System and method for detecting torque performance of torque motor

Abstract

The invention relates to the technical field of torque motors, in particular to a torque performance detection system and method for a torque motor. The method comprises the following steps: evaluating the initial performance of the motor by acquiring object data of the torque motor and acquiring structural data; predicting a torque fluctuation dynamic overrun condition according to the initial performance of the motor, further evaluating an electromechanical coupling chain scission effect, and calculating a dynamic thermal load overload degree; evaluating the abnormal condition of the motor, analyzing the operation balance attenuation trend of the motor, and carrying out optimization processing based on the performance attenuation condition and the aging fault probability of the motor; according to the invention, the torque motor is optimized, so that the operation efficiency of the torque motor is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of torque motors, and in particular to a torque performance detection system and method for a torque motor. Background Art

[0002] Torque motor torque performance testing primarily involves real-time monitoring and analysis of torque motor parameters such as torque output, vibration, and temperature to assess operating status and performance changes. Motor performance testing technology based on data acquisition and analysis has gradually matured and has been widely applied, particularly in motor health monitoring and fault diagnosis. In recent years, torque performance testing methods incorporating technologies such as the Internet of Things, big data analytics, and machine learning have replaced traditional mechanical testing methods, improving detection accuracy and efficiency. Existing testing methods mostly rely on traditional physical models and basic parameters such as torque and speed for analysis. These methods lack comprehensive awareness and in-depth analysis of the complex operating conditions of torque motors, particularly under conditions of large load fluctuations, and are unable to accurately capture subtle performance fluctuations. Existing monitoring systems often focus on fault early warning but are limited in predicting long-term operating trends and analyzing performance degradation. Traditional torque motor performance testing suffers from inaccurate analysis of torque motor performance degradation trends and aging trends. Summary of the Invention

[0003] Based on this, it is necessary to provide a torque motor torque performance detection system and detection method to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for detecting torque performance of a torque motor includes the following steps:

[0005] Step S1: acquiring torque motor object data; collecting torque motor structural data according to the torque motor object data; and evaluating the initial performance of the torque motor based on the torque motor structural data;

[0006] Step S2: detecting a motor torque dynamic fluctuation exceeding a limit condition based on the initial performance of the torque motor; evaluating an electromechanical coupling disconnection effect of the torque motor based on the motor torque dynamic fluctuation exceeding a limit condition; and estimating a degree of dynamic thermal load overload of the motor based on the electromechanical coupling disconnection effect of the torque motor;

[0007] Step S3: evaluating the abnormal condition of the torque motor according to the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor; and evaluating the operating balance attenuation trend of the torque motor according to the abnormal condition of the torque motor;

[0008] Step S4: estimating the performance attenuation of the torque motor based on the torque motor operation balance attenuation trend; calculating the aging failure probability of the torque motor based on the torque motor performance attenuation; performing torque motor optimization processing based on the aging failure probability of the torque motor to obtain torque motor optimization data.

[0009] This invention acquires torque motor object data and uses this data to evaluate the motor's initial performance, ensuring accurate and targeted testing. Structural data collection and analysis provide a comprehensive understanding of the motor's operating status, providing accurate baseline data for subsequent performance prediction and adjustment. The dynamic thermal overload degree calculation module predicts the motor's dynamic torque fluctuation overrun, effectively identifying abnormal load conditions encountered during actual operation and further assessing the motor's electromechanical coupling scission effect. Calculating the electromechanical coupling scission effect accurately predicts the interactions between the motor's internal components, identifying potential failure risks early, effectively reducing the probability of failure, and optimizing maintenance cycles. The operating balance decay trend assessment module combines the motor's dynamic thermal overload degree and electromechanical coupling scission effect to assess abnormal motor conditions, accurately identifying balance decay issues that may occur during long-term operation. This assessment helps predict whether the motor will experience operational imbalance, allowing timely adjustments to the motor's operating state to avoid excessive wear or performance degradation. The torque motor optimization processing module predicts motor performance decay and calculates the probability of aging failures, providing a specific optimization solution for the motor. Based on these data, necessary maintenance or adjustments can be performed on the motor before a fault occurs, thereby extending its service life and reducing unnecessary maintenance costs. In summary, this system can monitor the performance changes of the motor in real time during operation through multi-level detection and optimization, effectively reduce the probability of faults, and improve the operating stability and efficiency of the motor. Therefore, the present invention is an optimization of the traditional torque motor torque performance detection, which solves the problem of inaccurate analysis of the torque motor performance attenuation trend and the problem of inaccurate analysis of the torque motor aging trend in the traditional torque motor torque performance detection method, and improves the accuracy of the torque motor performance attenuation trend and the accuracy of the torque motor aging trend analysis.

[0010] The present invention further provides a torque motor torque performance detection system for executing the torque motor torque performance detection method described above, the torque motor torque performance detection system comprising:

[0011] An initial performance evaluation module is used to obtain torque motor object data; collect torque motor structure data according to the torque motor object data; and evaluate the initial performance of the torque motor based on the torque motor structure data;

[0012] The dynamic thermal load overload degree calculation module is used to detect the motor torque dynamic fluctuation exceeding the limit condition based on the initial performance of the torque motor; evaluate the electromechanical coupling disconnection effect of the torque motor based on the motor torque dynamic fluctuation exceeding the limit condition; and estimate the motor dynamic thermal load overload degree based on the electromechanical coupling disconnection effect of the torque motor;

[0013] The operation balance attenuation trend assessment module is used to assess the abnormal condition of the torque motor based on the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor; and to assess the operation balance attenuation trend of the torque motor based on the abnormal condition of the torque motor;

[0014] The torque motor optimization processing module is used to estimate the torque motor performance degradation based on the torque motor operation balance degradation trend; calculate the torque motor aging failure probability based on the torque motor performance degradation; and perform torque motor optimization processing based on the torque motor aging failure probability to obtain torque motor optimization data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic flow chart of the steps of a method for detecting torque performance of a torque motor;

[0016] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0017] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0020] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0021] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0022] To achieve this, please refer to Figures 1 to 3 A method for detecting torque performance of a torque motor comprises the following steps:

[0023] Step S1: acquiring torque motor object data; collecting torque motor structural data according to the torque motor object data; and evaluating the initial performance of the torque motor based on the torque motor structural data;

[0024] In an embodiment of the present invention, basic operating data of the torque motor object is collected by constructing a real-time data acquisition link connected to the torque motor. The data acquisition link includes a high-precision current transformer, a voltage sensor, and an angle encoder module for electrical quantity acquisition. The sampling period set by the system is 1ms, covering basic data such as current, voltage, speed, angle, torque feedback signal, etc. in the initial stage of motor operation. After the torque motor object data is collected, it is uploaded through the integrated CAN bus communication interface and input into the edge processor for analysis and processing. The key data extracted by the analysis module include the initial excitation current waveform characteristics, rotor position information, torque feedback fluctuation amplitude, and power supply end voltage stability range. Based on the acquisition of the original operating data, the torque motor structural data identification stage is entered. The structural data is composed of the static structural parameters provided by the motor manufacturer and the structural deformation parameters dynamically deduced in the online operation state. Static structural parameters include the number of stator winding turns, the number of rotor poles, the type of permanent magnet material, the length of the heat dissipation path, and the shape and dimensions of the stator core. Dynamic structural parameters are extracted using an electromagnetic coupling analysis method to calculate the changing trend of the stator-rotor air gap during actual operation, taking into account the influence of thermal expansion and electromagnetic stress coupling on the air gap width. During this stage, a time-domain difference method based on an edge computing array is used to analyze the deformation trends caused by stator and rotor temperature rise, further revising the structural parameters. After obtaining the structural data, the initial performance of the torque motor is evaluated. This evaluation is conducted based on two dimensions: steady-state torque output consistency at rated input voltage and dynamic response consistency during startup. The former is achieved by continuously applying the rated voltage and measuring the mean and standard deviation of the stable torque output range. The latter is achieved by inputting an excitation pulse and recording the torque rise time, response delay time, and speed ramp curve characteristics. The initial performance evaluation value of the torque motor is obtained and output as a five-tuple data {T_mean, T_std, τ_rise, Δt_delay, N_max}, which respectively correspond to the steady-state torque mean, torque stability standard deviation, torque response rise time, delay time, and peak speed, and the initial performance of the torque motor is obtained.

