Data processing method and system of motor rotor and computer equipment

Abstract

The embodiment of the invention relates to the technical field of motor state monitoring, in particular to a data processing method and system of a motor rotor and computer equipment. The method mainly comprises the following steps: according to resonance signals radiated by a plurality of passive sensors arranged at different parts of the motor rotor, determining measuring point temperatures corresponding to a plurality of set parts of the motor rotor; based on the measuring point temperatures corresponding to the plurality of set parts and the operation condition of the motor rotor, adopting a preset multi-physical field coupling model to obtain a performance index of the motor rotor, and determining association relationship data between the operation condition and the performance index; and determining a health index of the motor rotor based on the performance index. On-line measurement and multi-dimensional performance evaluation of the internal temperature of the motor rotor are realized through a passive sensor and a multi-physical field coupling model.

Description

Technical Field

[0001] This application relates to the field of motor condition monitoring technology, and in particular to a data processing method, system and computer device for motor rotors. Background Technology

[0002] With the widespread application of high-performance motors in fields such as new energy vehicles, the requirements for motor reliability and lifespan are gradually increasing. Motor rotor temperature is one of the core parameters affecting its performance and lifespan. Consequently, various methods for measuring motor rotor temperature have emerged. For example, non-contact infrared thermometry is used, which infers the temperature by detecting infrared radiation from the motor rotor surface; or contact thermometry is used, such as installing thermocouples or resistance thermometers on the motor rotor via slip ring leads for temperature detection.

[0003] However, because the motor rotor is rotating at high speed, non-contact infrared temperature measurement is easily affected by changes in the emissivity of the rotor surface, internal structural obstruction, and interference from strong electromagnetic environments, resulting in low measurement accuracy and delayed response. On the other hand, contact temperature measurement methods have problems such as the lead wires being easily damaged by centrifugal force and unreliable slip ring contact. Summary of the Invention

[0004] Therefore, it is necessary to provide a data processing method, system, and computer device for an electric motor rotor to address at least one of the aforementioned technical problems.

[0005] In a first aspect, embodiments of this application provide a data processing method for an electric motor rotor, the method comprising: The temperature of the measuring points corresponding to multiple set parts of the motor rotor is determined by the resonant signals radiated by multiple passive sensors set at different parts of the motor rotor.

[0006] Based on the temperature measurements at multiple designated locations and the operating conditions of the motor rotor, a pre-defined multiphysics coupling model is used to obtain the performance indicators of the motor rotor and determine the correlation data between the operating conditions and the performance indicators. The operating conditions include the motor rotor's speed and current; the performance indicators include the motor rotor's global temperature field distribution, thermal stress distribution, and demagnetization risk probability.

[0007] Based on performance indicators, the health index of the motor rotor is determined.

[0008] In some embodiments, when determining the temperature of multiple set points corresponding to multiple locations on the motor rotor based on the resonant signals radiated by multiple passive sensors disposed at different locations on the motor rotor, the data processing method for the motor rotor further includes: Spectral analysis was performed on the resonant signals radiated by multiple passive sensors to obtain the resonant frequencies corresponding to each resonant signal. Based on the preset mapping relationship between resonant frequency and temperature, the temperature of the measuring points corresponding to multiple set parts of the motor rotor is determined.

[0009] In some embodiments, when obtaining the performance indicators of the motor rotor based on the temperature of measuring points corresponding to multiple set parts and the operating conditions of the motor rotor using a preset multiphysics coupling model, the data processing method for the motor rotor further includes: Based on the operating conditions of the motor rotor, a preset multi-physics coupling model is used to predict the theoretical temperature of multiple set parts of the motor rotor. Based on the error between the temperature of the measuring point corresponding to each of the multiple set parts and the theoretical temperature, the model parameters are corrected so that the error converges to the preset error threshold. The global temperature field distribution of the motor rotor is determined by using a corrected multiphysics coupling model.

[0010] In some embodiments, the data processing method for the motor rotor further includes: The thermal stress distribution of the motor rotor is determined based on the global temperature field distribution and the material expansion coefficient of the motor rotor. The probability of demagnetization risk of the motor rotor is determined based on the global temperature field distribution and the demagnetization curve of the motor rotor.

[0011] In some embodiments, when determining the health index of the motor rotor based on performance indicators, the data processing method for the motor rotor further includes: The first evaluation parameter is determined based on the highest temperature value in the global temperature field distribution, the ambient temperature of the motor rotor, and the extreme temperature of the motor rotor. The second evaluation parameter is determined based on the probability of demagnetization risk. The third evaluation parameter is determined based on the maximum thermal stress in the thermal stress distribution and the material yield strength of the motor rotor; The fourth evaluation parameter is determined based on the highest temperature value and the temperature difference between the highest temperature value and the lowest temperature value in the global temperature field distribution. The first, second, third, and fourth evaluation parameters are weighted and summed to determine the health index of the motor rotor.

[0012] In some embodiments, the data processing method for the motor rotor further includes: When the health index exceeds the preset warning threshold, a preset cooling strategy is executed for the motor rotor.

