Methods for monitoring motors

Abstract

A method for monitoring an electric motor, wherein the method comprises: a) obtaining temperature measurements of the temperature at a plurality of locations of the electric motor, b) obtaining estimated temperatures at the plurality of locations given by a thermal model of the electric motor, the thermal model including initial weight parameter values, c) minimizing the difference between the temperature measurements and the estimated temperature by finding an optimal weight parameter value, d) storing the initial weight parameter value, thereby obtaining a storage of used weight parameter values, and updating the optimal weight parameter value to a new initial weight parameter value, and repeatedly repeating steps a)-d) during operation of the electric motor.

Description

Technical Field

[0001] The present disclosure generally relates to electric machines. Background Art

[0002] Thermal models are used to predict the temperature distribution in electric machines. Several thermal simulation methods are currently used to estimate temperature distribution. These include finite element and computational fluid dynamics thermal models and lumped parameter thermal network (LPTN) models. LPTN models are preferred due to their inherent simplicity and fast computation time.

[0003] In LPTN, the heat flow and temperature distribution in the motor are estimated by using an equivalent circuit including thermal resistance, thermal capacitance, and heat sources. The geometry and material properties are used to derive thermal parameters during the design phase.

[0004] The publication "Analytical Thermal Model for Fast Stator Winding Temperature Prediction," by Sciascera et al., IEEE Transactions on Industrial Electronics, Vol. 64, No. 8, August 2017, discloses a thermal modeling technique for predicting motor winding temperatures. A seven-node thermal model is first implemented. An empirical procedure is disclosed for fine-tuning the model's key parameters due to uncertainties in material properties, manufacturing tolerances, assembly processes, and interactions with other drive system components. The tuning process involves 1) experimental acquisition of winding temperature profiles, 2) defining an objective function representing the error between the LPTN predicted and experimental profiles, and 3) finding an optimal set of correction factors that minimize the objective function. The seven-node thermal network is then simplified using an equivalent three-node network. Summary of the Invention

[0005] The inventors have discovered that the expected estimated temperatures differ significantly from the temperatures measured in situ. The accuracy of the analytical model is unquestionable, as numerous tests were conducted during the development and validation phases. However, its simplified nature dictates that equivalent circuit parameters are used to represent the thermal distribution. Some parameters are determined solely by geometry and material properties and remain constant throughout the life of the machine despite changes in operating conditions. Other parameters vary throughout the life of the machine due to wear or the environmental conditions to which the machine is exposed. Inaccurate heat flow and temperature estimates result from ignoring the non-static nature of the analytical model parameters.

[0006] In view of the above, a general object of the present disclosure is to provide a method for monitoring an electric motor, which solves or at least alleviates the problems of the prior art.

[0007] Therefore, according to a first aspect of the present disclosure, a method for monitoring a motor is provided, wherein the method comprises: a) obtaining temperature measurement values ​​of the temperature at multiple positions of the motor, b) obtaining estimated temperatures at the multiple positions given by a thermal model of the motor, the thermal model including initial weight parameter values, c) minimizing the difference between the temperature measurement values ​​and the estimated temperature by finding the optimal weight parameter value, d) storing the initial weight parameter value, thereby obtaining a storage of used weight parameter values, and updating the optimal weight parameter value to a new initial weight parameter value, and repeatedly repeating steps a)-d) during operation of the motor.

[0008] In the context of this disclosure, the term "during (the motor's) operation" is interpreted to refer to a relatively long period of time, such that the values ​​of some circuit parameters in the thermal model can be assumed to have changed due to, for example, motor wear or environmental conditions. Such time periods are at least on the order of weeks or months, and in many cases on the order of years. The thermal model is thus updated to remain close to the actual thermal characteristics of the motor over time. As a result, temperature estimates can be made more accurately over the life of the motor.

[0009] The electric machine can be a motor or a generator.

[0010] One embodiment includes comparing the optimal weight parameter value with an initial weight parameter value or a used weight parameter value, and detecting whether a motor performance change or a motor failure has occurred based on the comparison result.

[0011] Each optimal weight parameter value is advantageously compared with a corresponding initial weight parameter value or a corresponding used weight parameter value.

[0012] According to one embodiment, the detecting involves detecting a motor performance change or motor failure if one of the optimal weight parameter values ​​deviates from its corresponding initial weight parameter value or used weight parameter value by more than a predetermined amount.