[0025] Step S2: detecting a motor torque dynamic fluctuation exceeding a limit condition based on the initial performance of the torque motor; evaluating an electromechanical coupling disconnection effect of the torque motor based on the motor torque dynamic fluctuation exceeding a limit condition; and estimating a degree of dynamic thermal load overload of the motor based on the electromechanical coupling disconnection effect of the torque motor;

[0026] In an embodiment of the present invention, after completing the initial performance evaluation, the torque dynamic fluctuation state of the torque motor is monitored. Torque dynamic fluctuation detection is implemented using a combination of a high-frequency data sampling array and a fast Fourier transform module. 1000 sets of continuous torque data acquisition are completed within 10ms, and the high-frequency fluctuation components are identified by frequency domain analysis. In the specific operation, it is defined that when there is a subharmonic component with an amplitude higher than 1kHz exceeding 20% ​​of the fundamental frequency in the frequency domain energy distribution, it is considered that the torque dynamic fluctuation over-limit state is established. After establishing the torque fluctuation over-limit identification, a cross-comparison is performed with the structural data in step S1 to evaluate whether the source of the fluctuation is due to the breakage of the electromechanical coupling chain or the weak coupling effect. The electromechanical coupling break effect evaluation is achieved by the following method: using the synchronous delay analysis between the stator flux change rate and the rotor response torque. If there is a response misalignment time between the two that exceeds the specified threshold (such as 1.5ms), it can be deduced that there is a partial break trend in the system. Secondly, the asymmetry of the air gap magnetic flux density is detected. The magnetic flux density sensor array arranged in the air gap area provides a 360° distribution signal. If the magnetic flux density is significantly low (20% lower than the mean value) within a range of more than 5°, it further supports the chain break judgment. After the chain break effect is identified, the dynamic thermal load overload assessment stage begins. This stage is based on a comprehensive deduction of three parameters: operating temperature, ventilation and heat dissipation efficiency, and thermal resistance of the heat conduction path. The temperature rise curves of the stator winding and the core are collected. If the temperature rise slope increases abnormally (such as the slope increases by more than 0.5°C / s) under unchanged operating load conditions, it is considered a nonlinear increase in thermal load. The thermal resistance value of the stator-shell heat conduction path in the thermal resistance path is then calculated. Based on the material thermal conductivity and contact area, the heat transfer efficiency per unit time is derived. If the difference between the actual temperature rise and the theoretical temperature rise is found to be greater than 10%, it is determined that there is a dynamic thermal load overload trend. The thermal load overload degree data is output.

[0027] Step S3: evaluating the abnormal condition of the torque motor according to the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor; and evaluating the operating balance attenuation trend of the torque motor according to the abnormal condition of the torque motor;

[0028] In an embodiment of the present invention, after obtaining the data on the degree of dynamic thermal load overload and electromechanical coupling disconnection effect of the motor, a comprehensive assessment of the abnormal condition of the torque motor is carried out. The abnormality assessment uses state entropy difference analysis as the core means to construct an entropy mapping model between multiple key operating parameters. Specifically, a four-dimensional state mapping table is established with four variables, namely, torque fluctuation coefficient, rotor angle drift, temperature rise rate, and magnetic flux synchronization misalignment time. By comparing the entropy value of each parameter in the operating window with the entropy value of the reference state, if the total entropy increment exceeds the threshold value of 0.7, it is judged that the torque motor is in an abnormal state. After completing the abnormality identification, the operating balance attenuation trend is further analyzed. Operating balance is mainly defined as torque output consistency, energy consumption stability, and vibration interference suppression capability. The balance degradation trend is assessed using a three-step method. First, the torque output curve is integrated per unit time to extract the actual work curve. This is then compared with the input power to calculate the energy utilization rate η. If η continues to decrease at a rate exceeding 3% / h, it is marked as a balance drop. Second, the harmonic components of the current waveform are extracted, specifically analyzing the amplitude trends of the fifth and seventh harmonics. An amplitude increase exceeding 10% is marked as a magnetic disturbance increase. Third, vibration data measured using a triaxial accelerometer is used. If a random shock peak exceeding 2g is detected in the 200Hz frequency band, the balance state is considered to have been disrupted by physical disturbance. The output balance degradation trend is quantified as a triplet {η_drop, Harmonic_rise, Vib_peak}, corresponding to the energy efficiency drop rate, harmonic increase amplitude, and vibration peak.

[0029] Step S4: estimating the performance attenuation of the torque motor based on the torque motor operation balance attenuation trend; calculating the aging failure probability of the torque motor based on the torque motor performance attenuation; performing torque motor optimization processing based on the aging failure probability of the torque motor to obtain torque motor optimization data.

[0030] In this embodiment of the present invention, based on the previous stage's operational balance attenuation trend data, the torque motor's performance degradation is further estimated. This process is accomplished using a time series trend extrapolation method. Within a fixed sampling period, data such as the η value, harmonic amplitude, and vibration peak value are recorded for multiple consecutive operating cycles, and the first-order and second-order rates of change are calculated using the differential method. Each parameter is fitted into a trend line in the time series, and the trend is extrapolated over the next 10 hours. If the slope of the trend line exceeds a predetermined threshold (e.g., the η decline rate is greater than 0.5% / h), the performance is considered to be at risk of continued degradation. After obtaining the performance degradation trend data, the aging failure probability calculation phase begins. This phase utilizes a multi-parameter segmented statistical method to divide the historical operating data into multiple intervals and calculate the actual fault frequency within each performance interval. The fault probability calculation is based on an operating state weighted scoring mechanism. Three sets of parameters, {η_drop, Harmonic_rise, and Vib_peak}, are used to match operating states with similar characteristics in historical samples. The corresponding fault frequencies are accumulated to calculate the aging failure probability under the current operating state. The torque motor is optimized based on the probability of aging failure. This optimization process includes adjusting the excitation input strategy, reconfiguring the current limit, and improving the cooling path efficiency. In this embodiment, the excitation pulse input method is preferentially adjusted to shorten the rotor response time. Simultaneously, the cooling fan speed is increased from 2800 rpm to 3400 rpm. The output current limit is reconfigured via the PLC from the original 15A to 12.5A. After optimization, the operating status data is recollected, and the analysis process from steps S1 to S3 is repeated to obtain updated operating status data, which is then output as an optimized data set.

[0031] Preferably, step S1 includes the following steps:

[0032] Step S11: acquiring torque motor object data;

[0033] In an embodiment of the present invention, torque motor object data is obtained. The basic parameters of the torque motor are collected through an industrial automation data acquisition system, including the motor's rated power, rated torque, rated voltage, rated speed, load characteristics, and operating environment data. Specifically, high-precision sensors are used to monitor the motor's current, voltage, temperature, vibration and other parameters in real time, and these data are recorded in real time through a data acquisition module. The motor's environmental information (such as operating temperature, humidity, pressure, etc.) is also provided by environmental monitoring equipment. Through the collection of these data, the working status of the motor can be fully understood, providing accurate basic data for subsequent performance evaluation and analysis.

[0034] Step S12: collecting torque motor structure data based on the torque motor object data;

[0035] In an embodiment of the present invention, the torque motor structural data is collected based on the torque motor object data. After obtaining the basic working data of the torque motor, the structural parameters of the motor are collected through a digital design platform. This process uses precision instruments such as 3D scanners to obtain the geometric data of the motor, such as rotor radius, stator size, winding configuration, bearing design, magnetic circuit design and other structural data. According to the CAD model, combined with the mechanical analysis method, the structural characteristics of the motor are analyzed in detail to ensure that these structural data have high precision and integrity. The collected structural data will provide a theoretical basis for the evaluation and optimization of motor performance. In this step, the focus is on real-time or regular collection of the motor's structural data through sensors and automated measurement tools to ensure the real-time and accuracy of the data.

[0036] Step S13: Evaluating the motor aerodynamic state according to the torque motor structure data;

[0037] In an embodiment of the present invention, the aerodynamic state of the motor is evaluated based on the structural data of the torque motor. After the structural data of the torque motor is collected, the aerodynamic characteristics of the motor are evaluated using a fluid dynamics simulation tool. The specific steps are to use the finite element analysis method to simulate and analyze the air flow of components such as the motor housing, rotor, and stator, and calculate factors such as air resistance, windage effect, and ventilation efficiency. Based on the working environment and speed range of the motor, its aerodynamic state under different working conditions is evaluated. By simulating the working effect of the air cooling or liquid cooling system, the heat distribution inside and outside the motor is calculated, and the heat dissipation design of the motor is optimized to ensure that its performance does not degrade due to overheating under high-load operation. The entire evaluation process relies on accurate fluid mechanics models and simulation tools to ensure accurate prediction of the aerodynamic state of the motor.

[0038] Step S14: Evaluate the initial performance of the torque motor based on the torque motor structural data and the motor aerodynamic state.