[0013] In some embodiments, the data processing method for the motor rotor further includes: The abnormal state of the motor rotor is determined by the temperature deviation of the motor rotor and multiple identical motor rotors in the motor cluster, as well as the changing trend of the comprehensive health index of the motor cluster. Among them, the multiple identical motor rotors in the motor cluster have the same operating conditions, and the comprehensive health index of the motor cluster is obtained based on the health index of each identical motor rotor.

[0014] In a second aspect, embodiments of this application provide a data processing system for an electric motor rotor, the system comprising: Multiple passive sensors are embedded in multiple designated locations on the motor rotor and radiate resonant signals through the alternating magnetic field generated by the motor stator windings.

[0015] A broadband receiving antenna array is used to capture resonant signals radiated by multiple passive sensors.

[0016] A processor for performing the steps of a data processing method for an electric motor rotor as provided in any embodiment of the first aspect of this application.

[0017] In a third aspect, embodiments of this application provide a method for determining the health index of an electric motor rotor, the method comprising: Obtain the real-time operating conditions of the target motor rotor; the real-time operating conditions should include at least the rotational speed and current of the target motor rotor; Based on the preset correlation data between operating conditions and performance indicators, and considering the real-time operating conditions of the target motor rotor, the performance indicators of the target motor rotor are determined. The correlation data is obtained using the motor rotor data processing method provided in any embodiment of the first aspect of this application. The performance indicators include the global temperature field distribution, thermal stress distribution, and demagnetization risk probability of the target motor rotor.

[0018] Determine the health index of the target motor rotor based on its performance indicators.

[0019] In a fourth aspect, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the data processing method for a motor rotor provided in any embodiment of the first aspect of this application, or the method for determining the health index of a motor rotor provided in any embodiment of the third aspect of this application.

[0020] In a fifth aspect, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the data processing method for a motor rotor provided in any embodiment of the first aspect of this application, or the method for determining the health index of a motor rotor provided in any embodiment of the third aspect of this application.

[0021] The aforementioned data processing method, system, computer equipment, and storage medium for the motor rotor, by arranging multiple passive sensors at multiple designated locations on the motor rotor and analyzing their resonant signals to determine the temperature at the measurement points, achieves direct, non-destructive, and online measurement of the internal temperature of the high-speed rotating motor rotor. This solves the technical problems of easy damage to traditional contact temperature measurement leads and the significant susceptibility of non-contact temperature measurement accuracy to environmental interference. By combining the limited measurement point temperatures with operating conditions and using a multi-physics coupling model for inversion, it realizes the calculation of the entire physical state (temperature field, stress field, magnetic field) of the motor rotor from temperature data at a few points. This expands the evaluation dimensions of the motor rotor from a single temperature to multiple physical domains such as heat, force, and magnetism, thereby achieving multi-dimensional and comprehensive monitoring of motor rotor performance. Furthermore, by integrating multiple performance indicators into a quantified health index, complex physical field information can be transformed into intuitive and quantifiable health status indicators. Attached Figure Description

[0022] Figure 1 This is an application environment diagram of the data processing method for the motor rotor in some embodiments; Figure 2 This is a flowchart illustrating the data processing method for the motor rotor in some embodiments; Figure 3 This is a flowchart illustrating the steps involving the resonant frequency in some embodiments; Figure 4 This is a flowchart illustrating the steps involved in determining the global temperature field distribution in some embodiments; Figure 5 This is a flowchart illustrating the steps involved in determining the thermal stress distribution and demagnetization risk probability in some embodiments; Figure 6 This is a flowchart illustrating the steps involved in determining health indices in some embodiments; Figure 7 This is a structural block diagram of the data processing system for the motor rotor in some embodiments; Figure 8 This is a diagram showing the internal structure of a computer device in some embodiments. Detailed Implementation

[0023] To make the technical solutions and advantages of this application clearer, the embodiments and related technical content of this application will be further described in detail below with reference to the accompanying drawings and text description. It should be understood that the embodiments described below are only used to explain the technical solutions of the embodiments of this application and are not intended to limit more possible implementations of this application.

[0024] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0025] It should be noted that relational terms such as "first" and "second" appearing in this document are used only to distinguish things, states, or actions, and do not necessarily indicate or imply relative importance or order. The terms "including," "comprising," or any other variations thereof are used to indicate non-exclusive inclusion, and the included objects may not be limited to those listed in this document. The terms "multiple" or other variations are used to indicate that the number of objects is two or more.

[0026] For ease of understanding, Figure 1 An application environment is illustrated, in which server 101 executes the steps of a data processing method for a motor rotor. During execution, server 101 can communicate with terminal 102 via a network to obtain motor rotor data sent by terminal 102. Server 101 can be implemented using a standalone server or a server cluster consisting of multiple servers. Terminal 102 can be, but is not limited to, vehicles, equipment, etc., containing permanent magnet synchronous motors or other types of motors.

[0027] Servers can be implemented using at least one of the following hardware forms: programmable logic array (PLA), field-programmable gate array (FPGA), digital signal processor (DSP), application-specific integrated circuit (ASIC), general-purpose processor, or other programmable logic device.

[0028] Of course, the data processing method for motor rotors provided in this application embodiment can also be applied to more scenarios not shown.

[0029] In a first aspect, embodiments of this application provide a data processing method for an electric motor rotor, which can be applied to... Figure 1 In the application environment shown, it can be applied to Figure 1 Taking server 101 as an example, in some embodiments, such as Figure 2 As shown, the data processing method for the motor rotor includes steps S201, S202, and S203 that can be executed by the server 101. Each step is described in detail below.