[0013] Thus, motor diagnostics regarding motor performance or motor faults can be provided.

[0014] Motor performance may be affected, for example, by material-related problems such as some degradation of the winding insulation due to thermal stress or due to anomalies in external components such as in a heat exchanger, due to dust accumulation on the motor frame or environmental changes that affect the thermal behavior of the motor.

[0015] Motor faults may be, for example, material-related faults, such as winding insulation breakdown resulting in turn-to-turn shorts in the rotor or stator windings.

[0016] One embodiment includes a method for determining a cause of a motor performance change or motor failure based on a deviation from an optimal weight parameter value.

[0017] The weight parameter values—the initial, used, and optimal weight parameter values—are constants in the equations that form the thermal model and describe the motor's thermal behavior. By detecting deviations from these constants, the corresponding thermal impedance or power loss injection location can be determined, thereby identifying the type and location of the fault.

[0018] According to one embodiment, the weight parameter values ​​are arranged in subsets forming corresponding correction matrices. "Weight parameter values" herein refer to any of the optimal weight parameter values, initial weight parameter values ​​or used weight parameter values.

[0019] According to one embodiment, the thermal model is a matrix equation including a thermal capacitance matrix, a thermal resistance matrix, and a power loss injection vector, each of which is multiplied by a corresponding one of the correction matrices.

[0020] According to one embodiment, the thermal model is a lumped parameter thermal network LPTN model.

[0021] One embodiment includes monitoring the motor using the thermal model with new initial weight parameter values.

[0022] According to a second aspect of the present disclosure, there is provided a computer program comprising computer code which, when executed by processing circuitry of a monitoring device, causes the monitoring device to perform the method of the first aspect.

[0023] According to a third aspect of the present disclosure, a monitoring device for monitoring a motor is provided, the monitoring device comprising: a storage medium containing computer code, and a processing circuit, wherein when the processing circuit executes the computer code, the monitoring device is configured to: a) obtain temperature measurement values ​​of the temperature at multiple positions of the motor, b) obtain estimated temperatures at the multiple positions given by a thermal model of the motor, the thermal model including initial weight parameter values, c) minimize the difference between the temperature measurement values ​​and the estimated temperature by finding an optimal weight parameter value, d) store the initial weight parameter value, thereby obtaining a storage of used weight parameter values, and update the optimal weight parameter value to a new initial weight parameter value, and repeatedly repeat steps a)-d) during operation of the motor.

[0024] According to one embodiment, the processing circuit compares the optimal weight parameter value with the initial weight parameter value or the used weight parameter value, and detects whether the motor performance has changed or whether the motor has failed based on the comparison result.

[0025] According to one embodiment, the detecting involves detecting a motor performance change or motor failure if one of the optimal weight parameter values ​​deviates from its corresponding initial weight parameter value or used weight parameter value by more than a predetermined amount.

[0026] According to one embodiment, the processing circuit is configured to determine a cause of a motor performance change or a motor failure based on a deviation from an optimal weight parameter value.

[0027] According to one embodiment, the weight parameter values ​​are arranged in subsets forming corresponding correction matrices.

[0028] According to one embodiment, the thermal model is a matrix equation including a thermal capacitance matrix, a thermal resistance matrix, and a power loss injection vector, each of which is multiplied by a corresponding one of the correction matrices.

[0029] According to one embodiment, the thermal model is a lumped parameter thermal network LPTN model.

[0030] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. Unless explicitly stated otherwise, all references to "a / an / the element, device, component, device, etc." should be interpreted as referring to at least one instance of the element, device, component, device, etc. disclosed herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Specific embodiments of the inventive concept will now be described by way of example with reference to the accompanying drawings, in which:

[0032] Figure 1 An example of a monitoring device for monitoring an electric motor is schematically shown;

[0033] Figure 2 An example of a simplified LPTN showing a motor; and

[0034] Figure 3 With the help of Figure 1 Flowchart of a method for monitoring a motor using a monitoring device. DETAILED DESCRIPTION

[0035] The concepts of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. However, the concepts of the present disclosure may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete and will fully convey the scope of the concepts of the present disclosure to those skilled in the art. Throughout the description, like reference numerals refer to like elements.