[0039] In an embodiment of the present invention, the initial performance of the torque motor is evaluated based on the torque motor structural data and the motor aerodynamic state. After completing the evaluation of the motor structural data and aerodynamic state, a mechanical analysis method is used to comprehensively evaluate the initial performance of the torque motor. Based on the motor's geometric structure data, winding parameters, and magnetic circuit design, the motor's basic performance indicators such as theoretical output torque, power, and efficiency are calculated. Combined with the aerodynamic evaluation results, the impact of the motor's heat dissipation capacity on its thermal load is analyzed, thereby further inferring the actual performance of the motor under different working conditions. By comparing simulation data with actual operating data, the motor's operating efficiency and torque stability are determined, and it is evaluated whether the motor meets the design requirements.

[0040] Preferably, step S13 includes the following steps:

[0041] Step S131: extracting torque motor rotor structure data according to the torque motor structure data;

[0042] In an embodiment of the present invention, the torque motor rotor structure data is extracted based on the torque motor structure data. In this step, the detailed structural parameters of the rotor part are extracted through the CAD model or three-dimensional scanning data of the torque motor, and the geometric shape of the rotor and its key features, including the diameter, length, bearing position, magnet configuration, etc. of the rotor are accurately obtained through precision measuring tools such as laser scanners, three-dimensional CT scanning or three-dimensional modeling software. By comparing with the motor structure design drawing, the various dimensions, materials and processing technology of the rotor design are confirmed. Specifically, parameters such as the rotor axis, winding position, and rotor pole pair number will be accurately extracted. These data will provide a basis for the subsequent calculation of the rotor rotation radius, air shear force and aerodynamic state evaluation.

[0043] Step S132: Calculating the motor rotor rotation radius data according to the torque motor rotor structure;

[0044] In an embodiment of the present invention, the rotation radius data of the motor rotor is calculated based on the torque motor rotor structure. After obtaining the rotor structure data, the rotation radius of the rotor is calculated using a geometric analysis method. In the specific calculation process, the distance from the center to the outer edge of the rotor, that is, the rotation radius of the rotor, is calculated based on the design of the rotor winding and the physical characteristics of the rotor. For motors with multiple rotor poles, the rotation radius of each rotor pole will be calculated one by one, and the key parameters affecting the rotor performance, such as the rotor air gap, magnetic flux distribution, and torque output, will be determined. This process needs to be combined with the physical parameters of the motor rotor, such as material density and magnetic permeability, as well as the operating conditions of the motor, to ensure the accuracy of the rotor rotation radius.

[0045] Step S133: identifying the internal connectivity of the torque motor based on the torque motor rotor structure;

[0046] In an embodiment of the present invention, the internal connectivity of the torque motor is detected based on the torque motor rotor structure. In this step, the connectivity of different components inside the torque motor is detected through structural simulation analysis or physical measurement. Focus on the connectivity of the electrical connection, mechanical connection and airflow channel between the rotor and the stator. Non-destructive testing technologies such as X-ray imaging and ultrasonic testing are used to check the tightness and fit of the internal components of the motor, especially the rotor, stator, bearings and connecting parts. By analyzing the size of the air gap between the rotor and the stator and the contact between the windings, it is detected whether the motor has poor connectivity, resulting in uneven distribution of airflow and current inside the motor.

[0047] Step S134: Calculating the air shear force of the torque motor using the electronic rotor rotation radius data and the internal connectivity of the torque motor;

[0048] In an embodiment of the present invention, the air shear force of the torque motor is identified using the electronic rotor rotation radius data and the internal connectivity of the torque motor. In this step, the shear force between the motor rotor and the air is identified using a fluid dynamics model in combination with the rotor rotation radius data and the internal connectivity of the motor obtained in the previous step. Through numerical simulation, the CFD (computational fluid dynamics) analysis method is applied to simulate the changes in air flow when the rotor rotates. Focus on analyzing the interaction between the rotor surface and the air, and calculate the shear force between the rotor and the air under different speeds and load conditions. The magnitude of the shear force is closely related to factors such as the rotor radius, speed, airflow velocity and gas density. Based on the airflow shear force generated when the rotor rotates, the potential impact of the motor aerodynamic state on the torque output is identified.

[0049] Step S135: estimating the increase degree of the motor air resistance according to the air shear force of the torque motor;

[0050] In an embodiment of the present invention, the degree of growth of the air resistance of the motor is estimated based on the air shear force of the torque motor. In this link, based on the air shear force data, the growth trend of the motor air resistance is further predicted through fluid mechanics simulation. Air resistance is mainly affected by factors such as motor speed, rotor design and air density. By calculating the air resistance of the motor under different working conditions and combining the working environment data of the torque motor, a resistance growth model is established. The model is used to analyze the growth trend of air resistance with running time, temperature changes and load changes, and calculate the impact of resistance on motor efficiency and performance. The calculated resistance value will be used to optimize the design of the motor and reduce the negative impact of air resistance on motor performance.

[0051] Step S136 : Evaluate the motor aerodynamic state based on the motor air resistance growth degree and the torque motor air shear force.

[0052] In an embodiment of the present invention, the aerodynamic state of the motor is evaluated based on the degree of increase in the motor's air resistance and the air shear force of the torque motor. By combining the degree of increase in the motor's air resistance with the shear force data, the overall aerodynamic state of the torque motor is evaluated. This process uses CFD analysis tools to analyze whether the motor has problems such as overheating and performance degradation during operation, based on the airflow characteristics and air resistance changes when the motor rotor rotates, and comprehensively considers the rotor structure, stator air gap, and the cooling method of the motor. By monitoring the actual operating data of the motor, the airflow channel and the heat dissipation system are adjusted in real time to ensure that the motor maintains good aerodynamic performance under different working conditions. Based on the simulation results and measured data, the aerodynamic state of the motor is evaluated and its changing trend in long-term operation is predicted.

[0053] Preferably, step S14 includes the following steps:

[0054] Step S141: when the internal pressure is between 3.2 Pa and 4 Pa, the motor aerodynamic state is used to detect airflow restriction inside the motor;

[0055] In an embodiment of the present invention, the aerodynamic state of the motor is used to detect the airflow restriction inside the motor when the internal pressure is 3.2Pa to 4Pa. In this step, based on the internal airflow data of the motor, the aerodynamic state is monitored to evaluate whether the airflow of the motor is restricted, and the air pressure sensor and flow meter are used to monitor the airflow and air pressure inside the motor in real time. The pressure distribution inside the motor when it is working is obtained through the data collected by the sensor. In particular, when the pressure range is 3.2Pa to 4Pa, the flow rate and resistance value of the airflow channel are measured. The degree of airflow restriction is calculated by comparing the flow rate of the airflow with the internal pressure data of the motor. When the flow rate of the airflow is significantly lower than the expected value, it indicates that the airflow is restricted, affecting the heat dissipation performance and working efficiency of the motor.

[0056] Step S142: When the airflow restriction inside the motor is 25%, the heat dissipation capacity of the torque motor is evaluated;

[0057] In an embodiment of the present invention, the heat dissipation capacity of the torque motor is evaluated when the airflow restriction inside the motor is 25%. In this step, the heat dissipation capacity of the motor is further calculated by combining the evaluation results of the airflow restriction. When the airflow restriction reaches 25%, it means that the airflow cannot effectively pass through the inside of the motor, resulting in reduced heat dissipation efficiency. Thermal sensors and temperature monitoring instruments are used to monitor the temperature changes of the motor in real time. The thermal load of the motor is analyzed using key data such as the motor housing temperature, rotor temperature, and stator temperature. The heat dissipation capacity of the motor under such airflow restriction is evaluated in combination with the motor's power output, load conditions, and design parameters of the heat dissipation system. When the heat dissipation capacity is insufficient, the heat dissipation system of the motor is optimized, such as adding heat sinks or increasing the coolant flow rate, to ensure that the motor can maintain stable operation under high load.

[0058] Step S143: identifying the dynamic motion characteristics of the torque motor based on the torque motor structure data;

[0059] In an embodiment of the present invention, the dynamic motion characteristics of the torque motor are identified based on the torque motor structural data. In this step, based on the structural data of the motor, such as the rotor, stator, bearing and magnet configuration, the motion characteristics of the motor are identified through dynamic analysis, the dynamic characteristics of the motor are modeled using the finite element analysis (FEA) method, and the vibration mode, moment of inertia and dynamic response of the motor under different loads are calculated. These parameters reflect the motion behavior of each component of the motor during operation, including the acceleration and deceleration of the rotor and the relative motion between the components. Through the vibration test equipment, the vibration frequency and amplitude of the motor under different working conditions are actually measured, and compared with the theoretical analysis results to confirm the dynamic motion characteristics of the motor.