[0030] Step S201: Determine the temperature of the measuring points corresponding to multiple set parts of the motor rotor based on the resonant signals radiated by multiple passive sensors set at different parts of the motor rotor.

[0031] Passive sensors are sensing devices that do not require an external power supply during operation. Common passive sensors include those based on inductive-capacitive (LC) resonant circuits and surface acoustic waves. In this application, the passive sensor is specifically a miniaturized LC resonant temperature sensor, which consists of a temperature-sensitive ceramic capacitor and a planar inductor. It is wirelessly excited by the alternating magnetic field generated by the stator windings of a motor, thereby radiating a resonant signal. The resonant frequency of the resonant signal changes with temperature. By pre-calibrating the frequency-temperature curve, the temperature at the location of the passive sensor can be deduced from the resonant frequency of the resonant signal radiated by the passive sensor.

[0032] Multiple designated locations refer to several key positions on the motor rotor structure, intentionally selected based on the distribution characteristics of thermal, mechanical, and electromagnetic loads. Typically, the temperature and stress fields of a motor rotor are non-uniform, containing hot spots or high-stress points due to design, materials, or cooling conditions. Placing passive sensors at these key locations allows for monitoring of the worst-case conditions of the motor rotor.

[0033] For example, the back of the permanent magnet is the area with the highest risk of demagnetization in a permanent magnet synchronous motor. Installing a passive sensor on the back of the permanent magnet to monitor the temperature here is of great significance for assessing the risk of rotor demagnetization. The connection between the shaft and the rotor core is an area where thermal and mechanical stresses are concentrated, and the fit is prone to loosening due to differences in thermal expansion. Monitoring the temperature here can indirectly assess the reliability of the connection. The connection between the end ring and the guide bar is a high-incidence area for cracks caused by thermal fatigue, and it is also an area where eddy current losses are concentrated. Monitoring the temperature here can indirectly assess the reliability of the connection and the degree of rotor loss.

[0034] In some specific embodiments, the multiple designated locations for installing the passive sensor include at least two of the following: the back of the permanent magnet, the connection between the shaft and the rotor core, and the connection between the end ring and the guide bar.

[0035] The measured point temperature refers to the temperature value directly measured at a specific, localized location on or inside an object using a sensing device. It represents the instantaneous thermal state of the object at that discrete point. In this application, the measured point temperature reflects the actual physical temperature of a point on the motor rotor at a designated location.

[0036] Step S202: Based on the temperature of the measuring points corresponding to multiple set parts and the operating conditions of the motor rotor, a preset multi-physics coupling model is used to obtain the performance indicators of the motor rotor and determine the correlation data between the operating conditions and the performance indicators.

[0037] The operating conditions include the speed and current of the motor rotor; the performance indicators include the global temperature field distribution, thermal stress distribution, and demagnetization risk probability of the motor rotor.

[0038] The operating condition of a motor rotor refers to the collection of all external excitation conditions, internal control commands, and environmental parameters of the motor at a specific moment. This includes, but is not limited to, electrical operating data, mechanical operating data, thermal management and environmental data, time and status data, etc.

[0039] Specifically, electrical operating data may include: phase currents of the motor stator windings (such as the instantaneous values ​​of the three-phase currents U, V, and W), phase voltages of the motor stator windings, bus voltage, switching frequency and adjustment strategy of the motor controller, power factor, etc.

[0040] Mechanical operating data may specifically include: the real-time speed of the motor rotor, the real-time torque output by the motor, and the electrical angle of the motor rotor.

[0041] Thermal management and environmental data may specifically include: the temperature of the cooling medium, the flow rate or pressure of the cooling medium, the ambient temperature of the motor, and the temperature of the motor housing.

[0042] The time and status data may include: the motor's cumulative running time, the current operating mode (such as start, acceleration, constant speed, deceleration, braking), etc.

[0043] In this application, the rotor speed and current of the motor are among the necessary parameters for operating conditions, while the remaining data can be used as optional parameters to accurately describe the motor's state. In practical applications, one or more data combinations can be selected from the above data, along with the measured temperature, as input to the multiphysics coupling model, depending on the required model accuracy and available data sources.

[0044] A multiphysics coupling model is a computational model that can solve the governing equations of multiple physical domains (such as electromagnetic fields, temperature fields, and stress fields) simultaneously or sequentially, and consider the interactions between them. Typically, such models are constructed based on numerical methods such as the finite element method and the finite volume method.

[0045] In the actual operation of a motor rotor, its electromagnetic, thermal, and structural behaviors are closely coupled. For example, changes in winding current and magnetic field generate losses, which are heat sources for the temperature field; temperature changes, in turn, alter the electromagnetic properties of the material, such as permeability and resistivity, affecting the loss distribution; uneven temperature distribution induces uneven thermal expansion, resulting in thermal stress and deformation. By establishing and solving the relationship equations between multiple physical fields through a coupled model, this complex interaction can be accurately described, thus ensuring the accuracy of the global performance indicators derived from the temperature at finite measurement points.

