if a liquid such as machine coolant enters the inside of the electric motor 10, components with a lower temperature than the components of the electric motor come into contact with the coil 16, rotor 11, and stator core 20, causing the temperatures of these components to drop. The component addition unit 73 generates a second model by adding a model of an additional component with zero heat generation to the first model 10a. A predetermined heat capacity is set for the model of the additional component. The initial temperature of the additional component can be predetermined. Through the search of the second model, the model of the additional component is set to exchange heat with the coil model, rotor model, and stator core model. The parameters of the second model are then set so that the estimated temperature of the temperature sensor corresponds well with the measured temperature.

[0156] Thus, the monitoring device of this embodiment can generate models not only for cases where abnormal heat generation occurs in and around the motor, but also for cases where abnormal heat absorption occurs in and around the motor.

[0157] The first model of the electric motor in the above embodiment consists of a coil model, a stator core model, a temperature sensor model, an air layer model, a rotor model, and an ambient air model, but is not limited to this form. The first model of the electric motor may also include models of other components. For example, the first model of the electric motor may include a housing model supporting the stator and rotor, a bearing model, and a shaft model supporting the rotor, etc. Alternatively, the first model of the electric motor may not include some models. For example, the model of the electric motor may not include a model of the air layer.

[0158] The motor monitoring device of this embodiment is configured to allow switching between a normal model creation mode and an abnormal model creation mode, but it is not limited to this configuration. The motor monitoring device does not need to have a function for the normal model creation mode. The first model of the motor when the motor is operating normally may be generated by another device.

[0159] Furthermore, in this embodiment, the motor monitoring device is configured as a separate processing unit from the machine control device, but the embodiment is not limited to this. The machine control device may also have the function of a motor monitoring device. That is, the processor of the machine control device may function as a model search unit, a temperature estimation unit, a parameter calculation unit, etc.

[0160] In each of the above-described controls, the order of the steps can be changed as appropriate, as long as the function and operation remain unchanged. The above embodiments can be combined as appropriate.

[0161] In each of the figures described above, identical or equivalent parts are denoted by the same reference numerals. The embodiments described above are illustrative and do not limit the invention. Furthermore, the embodiments include modifications of the embodiments shown in the claims. [Explanation of Symbols]

[0162] 2 Monitoring device 10 Electric motor Model of a 10A electric motor 11a Rotor Model 14,15 Bearings Model of 16a coil Model of 20a stator core 30A electric motor model 31a Model of a temperature detector 35a Air layer model 36a Outdoor air model 51 Storage section 52 Display section 53 Temperature estimation section 54 Loss calculation section 55 Temperature calculation section 62 Status acquisition unit 63 Parameter Calculation Unit 66 Evaluation Department 67 Parameter Change Section 71 Model Search Unit 72 Temperature judgment section 73 Additional component 74 Settings Section 75 Anomaly location estimation unit 76 Notification Department Models 81a and 82a

Claims

1. A state acquisition unit acquires the operating status of the motor, including the measured temperature detected by a temperature sensor attached to the motor, A temperature estimation unit that calculates the estimated temperature of the temperature detector based on the model of the electric motor, A memory unit that stores a first model of the electric motor when the electric motor is operating normally, It includes a model search unit that generates a second model of the electric motor when the electric motor's operating state is abnormal, The electric motor model includes models of the components of the electric motor, such as a rotor model, a stator core model, a coil model, and a temperature sensor model. A heat capacity is set for at least one component model. A coefficient for heat transfer between the model components has been set. The model search unit includes a component addition unit that generates a second model by adding a model of an additional component that performs heat exchange with the motor components to the first model when the difference between the measured temperature of the temperature detector and the estimated temperature of the temperature detector based on the first model deviates from a predetermined determination range, A motor monitoring device including a setting unit that sets a model of an additional member, a model of a component of a motor that performs heat exchange, and a coefficient for heat transfer of the additional member, so that the estimated temperature of the temperature sensor based on a second model corresponds to the measured temperature of the temperature sensor.

2. It includes a notification unit that notifies other devices of information regarding a second model of the electric motor, The motor monitoring device according to claim 1, wherein the notification unit notifies at least one piece of information from among the model of the additional member, the model of the components of the motor that perform heat exchange, the heat capacity of the additional member, and the coefficient relating to heat transfer with respect to the additional member.

3. It is equipped with a display unit that displays information about a second model of electric motor, The motor monitoring device according to claim 1 or 2, wherein the display unit displays at least one piece of information from among the model of the additional member, the model of the components of the motor that perform heat exchange, the heat capacity of the additional member, and the coefficient relating to heat transfer to the additional member.

4. The setting unit is configured to set an additional heat-generating component, as described in claim 1 or 2, for the electric motor monitoring device.

5. The motor monitoring device according to claim 4, wherein the model search unit sets the model of an additional member so as to perform heat exchange with the coil model and the rotor model when an abnormality occurs in the motor bearing.

6. The motor monitoring device according to claim 4, wherein the model search unit sets the model of an additional member so as to perform heat exchange with the stator core model when a heat source is generated outside the motor.

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

8. It is equipped with an abnormality location estimation unit that estimates the location of the abnormality, The memory unit stores the second model of the electric motor and the location of the abnormality that occurred in the past. The motor monitoring device according to claim 1 or 2, wherein the abnormality location estimation unit estimates the location of an abnormality relating to the second model of the motor currently generated, based on the second model and location of abnormalities of the motor generated in the past.

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