[0060] Step S144: detecting the smoothness of rotation of the motor components based on the dynamic motion characteristics of the torque motor;

[0061] In an embodiment of the present invention, the degree of smoothness of rotation of motor components is detected based on the dynamic motion characteristics of the torque motor. The smoothness of rotation of motor components is evaluated by analyzing the dynamic motion characteristics of the motor and combining the sensor detection data. Accelerometers, vibration sensors and other equipment are used to monitor the vibration amplitude and frequency of each component during the operation of the motor in real time, especially the fit between the rotor and the stator. The smoothness of the motor during operation is determined by monitoring the gap, friction and imbalance that occur during the rotation process. Excessive vibration amplitude or abnormal frequency indicates that there are asymmetric loads or manufacturing tolerance problems between motor components. This step can also be combined with vibration analysis software to identify imbalance or friction problems in the rotation of motor components through spectrum analysis, and make adjustments based on the analysis results to ensure the stability of the motor during operation.

[0062] Step S145: collecting torque parameters of motor components according to the torque motor structure data;

[0063] In an embodiment of the present invention, the torque parameters of the motor components are collected based on the torque motor structural data. In this step, based on the structural data of the torque motor, the torque parameters of each motor component are collected, and the torque output of each component is monitored in real time by means of torque sensors installed on each key component of the motor. The data collected by the sensor will be used to calculate the torque distribution of components such as the rotor, stator, and bearings. Under different operating conditions of the motor, the torque output between the rotor and stator will change, and the torque parameters under different operating conditions are dynamically collected. In addition, the accuracy of the torque sensor is calibrated to ensure the accuracy of data collection. The collected data will provide an important basis for subsequent stability evaluation, especially the torque changes under high load or high-speed operation.

[0064] Step S147: evaluating the initial stability of the torque motor according to the torque parameters of the motor components and the rotation smoothness of the motor components;

[0065] In an embodiment of the present invention, the initial stability of the torque motor is evaluated based on the torque parameters of the motor components and the rotation smoothness of the motor components. The initial stability of the motor is evaluated through mathematical modeling and analysis in combination with the torque parameters and rotation smoothness of the various components of the motor. The stability of the motor is analyzed based on the collected torque data, combined with the friction, clearance and assembly accuracy between the motor components. The uneven rotation between the motor components leads to torque fluctuations and unstable torque output, affecting the long-term reliability of the motor. By combining and analyzing vibration data, torque data and rotation smoothness, the stability of the motor under different working conditions is evaluated, and the performance of the motor in long-term operation is predicted. This evaluation will provide guidance for subsequent optimization design, especially in improving motor stability and reducing failure rates.

[0066] Step S148: When the heat dissipation capacity of the torque motor exceeds 300 kW and the initial stability of the torque motor is determined, the initial performance of the torque motor is evaluated.

[0067] In an embodiment of the present invention, when the operating heat dissipation capacity of the torque motor exceeds 300kW and the initial stability of the torque motor is evaluated, the initial performance of the torque motor is evaluated. In this step, when the operating heat dissipation capacity of the motor reaches 300kW, the overall performance of the motor is comprehensively evaluated in combination with the initial stability evaluation result of the motor. By measuring the maximum power output, efficiency and various performance indicators of the motor, it is determined whether the motor meets the preset performance standards. If the heat dissipation capacity of the motor is sufficient and the initial stability is good, it indicates that the motor has a good working state and can maintain stability during long-term operation. By comparing and analyzing the various parameters of the motor, the motor design is further optimized to ensure its efficient operation under various working conditions. On this basis, combined with the fault prediction model, the remaining life and failure probability of the motor are predicted to obtain the initial performance of the torque motor.

[0068] Preferably, the motor torque dynamic fluctuation over-limit condition detection in step S2 includes:

[0069] Simulate the torque motor load condition based on the initial performance of the torque motor;

[0070] In this embodiment of the present invention, the initial performance parameters of the torque motor evaluated in step S1 need to be obtained. These parameters include, but are not limited to, the torque-current response curve, rotor mechanical inertia, magnetic circuit equivalent conductance, stator winding resistance, inductance, and the thermal stability time constant corresponding to the rated operating temperature. A motor operation dataset constructed based on dual-channel sampling of static conditions and dynamic disturbances is used to construct a joint input power-torque-temperature rise mapping table for the motor's current state using a three-dimensional matrix. Specifically, a PLC control system applies step load commands consisting of multiple sets of pulse-modulated control signals to the torque motor, and a high-speed dynamic data acquisition module integrated in the control platform collects response data within each cycle. After the response data is transformed and processed, the steady-state and transient state data distributions of the torque motor under each step condition are extracted using the torque motor's instantaneous output torque, input current amplitude, and motor surface and winding temperatures as indices. This simulates the full-cycle load response and constructs a standardized load distribution sequence to obtain a torque motor load condition dataset for subsequent overload determination.

[0071] Detect the rated torque overload degree of the torque motor based on the load condition of the torque motor;

[0072] In an embodiment of the present invention, the output torque value in each sampling period of the load sequence is extracted from the data set, and compared point by point with the rated torque set by the torque motor at the factory. A discrete contrast function is used to set that when the periodic torque value exceeds 10% of the rated value, it is marked as an "overload point", and the number of overload points and the intensity factor are counted in each time segment. The intensity factor is calculated as: (actual torque value-rated torque value) / rated torque value. By continuously analyzing the frequency and intensity of overload points in multiple cycles, the maximum overload amplitude and the number of overload durations in each time period are counted using Excel pivot tables or SQL query commands based on structured data tables to generate a "rated torque overload degree" data table. The output detection data includes three items: the proportion of overload point occurrence, the maximum overload coefficient, and the mean value of the overload coefficient, which are used for subsequent trend analysis of magnetic field enhancement.

[0073] Count the duration of torque motor overload according to the torque motor load condition;

[0074] In an embodiment of the present invention, the acquired load sequence and the identified overload point position are combined to use the time window integration method to realize the statistics of overload duration. The specific method is: set the time sliding window to 50ms, traverse the entire load data sequence, and start the overload timing logic if overload points appear continuously in the window (the torque value is continuously greater than 10% of the rated torque). When the overload point ends, the overload timing of the window is ended, and the duration is recorded and accumulated. Use the logic analysis tool in the Matlab environment or the "groupby" function in Python Pandas to perform batch overload period slicing processing and output the duration data of all overload segments. The formed "load overload duration data set" includes: the overload duration of each segment, the total overload duration, the average overload duration, the maximum single overload duration, etc., which are used for subsequent magnetic field effect deduction operations.

[0075] The torque motor magnetic field enhancement effect is detected by using the torque motor load overload duration and the torque motor rated torque overload degree;

[0076] In an embodiment of the present invention, the load sequence obtained in step 1 and the position of the identified overload point are combined to realize the statistics of the overload duration by using the time window integration method. The specific method is: set the time sliding window to 50ms, traverse the entire load data sequence, and start the overload timing logic if overload points appear continuously in the window (the torque value is continuously greater than 10% of the rated torque). When the overload point ends, the overload timing of the window is ended, and the duration is recorded and accumulated. Use the logic analysis tool in the Matlab environment or the "groupby" function in Python Pandas to perform batch overload period slicing processing and output the duration data of all overload segments. The formed "load overload duration data set" includes: the overload duration of each segment, the total overload duration, the average overload duration, the maximum single overload duration, etc., which are used for subsequent magnetic field effect deduction operations.

[0077] Predicting the magnetic saturation phenomenon of torque motors based on the magnetic field enhancement effect of torque motors;

[0078] In an embodiment of the present invention, motor operating data, including parameters such as motor load, torque, and magnetic field strength, is obtained to predict whether a motor has reached magnetic saturation based on its load and magnetic field data. When a motor is overloaded, increases in load and torque directly affect the motor's internal magnetic field. As the motor load increases, the current also increases, leading to an increase in magnetic flux density, which in turn affects the magnetic field distribution within the motor. When a motor operates for a long time and its load continuously exceeds the rated value, the magnetic flux density within the motor core gradually approaches its saturation point. When magnetic saturation occurs, the magnetic flux density no longer increases linearly with increasing current, resulting in a non-linear increase in the motor's output torque and affecting the motor's torque output stability. By monitoring the motor's load and magnetic field data in real time, the changing trend of the motor's internal magnetic flux density is identified. Using this data, combined with the motor's core material properties, the core's saturation point is calculated. This saturation point is typically determined by the material's magnetic permeability curve and the motor's design parameters. To assess whether a motor has reached magnetic saturation, a detailed analysis of the motor's internal magnetic field strength is performed. When the magnetic field strength approaches saturation, the field enhancement effect becomes less noticeable, and the motor's torque output no longer effectively increases with increasing current. By comparing the set threshold with actual data, it is possible to determine in real time whether the motor is in a critical state of magnetic saturation. If the magnetic field strength exceeds the set safety threshold and the torque output no longer changes with input current, it is predicted that the motor has entered a state of magnetic saturation.