[0046] The multiphysics coupling model pre-set in this application is a digital twin model that uses the temperature at the measurement point and the operating conditions as inputs, and identifies parameters online through a mechanism of positive prediction and error correction. This model is used to invert the global temperature field distribution, thermal stress distribution, and demagnetization risk probability of the motor rotor.

[0047] In some optional embodiments, the increase in temperature of the motor rotor leads to a change in the magnetic field, which in turn increases the eddy current loss and copper loss of the motor rotor. Therefore, the temperature change and the loss of the motor rotor are also coupled, and the loss distribution of the motor rotor, such as hysteresis loss, eddy current loss, and copper loss, can be predicted using a multiphysics coupling model. This application does not limit this.

[0048] The correlation data between operating conditions and performance indicators refers to a dataset or response surface model that establishes the mapping relationship between motor operating conditions and performance indicators through the accumulation of a large amount of data and model calculations. It can be a high-dimensional lookup table or a set of empirical formulas. By testing and inverting the target model of motor under various combinations of operating conditions, the correspondence between operating conditions and performance indicators can be obtained, recorded, and solidified into a database or empirical model.

[0049] Step S203: Determine the health index of the motor rotor based on performance indicators.

[0050] The health index of a motor rotor can be understood as a single numerical value or status code used to quantify the overall health or risk level of a motor rotor. Its purpose is to reduce complex, multi-dimensional physical information into an intuitive, easy-to-understand, and decision-making indicator.

[0051] This health index can be expressed in various forms, including but not limited to the following: Normalized index: The index is limited to a fixed range, such as between 0 and 1 or 0% to 100%. The closer it is to 1 or 100%, the worse the health status or the higher the risk (and vice versa). This form facilitates the setting of uniform warning thresholds (e.g., exceeding 0.7 is a level 1 warning).

[0052] Discrete state type: The continuous health status is divided into a limited number of levels and represented by words or codes, such as "Healthy", "Caution", "Warning", "Critical", etc.; or represented by color codes, such as green (normal), yellow (caution), orange (warning), red (danger).

[0053] In some specific embodiments, the health index is a continuous numerical normalized index with a value between 0 and 1. This form can accurately reflect minute changes in the state and facilitates the setting of early warning thresholds.

[0054] By deploying multiple passive sensors at various designated locations on the motor rotor and analyzing their resonant signals to determine the temperature at each measurement point, direct, non-destructive, and online measurement of the internal temperature of a high-speed rotating motor rotor is achieved. This solves the technical challenges of easily damaged leads in traditional contact temperature measurement methods and the significant susceptibility of non-contact temperature measurement accuracy to environmental interference. By combining limited measurement point temperatures with operating conditions and utilizing a multi-physics coupling model for inversion, the calculation of the entire physical state (temperature field, stress field, magnetic field) of the motor rotor is realized from temperature data at a few points. This expands the evaluation dimensions of the motor rotor from a single temperature to multiple physical domains such as heat, force, and magnetism, thereby achieving multi-dimensional and comprehensive monitoring of motor rotor performance. Furthermore, by integrating multiple performance indicators into a quantified health index, complex physical field information can be transformed into intuitive and quantifiable health status indicators.

[0055] In some embodiments, such as Figure 3 As shown, when the server executes step S201, it may include steps S2011 and S2012.

[0056] Step S2011: Perform spectrum analysis on the resonant signals radiated by multiple passive sensors to obtain the resonant frequency corresponding to each resonant signal.

[0057] Spectrum analysis is a signal processing technique that converts time-domain signals into frequency-domain signals. Common spectrum analysis methods include Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT).

[0058] In some specific embodiments, the resonant signals radiated by multiple passive sensors are processed by FFT to convert them into frequency domain signals, thereby clearly identifying the resonant frequency corresponding to each passive sensor.

[0059] Step S2012: Determine the temperature of the measuring points corresponding to multiple set parts of the motor rotor according to the preset mapping relationship between the resonant frequency and temperature.

[0060] The preset mapping relationship between resonant frequency and temperature refers to a dataset or function model established through prior experimental calibration to describe the one-to-one correspondence between the resonant frequency of a passive sensor and the temperature of its location (i.e., the temperature of the set part of the motor rotor). This mapping relationship can be obtained by performing temperature cycling tests on the passive sensor in a temperature chamber and recording its resonant frequency at different temperature points, ultimately forming a lookup table or fitting a mathematical expression.

[0061] In some specific embodiments, the mapping relationship between resonant frequency and temperature can be a calibration data table stored in a database or a fitting formula. After obtaining the resonant frequency, the corresponding temperature value can be uniquely determined by looking up the table or by calculation.

[0062] By mapping the resonant frequency to temperature, the identified frequency value is directly converted into a physical temperature value without the need for complex real-time calculations. This method has the advantages of fast conversion speed, high accuracy, and good reliability, achieving efficient and high-precision motor rotor temperature detection.

[0063] In some embodiments, such as Figure 4 As shown, the data processing method for the motor rotor may further include steps S401, S402, and S403.

[0064] Step S401: Based on the operating conditions of the motor rotor, a preset multi-physics coupling model is used to predict the theoretical temperature corresponding to multiple set parts of the motor rotor.

[0065] In this application, during the motor design phase, a coupled finite element model containing electromagnetic field, temperature field and structural field is constructed, and the geometry, material, thermal conductivity, thermal expansion coefficient of the material, demagnetization curve of permanent magnet, and other data of motor rotor are used as known parameters of the model as a multiphysics coupling model.