[0036] Figure 1 A block diagram of an example of a monitoring device 1 is depicted. The monitoring device 1 is configured to monitor the condition and / or performance of an electrical machine. The electrical machine may be a motor or a generator.

[0037] The electric machine includes a plurality of temperature sensors configured to measure the temperature of a corresponding one of a plurality of different locations of the electric machine. The temperature sensors may, for example, be configured to detect the temperature of the stator windings, the rotor windings, the rotor surface and / or the stator chassis.

[0038] The monitoring device 1 comprises an input unit 2 configured to receive temperature measurements from a temperature sensor.The monitoring device 1 may be configured to receive temperature measurements via wireless, wired, or a combination of wireless and wired communication.

[0039] The monitoring device 1 comprises a processing circuit 5 configured to receive temperature measurements from an input unit 2. The monitoring device 1 may comprise a storage medium 7.

[0040] The storage medium 7 may comprise a computer program comprising computer code which, when executed by the processing circuit 7 , causes the monitoring device 1 to perform the methods disclosed herein.

[0041] The processing circuit 5 can, for example, use any combination of one or more suitable central processing units (CPUs), multiprocessors, microcontrollers, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), etc., and can perform any operations disclosed herein regarding motor monitoring.

[0042] The method involves estimating the temperature at a plurality of locations where temperature sensors measure temperature using a thermal model of the motor. The method further involves minimizing the difference between the estimated temperature and the temperature measurements by adjusting the thermal model, as will be described in detail below.

[0043] The storage medium 7 can be implemented, for example, as a memory such as a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or an electrically erasable programmable read-only memory (EEPROM) and more particularly, as a non-volatile storage medium of a device implemented in an external memory such as a USB (Universal Serial Bus) memory or a flash memory such as a compact flash memory.

[0044] Figure 2 An example of a thermal model 9 of an electric machine is shown. For the sake of clarity, the depicted thermal model 9 is a simplified thermal model. The thermal model 9 is a LPTN model.

[0045] The thermal model 9 simulates the heat flow and temperature distribution in the motor. This modeling is accomplished using an equivalent thermal circuit consisting of a thermal impedance and a power loss injection, Ploss. Power loss is represented as a heat source in the thermal model; the terms power loss injection and heat source are used interchangeably herein. Thermal impedance can be represented by thermal resistance R and thermal capacitance C.

[0046] Thermal resistance R can, for example, simulate heat dissipation by natural convection or heat flow by conduction. Thermal capacity C simulates the thermal mass of the various parts or components of the motor. Thermal capacity C can be calculated as the product of the mass and specific heat capacity of the material of the part or component of the motor.

[0047] The selection of thermal resistance R and thermal capacitance C can be determined initially during the design phase based on the geometry of the motor and the properties of the materials used.

[0048] The thermal model 9 is formed of a plurality of sections, including a rotor model section 9a, an air gap model section 9b, a tooth / stator winding model section 9c, a stator model section 9d, and an external air model section 9e.

[0049] The thermal model 9 comprises a plurality of points or nodes 11. The exemplary thermal model 9 is a nine-node circuit, but may alternatively comprise a different number of nodes depending on, for example, the type of electrical machine being modelled.

[0050] The thermal model 9 can be represented by the matrix equation:

[0051] Wherein T is a temperature vector describing the temperature at the node 11 in the LPTN, and P is a power loss injection vector including the power loss injection Ploss in each node 11. R is a matrix representing the thermal resistance R, and is therefore a thermal resistance matrix. C is a matrix representing the thermal capacitance C, and is therefore a thermal capacitance matrix. α, β, and γ are weight parameters in matrix form. The weight parameters α, β, and γ are correction matrices. Initially, the weight parameters α, β, and γ can be, for example, a unit matrix. The elements of the matrices α, β, and γ are referred to herein as weight parameter values. The weight parameter α is multiplied by the thermal resistance matrix R, the weight parameter γ is multiplied by the power loss injection vector P, and the weight parameter β is multiplied by the thermal capacitance matrix C.

[0052] refer to Figure 3 , a method for monitoring an electric machine by means of the monitoring device 1 will now be described.

[0053] In step a), temperature measurements of the temperature at a plurality of locations of the motor are obtained. The temperature measurements are obtained from temperature sensors located on or in the motor.

[0054] In step b), estimated temperatures at a plurality of locations given by a thermal model of the electrical machine are obtained.At this point in the method, the weight parameter values ​​for the weight parameters α, β and γ are referred to herein as initial weight parameter values.