[0079] Estimating the increase in motor torque fluctuation based on the magnetic saturation phenomenon of the torque motor;

[0080] In embodiments of the present invention, the estimated results of the magnetic saturation phenomenon are used to further analyze the increase in motor torque fluctuations. Magnetic saturation causes the motor's torque output to no longer linearly respond to the input current signal. When the magnetic field strength within the motor approaches or exceeds the saturation point, the motor's core can no longer effectively enhance the magnetic flux density, causing the motor's torque output to lose its normal linear relationship. At this point, the motor's torque output is not only affected by load fluctuations but also experiences larger fluctuations. These fluctuations typically have a large frequency and amplitude, far exceeding the expected range during motor design. To monitor this phenomenon, torque sensors are installed during motor operation to collect real-time torque output data. These sensors can accurately measure the motor's torque fluctuations under different load conditions and transmit this data to a data acquisition system for further analysis. By combining data on the degree of magnetic field saturation, the increasing trend of the motor's torque fluctuations can be estimated. When magnetic field saturation occurs, the amplitude of the torque fluctuations typically increases significantly, because the motor's output torque no longer changes linearly but becomes unstable. Using data analysis techniques, particularly frequency and time domain analysis methods, the motor's torque fluctuations are analyzed in detail to extract the frequency, amplitude, and changing trend of the fluctuations. These analysis results reveal increased motor torque ripple and, through correlation with magnetic field saturation, further predict motor stability risks. Increased torque ripple can cause additional vibration loads on the motor's internal mechanical components, impacting not only the motor's operational stability but also the entire electromechanical system, such as increased mechanical wear, mechanical resonance, and electrical system overload.

[0081] Based on the increase of motor torque fluctuation and the magnetic saturation phenomenon of the torque motor, the excessive dynamic fluctuation of the motor torque is detected.

[0082] In an embodiment of the present invention, the data collected in each of the above steps is combined to comprehensively analyze the increase in motor torque fluctuation and magnetic saturation to predict whether the motor torque fluctuation will exceed the design standard. The increase in torque fluctuation is directly related to magnetic saturation. Magnetic saturation causes the motor's magnetic field enhancement effect to weaken, and the motor torque output no longer steadily increases with increasing load. When the motor is under long-term overload operation, the magnetic field enhancement effect gradually increases, and the magnetic flux density inside the motor reaches or exceeds the saturation point of its iron core, thereby causing magnetic saturation. Magnetic saturation causes the motor's torque output to exhibit nonlinear changes, manifested as an increase in the amplitude of torque fluctuation. By real-time monitoring of the motor torque fluctuation, the motor's torque variation data under different load and overload conditions is collected and compared with the motor's rated torque to determine whether it is overloaded. On this basis, combined with the motor's load data, overload duration, magnetic field enhancement effect, and magnetic saturation data, a prediction model is constructed to estimate the torque fluctuation that will occur during the motor's future operation. This prediction model, based on the actual operating data of the motor, analyzes the changing trends of torque fluctuations and comprehensively considers factors such as the motor's magnetic saturation, load, and overload duration to determine whether the motor has reached the critical value for excessive torque fluctuations. If the prediction results indicate that the motor's torque fluctuations will exceed the design standard, it means that the motor's stability will be abnormal, its operating efficiency will be significantly reduced, and it may even cause system failures or component damage. In this process, key factors such as the motor's load, the amplitude of the torque fluctuations, the duration of the overload, and the magnetic field enhancement effect all participate in the judgment of the excessive state. By utilizing this comprehensive analysis method, the motor's status can be monitored in real time and potential failure risks can be promptly identified.

[0083] Preferably, the evaluation of the electromechanical coupling disconnection effect of the torque motor in step S2 includes:

[0084] The frequency of 10Hz is used to collect the degree of aggravation of the motor's local vibration when the dynamic fluctuation of the motor torque exceeds the limit;

[0085] In an embodiment of the present invention, a vibration sensor is used to monitor the local vibration of the motor during monitoring of excessive dynamic torque fluctuations. To ensure timely detection of changes in the motor's internal low-frequency vibrations, the vibration sensor is precisely installed at key locations on the motor, typically at the motor's joints or other components that bear significant loads. These locations significantly impact the motor's overall operational stability. The choice of installation location is determined based on the motor's design structure and motion characteristics, ensuring comprehensive monitoring of the motor's vibrations. The sampling frequency of the vibration sensor is set to 10 Hz. This frequency selection effectively captures low-frequency vibration changes that occur during motor operation, avoiding the loss of critical vibration information due to excessively high frequency settings. The sensor collects vibration signals in real time during motor operation. These signals undergo preprocessing and enter the data acquisition system for further recording and analysis. During signal processing, the vibration data is processed through techniques such as filtering and amplification. The collected vibration data is compared with the motor's standard operating data. This comparison determines whether there is vibration amplification under the motor's current operating state. If the motor shows a tendency to significantly increase its vibration amplitude under certain specific operating conditions, it means that there is a potential abnormality in the motor, such as loose mechanical parts, wear or other faults, which in turn affects the stability and operating efficiency of the motor.

[0086] Estimate the overlapping impact of motor parts based on the degree of local vibration aggravation of the motor;

[0087] In an embodiment of the present invention, a vibration analyzer performs spectrum analysis on the collected vibration signal. Spectral analysis converts the vibration signal from the time domain to the frequency domain, revealing the various frequency components contained in the signal. By extracting the frequency components of the vibration signal, it is possible to identify whether there are abnormal frequency fluctuations during the operation of the motor. Overlapping impact can cause localized frequency surges. These abnormal frequency surges typically do not occur in normal vibration patterns and are therefore important for determining whether components are experiencing overlapping impact. During operation, a discrete Fourier transform (DFT) or fast Fourier transform (FFT) is performed on the vibration signal to generate a spectrum. By comparing the frequency distribution of the motor vibration under normal operating conditions with the currently collected spectrum data, abnormal changes in frequency components can be identified. If the frequency amplitude significantly deviates from the normal fluctuation range, and the abnormal frequency coincides with the natural frequency or relative motion frequency of the motor's internal components, the motor has experienced overlapping impact. This phenomenon is typically caused by excessive wear, looseness, or inaccurate positioning of internal motor components under high load or long-term operation, resulting in collisions between components and the generation of significant mechanical stress. These shocks can have a significant impact on the operation of the motor, causing damage to mechanical components or unstable vibrations, affecting the overall performance and life of the motor. Spectral analysis is used to analyze the frequency amplitude changes in the vibration signal in detail to obtain the overlapping shock conditions of the motor parts.

[0088] Detect the mechanical stress growth of the torque motor based on the overlapping impact of motor parts;

[0089] In an embodiment of the present invention, the impact of overlapping parts is monitored to further assess changes in mechanical stress within the motor. To accurately monitor the mechanical stress within the motor, strain gauges are installed at key locations on the motor. Strain gauges can measure, in real time, the minute deformations of motor parts under stress and convert these strain data into stress information. When installing strain gauges, high-stress areas of the motor, such as the rotor, stator, connecting components, and bearings, are typically selected to ensure that changes in mechanical stress are captured. The data collected by the strain gauges is processed and analyzed in real time by a signal processing system. Mechanical calculation models, combined with strain data, are used to assess the mechanical stress conditions within the motor's internal parts after stress. In particular, when overlapping parts impact, mechanical stress increases dramatically, particularly in localized contact areas. The collision between components can lead to significant strain changes in these areas. This step relies on in-depth analysis of the strain data of the motor's internal parts after stress. This data is used to identify the components subject to the greatest stress and calculate the corresponding mechanical stress values. Specific calculation methods include using constitutive models, finite element analysis (FEA), and other techniques to simulate and calculate the stress state of motor components. By continuously monitoring and calculating the growth trend of mechanical stress, we can assess whether the motor has entered a state of stress overload. Frequent, overlapping impacts, in particular, can gradually reduce the load-bearing capacity of components, leading to further growth in mechanical stress. As stress accumulates, the motor's structure can be damaged, posing a threat to its stability and safety. By analyzing mechanical stress data, we can predict operational stability.

[0090] When the mechanical stress growth of the torque motor is ±2000N and the vibration frequency of the motor is 100Hz, the dynamic stability attenuation of the connection components is estimated;

[0091] In an embodiment of the present invention, when the mechanical stress of the motor reaches ±2000N and the vibration frequency reaches 100Hz, a dynamic stability analysis of the connecting components of the motor is required. At this stage, the vibration frequency data and mechanical stress data of the motor are collected and analyzed. The vibration signal of the connecting component is collected by a vibration sensor, the vibration frequency and amplitude are recorded, and these data are compared with the standard vibration frequency when the motor is working normally to determine whether the connecting component is close to or exceeds the normal working range. If the vibration frequency or amplitude of the connecting component significantly exceeds the preset normal range, it indicates that it is experiencing a large dynamic load or has been subjected to external impact, resulting in increased vibration, thereby affecting the dynamic stability of the connecting component. Then, combined with the mechanical stress data monitored inside the motor, the dynamic response of the connecting component under high load is evaluated. The increase in mechanical stress causes the stress state of the connecting component to change. This change will affect the rigidity, ductility and fatigue resistance of the component, thereby affecting its stability. By comparing the changing trends of mechanical stress, the load-bearing capacity of the connecting component when overloaded is evaluated, and its stability attenuation is estimated. The specific analysis method involves performing spectral analysis on vibration signals collected by sensors to identify any abnormal frequency components. This analysis, combined with mechanical models such as finite element analysis, further predicts performance changes in connected components under long-term, high-load operation. This comprehensive analysis determines whether the connected components are at risk of potential instability and allows for the implementation of necessary preventive measures.