[0066] Theoretical temperature refers to the temperature value that a passive sensor should exhibit at its installation location, predicted numerically by a multiphysics coupling model under given operating conditions and current model parameters. Typically, theoretical values ​​represent a simulation and prediction of the physical world by the model, and their accuracy depends on the completeness of the model itself.

[0067] Specifically, after receiving the operating conditions, the multiphysics coupling model calculates the predicted temperature distribution of various parts of the motor rotor (including the parts where passive sensors are located) by solving the coupling equations of the electromagnetic field and the temperature field, and extracts the temperature values ​​of each part where passive sensors are located from the predicted temperature distribution as the theoretical temperatures corresponding to multiple set parts of the motor rotor.

[0068] Step S402: Based on the error between the temperature of the measuring point corresponding to each of the multiple set parts and the theoretical temperature, correct the model parameters so that the error converges to the preset error threshold.

[0069] Correcting model parameters to bring the error to a preset error threshold is a feedback-based control optimization process. By comparing the error between the measured temperature and the theoretical temperature, the internal parameters of the multiphysics coupling model are adjusted in reverse so that the model's output approximates the actual situation as closely as possible.

[0070] Specifically, the algorithm for correcting model parameters can be the least squares method, gradient descent method, or extended Kalman filter. Such algorithms are driven by the error between the measured temperature and the theoretical temperature, and automatically adjust the key unknown or variable parameters in the model (such as equivalent thermal conductivity, contact thermal resistance, loss density proportionality coefficient, etc.) until the error is reduced to a pre-set acceptable range (e.g., ±1.5°C).

[0071] Step S403: Use the corrected multiphysics coupling model to determine the global temperature field distribution of the motor rotor.

[0072] The global temperature field distribution can be understood as a physical field distribution that describes the numerical magnitude of temperature at every point within the entire three-dimensional volume of the motor rotor at a specific moment. It is not a single temperature value, but a complete set containing the correspondence between temperature and all locations in space. Compared to the discrete and finite number of data points for measuring point temperature, the global temperature field distribution is a continuous and complete temperature map that depicts the thermal state of the entire motor rotor.

[0073] Specifically, the global temperature field distribution is a set of three-dimensional temperature data covering the entire set of motor rotor models calculated by the multiphysics coupling model after absorbing the temperature of a limited number of measurement points as spatial constraints and correction benchmarks. This set of data can be visualized as a color temperature cloud map or as a data file containing the temperature values ​​of all grid nodes.

[0074] By acquiring the temperature field distribution across the entire range, the thermal state at any location inside the motor rotor can be determined, completely overcoming the monitoring blind spot problem caused by the inability to deploy sensors across the entire range, thus achieving panoramic visualization of the motor rotor's thermal state.

[0075] In some embodiments, such as Figure 5 As shown, the data processing method for the motor rotor may also include steps S501 and S502.

[0076] Step S501: Determine the thermal stress distribution of the motor rotor based on the global temperature field distribution and the material expansion coefficient of the motor rotor.

[0077] The coefficient of thermal expansion is a physical parameter that describes the proportional change in length or volume of a solid substance when its temperature changes. Generally, the coefficient of thermal expansion is divided into linear expansion coefficient (describing length changes) and volumetric expansion coefficient (describing volume changes), and it is one of the fundamental thermophysical properties inherent in materials. Different materials (such as silicon steel sheets, permanent magnets, and aluminum alloy end rings) have different coefficients of thermal expansion.

[0078] When a temperature field is applied to the motor rotor, the multiphysics coupling model can use the material expansion coefficient to calculate the thermal strain of each component caused by temperature changes, thereby determining the thermal stress distribution of the motor rotor based on the global temperature field distribution.

[0079] Step S502: Determine the probability of demagnetization risk of the motor rotor based on the global temperature field distribution and the demagnetization curve of the motor rotor.

[0080] The curve showing the relationship between the magnetic flux density of the permanent magnet material in an electric motor rotor and the decrease in the strength of the reverse magnetic field at a specific temperature is called the demagnetization curve. The shape and position of the demagnetization curve change with temperature; as the temperature rises, the demagnetization curve becomes lower, and the permanent magnet's resistance to demagnetization decreases.

[0081] In this application, the demagnetization curve of the motor rotor is a pre-stored database of demagnetization curves measured at different temperature points for the permanent magnets used in the motor rotor. By comparing the real-time temperature of a point on the permanent magnet with the demagnetization curve corresponding to that temperature, the risk of demagnetization can be assessed.

[0082] By introducing the material expansion coefficient, a precise quantitative link is established between purely thermal and mechanical information, thereby accurately predicting the mechanical deformation of the motor rotor caused by heat. At the same time, the accurate prediction of demagnetization risk is achieved by using the global temperature field distribution and demagnetization curve, realizing a multi-dimensional performance evaluation of the motor rotor.

[0083] In some embodiments, such as Figure 6 As shown, the data processing method for the motor rotor may further include steps S601 to S605.

[0084] Step S601: Determine the first evaluation parameter based on the highest temperature value in the global temperature field distribution, the ambient temperature of the motor rotor, and the extreme temperature of the motor rotor.