[0055] In step c), minimization of the difference between the estimated temperature and the corresponding temperature measurement is performed. Minimization involves finding the optimal weight parameters α, β, and γ to minimize the difference between the estimated temperature and the temperature measurement. The elements of the matrices α, β, and γ, i.e., the weight parameter values, can therefore be varied to find the optimal weight parameter values, thereby finding the optimal weight parameters α, β, and γ, which minimize the difference between the estimated temperature determined by the thermal model 9 and the temperature measurement.

[0056] The optimization performed in step c) may be performed, for example, using standard optimization routines such as iterative sequential quadratic programming methods or machine learning.

[0057] In step d), the initial weight parameter values ​​are stored to obtain a storage or storage set of used weight parameter values. The initial weight parameter values ​​can be stored in matrix form, i.e. as a correction matrix. Thus, the used weight parameter values ​​are elements of the used weight parameters in matrix form, which are the initial weight parameters from a previous iteration of the method. The used weight parameter values ​​may also include reference weight parameter values, which form part of the weight parameters in the thermal model 9, which correctly describes the thermal behavior of the motor when commissioning (i.e. when the motor is new). The used weight parameters may be stored in the storage medium 7 in step d).

[0058] In step d), the optimal weight parameter value is updated to the new initial weight parameter. Therefore, the optimal weight parameter value is set as the new initial weight parameter value.

[0059] During operation of the motor, for example during the remaining life of the motor, steps a) to d) are repeated repeatedly. Each time the method is performed, the previous optimal weight parameter value is the initial weight parameter value and a new optimal weight parameter value is determined in step c), which new optimal weight parameter value may be the same as or different from the previous optimal weight parameter value.

[0060] The thermal model 9 will thus remain synchronized with the thermal behavior of the electrical machine and the temperature estimate will therefore become more accurate over time.

[0061] Steps a)-d) may be repeated, for example, after a predetermined time has elapsed since the last execution of the method.

[0062] According to one variant, the optimal weight parameter values ​​are compared with the initial weight parameter values ​​or the used weight parameter values. The corresponding optimal weight parameter values ​​are compared with the corresponding initial weight parameter values ​​or the corresponding used weight parameter values. Thus, a comparison can be made between the elements of the optimal weight parameter matrix and the corresponding elements of the corresponding initial weight parameters or the corresponding elements of the corresponding used weight parameters from a previous iteration of the method.

[0063] This step can be performed, for example, in conjunction with step c), after step c) but before step d), or after step d).

[0064] The reference weight parameter values ​​are elements of a matrix, where each element represents a reference value associated with a healthy motor modeled by the thermal model 9 .

[0065] In this variation, a motor performance change or motor failure is detected if one of the optimal weight parameters α, β, and γ deviates from its corresponding weight parameter value of the initial weight parameter or used weight parameter by more than a predetermined amount.

[0066] According to one example, the cause of the motor performance change or motor failure can be determined based on the deviated weight parameter value. Therefore, the cause can be determined based on which element or weight parameter value or element is the deviated weight parameter value or element and / or the deviation amount.

[0067] This approach may be able to detect both slow and rapid changes in motor performance. For example, to detect slow changes, the optimal weight parameter values ​​may be compared with corresponding reference weight parameter values, or with corresponding used weight parameter values ​​stored earlier in the motor's life. These changes may accumulate slowly, for example due to dust accumulation on the motor or fouling in a heat exchanger, leading to degraded cooling and increased temperatures.

[0068] For example, if it is determined that the thermal model has updated the elements of the thermal resistance matrix, i.e., the optimal weight parameter values ​​of the thermal resistance matrix, which describes how heat is dissipated through the motor frame, it may give an indication that cleaning of the motor is a desired action to mitigate elevated temperature levels.

[0069] Rapid changes may be determined by comparing the optimal weight parameter values ​​with corresponding initial weight parameter values, such as during a previous iteration of the method, or with corresponding recently stored used weight parameter values.

[0070] According to one variant, the predetermined time after which steps a) to c) are repeated can be determined based on the amount by which the values ​​of the weight parameters of the matrices α, β, and γ have changed since a previous iteration of the method. For example, if the magnitude of one or more elements has changed but by less than a predetermined amount, the method can be performed more frequently to better track any changes in motor performance or condition.