[0092] Calculate the resonance probability of the connection parts based on the attenuation of the dynamic stability of the connection parts and the degree of aggravation of the local vibration of the motor;

[0093] In an embodiment of the present invention, an in-depth spectrum analysis is performed on the motor vibration signal. Through spectrum analysis, the vibration characteristics and resonant frequency range of each frequency band of the motor can be identified. Specifically, the vibration signal is converted from the time domain vibration signal to the frequency domain signal through a frequency domain conversion method such as the Fast Fourier Transform (FFT), so that the vibration amplitude at different frequencies can be identified. If the vibration amplitude increases significantly or exceeds a set threshold within a certain frequency range, it indicates that the motor connection component is approaching or entering a resonant state. Next, the trend of the vibration amplitude change is further evaluated in combination with the motor load condition, because the amplitude of the vibration response of the motor will vary under different load conditions. When the load is large, the vibration amplitude of the motor will usually increase, increasing the risk of resonance. Resonance theory and dynamic analysis methods are used to calculate the resonance probability of each motor connection component based on the motor load condition, vibration signal and dynamic stability attenuation of the connection component. Resonance theory is based on a physical model. By identifying the relationship between vibration frequency and structural natural frequency, it can predict whether the connection component will resonate at certain specific frequencies. The occurrence of resonance is usually accompanied by a sharp increase in vibration amplitude. By calculating the resonance probability, it is possible to predict whether the motor component is likely to enter a resonant state.

[0094] Estimate the vibration transmission condition of the torque motor based on the resonance probability of the connection components;

[0095] In this embodiment of the present invention, the vibration transmission of the motor is further evaluated based on the resonance probability of the connecting components calculated in the previous step. Vibration transmission refers to the process by which vibrations within the motor are transmitted to other parts through the connecting components. In this process, the connecting components play a crucial role as a transmission medium for vibration. When the resonance probability of the connecting components is high, it means that these components are more likely to enter a resonant state at specific frequencies, resulting in a sharp increase in the local vibration amplitude, thereby triggering wider vibration propagation. To evaluate this process, a vibration transmission model is used to simulate the transmission path of vibration from the source to other components. This simulation takes into account factors such as the material and geometry of the connecting components and the operating conditions of the motor, which together determine the propagation characteristics of vibration between different components. Next, the calculated resonance probability data is combined with the vibration transmission model to analyze the probability and extent of vibration transmission from the connecting components to other parts. If vibrations resonate and are effectively amplified in the connecting components, these vibrations can further propagate through the motor's support structure, frame, or transmission components, affecting other motor components and even transmitting to external devices, causing vibration impacts on a wider range of areas.

[0096] Detect wear trends of motor parts based on the probability of resonance of connected components and the probability of resonance of connected components;

[0097] In an embodiment of the present invention, the wear condition of motor parts is detected by analyzing the resonance probability of connecting components and combining it with the wear trend of motor parts. During the operation of the motor, wear of parts is inevitable due to vibration and impact. In particular, when the motor is operating under high load, the wear rate of parts will accelerate due to deteriorating working conditions, resulting in a gradual decline in performance and even the risk of failure. To more accurately assess the wear condition of motor parts, the resonance probability data calculated in the above steps is combined with the vibration signal to identify the frequency components related to wear. During the wear process of motor parts, their vibration characteristics will change, specifically manifested as changes in vibration frequency and amplitude. An increase in vibration amplitude is usually associated with increased wear on the part surface, while a change in vibration frequency indicates a slight change in the part's geometry, such as imbalance or increased surface roughness. Long-term monitoring of motor vibration frequency, amplitude and other data can provide effective information on the wear of motor parts. By inputting this vibration data into a wear prediction model and combining it with the motor's workload and environmental conditions, the model simulates the wear rate and extent of parts under different loads. The prediction model analyzes the changing trend of vibration to determine the degree of wear of components, estimate their remaining life, and predict the development of wear.

[0098] Calculate the probability of torque motor transmission component fracture based on the wear trend of motor parts;

[0099] In an embodiment of the present invention, the fracture probability of the motor transmission components is further calculated based on the wear trend of the motor parts. During long-term operation, the motor transmission components are subjected to periodic loads and vibrations. These loads and vibrations cause repeated stress on the component structure, leading to material fatigue, wear, and gradual structural damage. To accurately predict the fracture risk of the motor transmission components, component wear data is analyzed and, combined with fatigue theory from material mechanics, the fatigue damage of the transmission components is assessed. During this process, relevant data such as the vibration frequency, amplitude, and wear rate of the motor components are collected. The stress and operating state of the components are determined by combining factors such as the motor's operating load and ambient temperature. The cumulative fatigue damage of the components is calculated using theories such as fatigue limit and durability from material mechanics, combined with the material properties of the motor components. By employing a cumulative damage model, such as Miner's law, the stress and vibration loads experienced by the components during each operating cycle are compared with the component's fatigue limit, and the damage is gradually accumulated to determine the component's fatigue damage degree. Combined with these fatigue damage degrees, the fracture risk of the motor transmission components within a certain period of time is predicted. Especially under high-load and long-term operation conditions, the degree of wear on transmission components will increase, leading to accelerated fatigue damage of components and increasing the probability of fracture. Through comprehensive analysis of all these factors, the fracture probability of motor transmission components can be accurately estimated.

[0100] The electromechanical coupling break effect of the torque motor is evaluated based on the fracture probability of the torque motor transmission components and the wear trend of the motor bearings.

[0101] In an embodiment of the present invention, the electromechanical coupling rupture effect of the motor is evaluated based on the fracture probability of the motor transmission components and the wear trend of the motor bearings. The electromechanical coupling rupture effect refers to the situation in which, when a key component in the motor system fails (such as a transmission component fracture or bearing wear), it triggers a chain failure of other components, thereby causing a significant decline in the overall performance of the motor or even complete failure. By calculating the fracture probability of the motor transmission components, the fracture risk of the transmission components caused by fatigue damage, wear and external loads during long-term operation is obtained. When the fracture probability of the transmission components is high, it has a serious impact on the transmission system of the motor. Correspondingly, the wear trend of the motor bearings is monitored in real time. The bearings undertake key support and rotation tasks during the operation of the motor. Long-term loads and vibrations will cause the bearings to wear more severely, thereby affecting their rotation accuracy and load-bearing capacity. The intensification of bearing wear often indicates that the operating state of the motor is unstable and may even lead to bearing failure or failure. Combining the fracture probability of the motor transmission components and the wear trend of the bearings, the probability of the electromechanical coupling rupture effect occurring in the system is judged. When transmission component breakage or bearing wear reaches a certain level, abnormal vibration, overload, or system instability can occur during motor operation, impacting the normal operation of other components. By developing a motor system fault prediction model and combining wear data from transmission components and bearings, we can evaluate the electromechanical coupling effect and further estimate the extent of its impact on other motor components. For example, a broken transmission component can cause increased vibration, accelerating bearing wear; a bearing failure can cause unstable operation of the motor shaft, further impacting the transmission system.

[0102] The calculation of the motor dynamic thermal load overload degree in step S2 includes:

[0103] Calculate the attenuation degree of motor energy transmission efficiency based on the electromechanical coupling disconnection effect of the torque motor;

[0104] In an embodiment of the present invention, a high-frequency vibration sensor and an encoder device are deployed during the electromechanical conversion process of the torque motor to respectively collect the rotor output mechanical response (speed, angular acceleration) and the stator input electromagnetic response (phase voltage, current waveform) to obtain the machine-end input electromagnetic energy and mechanical-end output torque data. The collected voltage and current phase information is synchronized with the phase vector to obtain the actual electromagnetic power input value. The participation frequency of the multi-order harmonic components in the torque coupling process is calculated by discrete Fourier transform, and the abnormal energy attenuation index in the harmonic frequency band is extracted. Afterwards, the energy conversion efficiency value of the torque motor is solved by using the ratio of the mechanical output torque to the electromagnetic input energy per unit time, eliminating the load disturbance term, and forming a two-dimensional efficiency attenuation map in different load and speed ranges. The slope of the electromechanical energy output ratio is used as a direct evaluation data for the degree of energy transmission efficiency attenuation to obtain the degree of energy transmission efficiency attenuation of the motor.