[0085] Since the installation locations of multiple passive sensors are typically the most representative locations reflecting the state of the motor rotor, the highest temperature value in the overall temperature field distribution is similar to the temperature at each measuring point. The limiting temperature of the motor rotor is related to factors such as the heat resistance rating of the insulation material, motor design, load conditions, and heat dissipation performance, and is usually between 80℃ and 100℃.

[0086] Step S602: Determine the second evaluation parameter based on the probability of demagnetization risk.

[0087] Step S603: Determine the third evaluation parameter based on the maximum thermal stress in the thermal stress distribution and the material yield strength of the motor rotor.

[0088] The yield strength of a material refers to the critical stress value at which a material begins to undergo plastic deformation under the action of external force. It is an important indicator of a material's ability to resist plastic deformation.

[0089] Step S604: Determine the fourth evaluation parameter based on the highest temperature value and the temperature difference between the highest temperature value and the lowest temperature value in the global temperature field distribution.

[0090] Step S605: Perform a weighted summation of the first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter to determine the health index of the motor rotor.

[0091] Evaluation parameters are derived from one or more original physical quantities and are used to quantify a certain characteristic or risk of the evaluated object. Typically, evaluation parameters are obtained by normalizing, dimensionlessizing, or comparing the original physical quantities with thresholds. The aim is to eliminate the influence of dimensions and convert absolute physical quantities into relative values ​​reflecting the degree of good or bad condition.

[0092] Specifically, assuming the highest temperature in the global temperature field distribution is... The temperature difference between the highest and lowest temperatures is The extreme temperature of the motor rotor is The ambient temperature of the motor rotor is The probability of demagnetization is The maximum thermal stress is The yield strength of the material is The formula for calculating the health index (CHI) of the motor rotor is: ; in, As the primary evaluation parameter, it is used to quantify the extent to which the highest temperature value exceeds the safe threshold (extreme temperature), thus avoiding the interference of ambient temperature fluctuations on the health index. This is the second evaluation parameter; This is the third evaluation parameter, used to assess the risks of deformation, cracking, etc. that may occur in the motor rotor structure due to thermal stress; This is the fourth evaluation parameter; , , and These are weight parameters that are dynamically configured based on the motor type.

[0093] By transforming performance indicators with different dimensions and physical meanings into unified evaluation parameters and performing weighted summation, complex information from multiple dimensions can be integrated into a single, intuitive health index, greatly simplifying the decision-making process for status monitoring and achieving efficient fusion of multi-source information and simplified decision-making.

[0094] In some embodiments, the data processing method for the motor rotor further includes the following steps: when the health index is greater than a preset warning threshold, a preset cooling strategy is executed for the motor rotor.

[0095] An early warning threshold is a pre-set critical value for a health index used to trigger different levels of alarms or interventions. Different levels of early warning thresholds can be set to achieve tiered responses.

[0096] For example, when using normalized values ​​from 0 to 1 to represent the health index, the first-level warning threshold can be set to 0.65, and the second-level warning threshold can be set to 0.85. The higher the value, the worse the health status.

[0097] A preset cooling strategy refers to a series of predefined control commands or equipment start-up and shutdown logic designed to reduce the temperature of the motor rotor and prevent heat-related faults from occurring or worsening. Cooling strategies may include reducing input power, enhancing cooling capacity, or a combination of both.

[0098] For example, when the Health Index (CHI) value exceeds the first-level warning threshold, a suggested load reduction command is sent to the motor controller; when the Health Index (CHI) value exceeds the second-level warning threshold, a forced current limiting signal is triggered or an active cooling device is activated.

[0099] By comparing health indices with warning thresholds and automatically executing cooling strategies, closed-loop control from status monitoring to proactive intervention is achieved, changing the traditional lag response mode that relies on manual judgment and operation, thereby realizing the automation and intelligence of motor thermal safety management.

[0100] In some embodiments, the data processing method for the motor rotor further includes the following steps: determining the abnormal state of the motor rotor based on the temperature deviation between the motor rotor and preset parts of each motor rotor of the same type in the motor cluster where the motor rotor is located, and the changing trend of the comprehensive health index of the motor cluster.

[0101] In this cluster, multiple identical motor rotors operate under the same conditions, and the overall health index of the cluster is obtained based on the health index of each identical motor rotor.

[0102] A motor cluster refers to a collection of multiple motors that work together to complete a specific technological task in the same industrial setting (such as a production line or a workshop). Identical motor rotors refer to motor rotors within this cluster that have the same signals, specifications, and design parameters. Typically, under the same or similar process cycles (such as a conveyor speed of 1.5 m / s and a load torque of 35 N·m), these identical motor rotors should theoretically be under similar loads, speeds, and thermal conditions.

[0103] Temperature deviation refers to the difference between the average or median temperature of a preset location on a motor rotor and the corresponding location on other motors of the same type in the motor cluster, under the same operating conditions. The comprehensive health index can be the average or standard deviation of the health indices of all motor rotors in the motor cluster. By comparing the temperature deviations of preset locations on each motor rotor under the same load conditions, and the changing trend of the comprehensive health index, abnormal conditions of individual motor rotors can be identified.