[0071] The inventive concept has mainly been described above with reference to a few examples. However, as is readily appreciated by a person skilled in the art, other embodiments than the ones disclosed above are equally possible within the scope of the inventive concept as defined by the appended claims.

Claims

1. A method for monitoring a motor, wherein the method comprises: a) obtaining temperature measurements of the temperature at a plurality of locations of the motor, b) obtaining estimated temperatures at said plurality of locations given by a thermal model (9) of said electrical machine, said thermal model (9) comprising initial weight parameter values, c) minimizing the difference between the temperature measurement and the estimated temperature by finding an optimal weight parameter value, d) storing the initial weight parameter value, thereby obtaining a storage of used weight parameter values, and updating the optimal weight parameter value to a new initial weight parameter value, Repeating steps a)-d) repeatedly during operation of the motor, and The electric machine is monitored using the thermal model with the new initial weight parameter values.

2. The method according to claim 1, comprising: Comparing the optimal weight parameter value with the initial weight parameter value or with the used weight parameter value; as well as Based on the comparison result, it is detected whether a change in motor performance or a motor failure occurs.

3. The method of claim 2, wherein the detecting involves detecting a motor performance change or a motor failure when one of the optimal weight parameter values ​​deviates from its corresponding initial weight parameter value or used weight parameter value by more than a predetermined amount.

4. The method according to claim 3, comprising: Based on the deviated optimal weight parameter value, a cause of the motor performance change or motor failure is determined.

5. A method according to any one of the preceding claims, wherein the weight parameter values ​​are arranged to form subsets of corresponding correction matrices.

6. The method according to claim 5, wherein the thermal model (9) is a matrix equation including a heat capacity matrix, a thermal resistance matrix and a power loss injection vector, wherein each term in the heat capacity matrix, the thermal resistance matrix and the power loss injection vector is multiplied by a corresponding correction matrix in the correction matrix.

7. The method according to any one of claims 1 to 4, wherein the thermal model (9) is a lumped parameter thermal network (LPTN) model.

8. A computer program comprising computer code which, when executed by processing circuitry (5) of a monitoring device (1), causes the monitoring device (1) to perform the method according to any one of claims 1 to 7.

9. A monitoring device (1) for monitoring an electric motor, the monitoring device (1) comprising: a storage medium (7) comprising computer code, and processing circuit (5), Wherein, when the processing circuit (5) executes the computer code, the monitoring device (1) is configured to: a) obtaining temperature measurements of the temperature at a plurality of locations of the motor, b) obtaining estimated temperatures at said plurality of locations given by a thermal model (9) of said electrical machine, said thermal model (9) comprising initial weight parameter values, c) minimizing the difference between the temperature measurement and the estimated temperature by finding an optimal weight parameter value, d) storing the initial weight parameter value, thereby obtaining a storage of used weight parameter values, and updating the optimal weight parameter value to a new initial weight parameter value, During operation of the motor, steps a)-d) are repeated repeatedly, and The electric machine is monitored using the thermal model with the new initial weight parameter values.

10. The monitoring device (1) according to claim 9, wherein the processing circuit (5) is configured to compare the optimal weight parameter value with the initial weight parameter value or with the used weight parameter value, and detect whether a motor performance change or a motor failure occurs based on the comparison result.

11. A monitoring device (1) according to claim 10, wherein the detection involves: detecting a motor performance change or a motor failure when one of the optimal weight parameter values ​​deviates from its corresponding initial weight parameter value or used weight parameter value by more than a predetermined amount.

12. The monitoring device (1) according to claim 11, wherein the processing circuit (5) is configured to determine a cause for the motor performance change or motor failure based on the deviated optimal weight parameter value.

13. Monitoring device (1) according to any one of claims 9 to 12, wherein the weight parameter values ​​are arranged to form subsets of corresponding correction matrices.

14. The monitoring device (1) according to claim 13, wherein the thermal model (9) is a matrix equation including a heat capacity matrix, a thermal resistance matrix and a power loss injection vector, wherein each of the heat capacity matrix, the thermal resistance matrix and the power loss injection vector is multiplied by a corresponding one of the correction matrices.

15. Monitoring device (1) according to any one of claims 9 to 12, wherein the thermal model is a lumped parameter thermal network (LPTN) model.

Images