[0105] Based on the degree of attenuation of motor energy transmission efficiency, it is estimated that the power consumption will increase excessively;

[0106] In an embodiment of the present invention, on the basis of obtaining the energy transmission efficiency attenuation map, it is necessary to calculate the relative attenuation difference within the same load-speed range by comparing with the historical benchmark efficiency map. The rate of change of the efficiency value at each point in a specific sampling period is selected to form an energy consumption increment vector, and the energy consumption growth value is obtained by dividing the actual power supply power in each operating section (collected synchronously by the voltage and current acquisition equipment) by the corresponding efficiency. Then, the cumulative increase of the growth value per unit time is used as the horizontal axis, and the transmission efficiency attenuation rate is used as the vertical axis to construct a two-dimensional energy consumption growth trend map. If the slope of the area in the map is too large (for example, greater than a certain threshold, such as 0.2 / kW·min), it is defined as an abnormal energy consumption growth section, and the growth trend index sequence of the trend within a given operating time period is output as the basic indicator for subsequent voltage regulation and winding load evaluation.

[0107] Estimate the attenuation of the torque motor's coordination ability based on the electromechanical coupling disconnection effect of the torque motor;

[0108] In an embodiment of the present invention, based on the acquired electromechanical coupling data, it is necessary to further utilize high-time-resolution data to analyze the time delay relationship between the stator current waveform and the rotor position change, and extract the phase difference change rate in combination with the electrical angle analysis algorithm. A signal synchronizer is used to align the speed signal with the current signal, and a cross-correlation function is used to calculate the dynamic correlation index between the two signals. If the electromagnetic response lags behind the mechanical response by more than a certain threshold (such as more than 3ms), it indicates that there is a sign of coordination rupture in the electromechanical coupling chain. Then, the cumulative value of multi-cycle vibration deviation is introduced, and the coordination ability attenuation coefficient is constructed by analyzing the integral value of the response error function of the torque command and the actual output torque at each operating point.

[0109] Detecting torque motor current overload conditions based on torque motor coordination capability attenuation conditions;

[0110] In an embodiment of the present invention, in combination with the obtained coordination ability attenuation coefficient, a high sampling rate current acquisition device (≥50kHz) is used to collect data on the three-phase stator current during the real-time operation of the system, and the data is compared with the rated current threshold after normalization. When the coordination ability attenuation coefficient is higher than the preset threshold (such as 0.75), if the current of any phase in the real-time sampling data exceeds 120% of the rated current continuously within 5 seconds, it can be judged as a current overload state. In this process, it is necessary to introduce a differential calculation of the current slope (dI / dt) to determine whether there is an abnormal current impact phenomenon, and to determine whether it is caused by a coordination mismatch in combination with the electrical angle offset data. The output includes a current overload record table including the overload duration, the current limit amplitude, and the abnormal frequency of each phase, and is synchronously sent to the winding loss analysis process of the next step.

[0111] Identify the increase in torque motor winding losses based on the torque motor current overload condition;

[0112] In the embodiment of the present invention, the peak value, RMS value and overload duration of each phase current are extracted from the acquired current overload data record table, and the resistance loss is calculated by combining the stator winding material characteristic parameters (such as copper resistivity, wire diameter, number of turns), using the formula P_loss=I 2 R is accumulated cycle by cycle to produce a time series of the winding heating power. Based on this, a thermocouple temperature acquisition probe (placed between the winding and the core) is deployed to obtain the actual temperature rise curve, which is then compared with the theoretical heating curve to analyze the error value. If the actual temperature rise is significantly higher than the calculated value, it further indicates the presence of nonlinear winding degradation (such as insulation aging). The cumulative power loss growth rate (the percentage of power growth per unit time) is used as the "winding loss growth factor," forming independent curves for the three phases. This factor is then output as input for subsequent voltage regulation decisions.

[0113] Estimate the torque motor voltage regulation out of control condition based on the torque motor winding loss growth and excessive power consumption growth trend;

[0114] In an embodiment of the present invention, the energy consumption growth trend index of step 2 and the winding loss growth factor of step 5 are combined and standardized to construct a two-dimensional indicator space. If the two indicators are synchronously located above 80% of their corresponding maximum thresholds within a certain time window, it is determined that the voltage regulation system is in a high-risk area. At this time, it is necessary to retrieve the feedback regulation records in the voltage control system, including the PWM duty cycle change trend, the voltage command and the actual output difference. The voltage regulation offset integral is constructed using a difference integral algorithm. If the integral value continues to grow and exceeds the set threshold (such as 1.5V·s), it can be determined that the system regulation function is out of control. Finally, the regulation out-of-control identification result and the corresponding time point are output to prepare data annotation for subsequent fluctuation data detection.

[0115] Detecting torque motor voltage fluctuation data based on a torque motor voltage regulation out-of-control condition;

[0116] In an embodiment of the present invention, during the period of voltage regulation out of control, a high-speed voltage sampling module (sampling rate greater than 100kHz) is started to collect three-phase line voltage and phase voltage data in real time, and the low-frequency (0-300Hz) and high-frequency (above 300Hz) fluctuation components are extracted through fast Fourier transform (FFT). The short-time standard deviation, range and frequency offset are calculated respectively to form a multi-dimensional voltage fluctuation feature vector. In the feature vector, if the standard deviation rises continuously or the range exceeds 5% of the rated value, it is marked as an "abnormal fluctuation window". This feature vector is bound to the winding loss growth factor to form a joint analysis data set, and the output voltage fluctuation curve, abnormal fluctuation section and voltage stability score are used for heating load analysis.

[0117] The dynamic thermal overload degree of the motor is calculated based on the torque motor voltage fluctuation data and the torque motor winding loss growth.

[0118] In an embodiment of the present invention, the extracted abnormal voltage fluctuation segment is time-aligned with the winding loss growth factor to construct a two-variable function of the input power fluctuation index (expressed as ΔU) and the thermal power growth index (expressed as ΔP_loss) in each time slice. Using a sliding time window analysis, the energy input fluctuation integral value and the corresponding thermal power growth gradient are calculated in each 10-second analysis cycle. The two are normalized and multiplied to form a "dynamic thermal load superposition index". If the index exceeds a threshold (such as 1.2), it is considered to be a thermal load overload state. A thermal load overload degree curve is output, including the peak time point, peak amplitude and abnormal duration, and the corresponding winding phase and voltage offset source are marked to provide a quantitative data basis for motor degradation judgment.

[0119] Preferably, step S3 includes the following steps:

[0120] Step S31: evaluating the abnormal condition of the torque motor according to the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor;

[0121] In an embodiment of the present invention, a thermal detection module is configured to monitor the temperature rise of the torque motor windings. The module consists of several high-precision thermocouples spaced apart in the upper and lower layers, middle slots, and end areas of the stator windings, with a sampling frequency set to 10 times per second. Monitoring data is aggregated in real time to the data acquisition and control module within the motor monitoring unit. The module analyzes the difference between the temperature rise rate and the rated temperature limit to determine the specific thermal overload value. If the temperature rise rate exceeds 30% of the rated temperature rise rate within a set time (e.g., 10 minutes), an overload event is recorded. If the overload persists for more than three consecutive times, with each overload lasting at least one minute, the thermal overload is considered moderate or above. The module consists of several high-precision thermocouples spaced apart in the upper and lower layers, middle slots, and end areas of the stator windings, with a sampling frequency set to 10 times per second. The monitoring data is aggregated in real time to the data acquisition and control module in the motor monitoring unit. By analyzing the difference between the temperature rise rate and the rated temperature limit, the specific value of the thermal load overload is obtained. If the temperature rise rate exceeds 30% of the rated temperature rise rate within the set time (for example, 10 minutes), it is recorded as an overload event. If the overload continues for more than three times in total and the single duration is not less than 1 minute, the degree of thermal load overload is judged to be moderate or above. The above-mentioned thermal load overload degree is jointly determined with the electromechanical coupling disconnection effect detection result. If both indicators reach the preset severity level, the system generates an abnormal state mark value as the input basis for the subsequent steps.