[0104] For example, suppose there are 24 identical 11kW induction motors (a motor cluster) operating on a car assembly line, each with the same torque of 35 N·m and a speed of 1450 rpm. The server continuously calculates the average temperature (106°C) and average CHI (0.52) of the cluster motor rotors. The temperature at the measuring point corresponding to motor 15 is 118°C, significantly higher than the average temperature at the measuring points of all motor rotors in the cluster, showing a temperature deviation of +12°C. Simultaneously, the CHI of motor 15 has been steadily increasing from 0.49 to 0.63 over the past hour, showing a clear deteriorating trend, while the standard deviation of the cluster motor rotor CHI has increased from 0.04 to 0.11. Considering these two factors, the server determines that motor 15 is in an abnormal state and issues an early warning.

[0105] By conducting horizontal comparisons among clusters of motors of the same type, a benchmark is provided for identifying individual motor anomalies, enabling early detection of individuals that have not yet reached the absolute alarm threshold but have already shown performance degradation relative to their peers, thereby achieving sensitive early identification of latent faults.

[0106] It should be understood that, although Figures 2 to 6 The steps in the flowchart are shown sequentially according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Figures 2 to 6 Unless otherwise expressly stated herein, the steps illustrated and other steps involved in the embodiments are not subject to strict order restrictions and may be performed in other orders. Furthermore, at least some steps in the foregoing embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0107] In a second aspect, embodiments of this application provide a data processing system for a motor rotor. The data processing system 700 for the motor rotor includes: a plurality of passive sensors 701, a broadband receiving antenna array 702, and a processor 703.

[0108] Among them, multiple passive sensors 701 are embedded in multiple set parts of the motor rotor and radiate resonant signals through the alternating magnetic field generated by the motor stator winding; a broadband receiving antenna array 702 is used to capture the resonant signals radiated by the multiple passive sensors 701; and a processor 703 is used to execute the steps of the data processing method for the motor rotor as provided in the first aspect of the present application embodiment.

[0109] Specifically, the passive sensor can adopt a dual resonant circuit structure, including a main sensing circuit and a reference compensation circuit. The main sensing circuit consists of a temperature-sensitive ceramic capacitor and a planar inductor, while the reference compensation circuit consists of an insensitive dielectric capacitor and a planar inductor. The size of the passive sensor can be no larger than 3mm × 3mm × 1mm, the operating temperature range is -40℃ to 200℃, and the temperature sensitivity of the resonant frequency is no less than 30kHz / ℃.

[0110] For example, multiple passive sensors adopt a dual resonant circuit structure. The main sensing circuit consists of a BaTiO3 (barium titanate) based temperature-sensitive ceramic capacitor and a planar spiral inductor, while the reference compensation circuit consists of an Al2O3 (alumina) dielectric capacitor and an inductor of the same geometric size. Both circuits are encapsulated in an aluminum nitride ceramic housing to ensure long-term reliable operation under 20,000g centrifugal force and 180℃ high temperature.

[0111] A broadband receiving antenna array refers to a collection of multiple antennas that cover a wide operating frequency range and can quickly switch to multiple different frequency points for signal reception. In some specific embodiments, multiple broadband receiving antennas are arranged circumferentially on the stator side of the motor, so that their operating frequency band covers the resonant frequency range of all passive sensors embedded in the motor rotor, for the purpose of stably and synchronously capturing the resonant signals radiated by all passive sensors.

[0112] A processor is a hardware computing core capable of executing instructions, performing arithmetic and logical operations, and controlling functions. A processor can be a central processing unit (CPU), graphics processing unit (GPU), field-programmable gate array (FPGA), or microcontroller (MCU), etc. In some specific embodiments, a high-performance FPGA chip or a dedicated edge computing module can be integrated into the motor controller, programmed to implement the entire algorithmic process from signal analysis, temperature calculation, model inversion to health index calculation and early warning decision-making.

[0113] By integrating passive sensors, broadband receiving antenna arrays, and processors into a complete system, an end-to-end solution from physical signal perception to intelligent decision output is provided, ensuring that the data processing method for the motor rotor can be reliably and stably executed in a real motor environment, thereby realizing the engineering and system integration of the technical solution.

[0114] In a third aspect, embodiments of this application provide a method for determining the health index of a motor rotor, specifically including: acquiring the real-time operating conditions of the target motor rotor; determining the performance index of the target motor rotor based on the real-time operating conditions of the target motor rotor according to preset correlation data between operating conditions and performance indicators; and determining the health index of the target motor rotor based on the performance indicators of the target motor rotor.

[0115] By using the data processing method for motor rotors described in the first aspect of this application, the mapping relationship between operating conditions and performance indicators can be obtained. After storing the data, for the target motor rotor, only the current real-time operating conditions, such as the speed of the motor rotor and the magnitude of the current, need to be collected. By querying the data of this relationship, the performance indicators of the target motor rotor can be quickly determined, and the health index of the target motor rotor can be calculated.

[0116] By using pre-defined relational data to replace complex online model calculations, the performance evaluation process is transformed from time-consuming numerical solutions to fast queries or simple calculations, greatly reducing the computational load and processor performance requirements. This enables efficient evaluation of the motor rotor state while reducing computational resources.

[0117] In a fourth aspect, embodiments of this application provide a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the data processing method for a motor rotor provided in any embodiment of the first aspect of this application, or the method for determining the health index of a motor rotor provided in any embodiment of the third aspect of this application.

[0118] In some embodiments, the computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data such as the operating conditions of the motor rotor. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the data processing method for the motor rotor or the method for determining the health index of the motor rotor in any embodiment of this document.

[0119] Those skilled in the art will understand that Figure 8The structures shown are merely block diagrams of some structures related to the embodiments of this application and do not constitute a limitation on the computer devices on which the embodiments of this application are applied. Specific computer devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0120] In a fifth aspect, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the data processing method for a motor rotor provided in any embodiment of the first aspect of this application, or the method for determining the health index of a motor rotor provided in any embodiment of the third aspect of this application.

[0121] The computer-readable storage medium may be Figure 8 The computer-readable storage medium in the computer device shown.

[0122] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The aforementioned computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments of this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0124] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A data processing method for an electric motor rotor, characterized in that, The method includes: The temperature of the measuring points corresponding to multiple set parts of the motor rotor is determined based on the resonant signals radiated by multiple passive sensors set at different parts of the motor rotor. Based on the temperature of multiple measured points corresponding to set parts and the operating conditions of the motor rotor, a preset multiphysics coupling model is used to obtain the performance indicators of the motor rotor and determine the correlation data between the operating conditions and the performance indicators; the operating conditions include the rotational speed and current of the motor rotor; the performance indicators include the global temperature field distribution, thermal stress distribution and demagnetization risk probability of the motor rotor. Based on the performance indicators, the health index of the motor rotor is determined.

2. The method according to claim 1, characterized in that, The step of determining the temperature at multiple designated locations on the motor rotor based on the resonant signals radiated by multiple passive sensors located at different parts of the motor rotor includes: Spectral analysis was performed on the resonant signals radiated by multiple passive sensors to obtain the resonant frequencies corresponding to each resonant signal. Based on the preset mapping relationship between resonant frequency and temperature, the temperature of the measuring points corresponding to multiple set parts of the motor rotor is determined.

3. The method according to claim 1, characterized in that, Based on the temperature measurements at multiple designated locations and the operating conditions of the motor rotor, a preset multiphysics coupling model is used to obtain the performance indicators of the motor rotor, including: Based on the operating conditions of the motor rotor, the theoretical temperature corresponding to multiple set parts of the motor rotor is predicted using the preset multi-physics coupling model. Based on the error between the temperature at each of the measured points corresponding to multiple set parts and the theoretical temperature, the model parameters are corrected so that the error converges to a preset error threshold. The global temperature field distribution of the motor rotor is determined by using a corrected multiphysics coupling model.

4. The method according to claim 3, characterized in that, The method further includes: Based on the global temperature field distribution and the material expansion coefficient of the motor rotor, the thermal stress distribution of the motor rotor is determined; The probability of demagnetization risk of the motor rotor is determined based on the global temperature field distribution and the demagnetization curve of the motor rotor.

5. The method according to claim 1, characterized in that, Determining the health index of the motor rotor based on the performance indicators includes: The first evaluation parameter is determined based on the highest temperature value in the global temperature field distribution, the ambient temperature of the motor rotor, and the extreme temperature of the motor rotor. The second evaluation parameter is determined based on the demagnetization risk probability. The third evaluation parameter is determined based on the maximum thermal stress in the thermal stress distribution and the material yield strength of the motor rotor; The fourth evaluation parameter is determined based on the highest temperature value and the temperature difference between the highest temperature value and the lowest temperature value in the global temperature field distribution. The first evaluation parameter, the second evaluation parameter, the third evaluation parameter, and the fourth evaluation parameter are weighted and summed to determine the health index of the motor rotor.

6. The method according to claim 1, characterized in that, The method further includes: When the health index exceeds a preset warning threshold, a preset cooling strategy is executed on the motor rotor.

7. The method according to claim 1, characterized in that, The method further includes: The abnormal state of the motor rotor is determined based on the temperature deviation between the motor rotor and preset parts of multiple identical motor rotors in the motor cluster, as well as the changing trend of the comprehensive health index of the motor cluster; wherein, the multiple identical motor rotors in the motor cluster operate under the same conditions, and the comprehensive health index of the motor cluster is obtained based on the health index of each identical motor rotor.

8. A data processing system for an electric motor rotor, characterized in that, The system includes: Multiple passive sensors are embedded in multiple designated locations on the motor rotor and radiate resonant signals through the alternating magnetic field generated by the motor stator windings. A broadband receiving antenna array is used to capture the resonant signals radiated by the multiple passive sensors; A processor for performing the steps of the method as described in any one of claims 1 to 7.

9. A method for determining the health index of a motor rotor, characterized in that, The method includes: The real-time operating conditions of the target motor rotor are obtained; the real-time operating conditions include at least the rotational speed and current of the target motor rotor. Based on the preset correlation data between operating conditions and performance indicators, and based on the real-time operating conditions of the target motor rotor, the performance indicators of the target motor rotor are determined; the correlation data is obtained using the data processing method for motor rotors as described in any one of claims 1 to 7; the performance indicators include the global temperature field distribution, thermal stress distribution, and demagnetization risk probability of the target motor rotor. Based on the performance indicators of the target motor rotor, the health index of the target motor rotor is determined.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7 or claim 8.

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