[0122] Step S32: detecting the mechanical fatigue degree of the torque motor based on the abnormal condition of the torque motor;

[0123] In this embodiment of the present invention, based on the abnormal state flag obtained in step S31 for the torque motor, fatigue testing of the motor's mechanical structure is further performed. A surface-mount metal resistance strain gauge array is deployed in a cross-symmetrical pattern in the middle of the motor's main shaft, the transition sections of the two end journals, the housing connection flange, and the mounting base connection plate. These areas are the structural points most frequently subjected to variable loads. The strain gauge sampling frequency is set to 500 times per second, and the data is amplified and filtered by the strain signal conditioner before being transmitted to the main control module. Mechanical fatigue assessment is based on statistical analysis of strain amplitude changes and frequency. All strain waveform data collected during operation is sliced ​​into equal time periods, each lasting 10 minutes. Within each time period, the maximum strain amplitude and the number of zero crossings of the strain waveform are counted to determine the number of alternating loads per unit time. The fatigue threshold is defined as a strain amplitude exceeding 80% of the material's yield strain. When the strain waveform of a region exhibits excessive strain for more than three time periods, it is considered to be showing signs of initial fatigue. Laser displacement sensors are further used to monitor micro-displacement between the housing and support components to confirm whether structural relaxation or permanent deformation due to fatigue has occurred. If the cumulative displacement exceeds 10% of the initial set gap, it is determined that the area is showing signs of plastic deformation or microcrack development. The strain amplitude, deformation frequency, and cumulative micro-displacement corresponding to all points are recorded in a mechanical fatigue analysis table, and a mechanical fatigue level classification (mild fatigue, moderate fatigue, and severe fatigue) is output to serve as a basis for further position offset detection.

[0124] Step S33: detecting the position offset of the mechanical components according to the mechanical fatigue degree of the torque motor;

[0125] In an embodiment of the present invention, given that fatigue levels are known to exist in the torque motor's mechanical structure, a spatial position detection system performs real-time comparisons of the operating positions of key components. A high-precision laser interferometer rangefinder is used to establish multiple distance measurement reference points between the stator housing and the output shaft's tail end. This is combined with an angle encoder and an axial displacement detection rail mounted on the fixed end of the structure to detect minute lateral and axial offsets of the output shaft's end during high-speed operation. The laser rangefinder is set to a measurement range of no less than 200 mm, with a resolution better than 0.001 mm. The system captures the spatial motion trajectory of the shaft end during high-speed rotation and records the trajectory center's changes. If the trajectory center deviates by more than an initial set value (e.g., 0.1 mm) over multiple sampling cycles, and if the offset shows a unidirectional accumulation trend, the component is deemed to have experienced actual physical offset. To verify the relevance of the offset, the position offset data is matched with fatigue level data from the previous stage. If the area with the largest offset corresponds exactly to a structural location with a "severe fatigue" fatigue level, this indicates that local deformation caused by fatigue has resulted in structural imbalance, leading to eccentricity of the rotating component or asymmetric force on the supporting components. All test data are reported in the form of trajectory reconstruction diagrams, position offset distribution diagrams and offset growth curves. The offset direction, displacement value and duration are also recorded, providing a spatial position information basis for determining the balance attenuation trend in subsequent operations.

[0126] Step S34: Evaluate the torque motor's operating balance attenuation trend based on the position offset of the mechanical components and the torque motor's mechanical fatigue level.

[0127] In an embodiment of the present invention, after completing the position offset and fatigue level assessments in the first two steps, this step analyzes the attenuation trend of the torque motor's overall operating balance. A built-in triaxial vibration sensor is used to simultaneously monitor the vibration response at the torque motor's mounting base, spindle support section, and housing midsection. The sampling frequency is set to 5,000 times per second, and the vibration frequency range covers 1 to 1,000 Hz. The main frequency component in the vibration signal is matched with the position offset direction recorded in the previous stage to determine whether there is an increase in offset coupling in the resonance range. If the vibration acceleration direction tends to be consistent with the offset direction and the amplitude maintains an upward trend over multiple time periods, it is determined to be a signal that the offset affects the operating balance. To quantify the trend of operating balance attenuation, the axial and radial vibration peak change rates per unit time are calculated, and the comprehensive balance attenuation index is calculated in combination with the fatigue level value. A larger index indicates a more significant decline in operating balance capability. A vibration intensity change graph and an offset trend overlay graph are plotted over a period of 6 hours of continuous operation to clearly reflect the dynamic process of attenuation. Output the running balance attenuation trend results in tabular form, including key indicators such as vibration growth rate, offset synchronization, fatigue area contribution and trend curve slope.

[0128] Preferably, step S4 includes the following steps:

[0129] Step S41: estimating the performance degradation of the torque motor based on the torque motor operation balance degradation trend;

[0130] In an embodiment of the present invention, the performance attenuation of the torque motor is estimated based on the balance attenuation trend of the torque motor operation. In this step, the operating data of the motor is collected, including vibration data, torque fluctuation, load status and temperature data. By monitoring and analyzing these data for a long time, the balance attenuation trend of the motor is identified. The specific implementation method includes using a vibration sensor and an accelerometer to monitor the vibration frequency and amplitude of the motor. These data can reflect whether the motor is in an unbalanced state. By analyzing the torque fluctuation data, the attenuation trend of the motor performance is calculated. The balance attenuation trend of the motor is closely related to its performance attenuation. By detecting changes in the balance condition, the degree of motor performance attenuation can be estimated. During the data processing process, time domain and frequency domain analysis methods are used, combined with Fourier transform for data analysis, to accurately estimate the attenuation of the motor performance.

[0131] Step S42: detecting the degree of attenuation of the torque motor's operating accuracy according to the torque motor's performance attenuation and the torque motor's operating balance attenuation trend;

[0132] In an embodiment of the present invention, the degree of attenuation of the torque motor's operating accuracy is detected based on the torque motor's performance attenuation and the torque motor's operating balance attenuation trend. This step combines the motor's performance attenuation data and balance attenuation trend, and further determines the accuracy attenuation by detecting the motor's actual operating accuracy. A precise angle sensor and displacement sensor are used to monitor the motor's output torque and rotation angle in real time to obtain the motor's accuracy data. By analyzing the accuracy changes of the motor under different load conditions and combining the aforementioned balance attenuation data, the motor's accuracy attenuation trend is identified. Especially under heavy load conditions, the motor experiences a decrease in accuracy, which is directly related to its performance attenuation and mechanical imbalance. The degree of accuracy attenuation is determined by comparing the accuracy data during normal operation of the motor with the current data. When implementing this operation, the data acquisition system and the accuracy calculation model are combined to analyze whether the motor's operating accuracy is affected.

[0133] Step S43: calculating the torque motor aging failure probability based on the torque motor operation accuracy attenuation degree and the torque motor performance attenuation condition;

[0134] In an embodiment of the present invention, the aging failure probability of the torque motor is calculated based on the degree of attenuation of the torque motor's operating accuracy and the torque motor's performance attenuation. According to a comprehensive analysis of the motor's performance attenuation and operating accuracy attenuation, a calculation model for the motor's aging failure probability is established, and the probability of the motor's aging failure is evaluated by statistically analyzing the accuracy and performance data of the motor under different working conditions. The aging failure probability of the motor is affected by many factors, including wear of mechanical components, temperature changes, vibration damage, etc. By combining the motor's aging data, vibration data, and load conditions, a statistical method (such as Poisson distribution, Gamma distribution, etc.) is used to calculate the aging failure probability of the motor. In this process, it is necessary to rely on the real-time monitoring data of the motor, combined with empirical formulas or calculation models, to obtain accurate aging failure prediction results. During data processing, a probability calculation model is used to output the aging failure probability of each motor component.

[0135] Step S44: performing torque motor optimization processing based on the torque motor aging failure probability to obtain torque motor optimization data.

[0136] In an embodiment of the present invention, torque motor optimization is performed based on the aging failure probability of the torque motor to obtain torque motor optimization data. Based on the aging failure probability of the motor, torque motor optimization is performed to delay motor failures and extend the motor's service life. Regular inspection and maintenance are performed on components with high aging levels, including replacing worn bearings and gears or adjusting the motor's balance. By combining the aging failure probability data, components prone to failure are identified, and corresponding repair or optimization measures are implemented. During the optimization process, the motor's operating state is improved by adjusting the motor's operating parameters, such as changing the load distribution, optimizing torque output, and adjusting the operating frequency. This process also includes optimizing the motor's thermal management system to reduce overheating and extend the motor's service life. The optimization data includes motor adjustment parameters, maintenance recommendations, and operating strategies, which serve as the basis for formulating subsequent monitoring and maintenance plans. All operations are performed through a data processing platform, which automatically generates optimization plans based on real-time collected motor operating data to ensure optimal motor operation.

[0137] The present invention further provides a torque motor torque performance detection system for executing the torque motor torque performance detection method described above, the torque motor torque performance detection system comprising:

[0138] An initial performance evaluation module is used to obtain torque motor object data; collect torque motor structure data according to the torque motor object data; and evaluate the initial performance of the torque motor based on the torque motor structure data;

[0139] The dynamic thermal load overload degree calculation module is used to detect the motor torque dynamic fluctuation exceeding the limit condition based on the initial performance of the torque motor; evaluate the electromechanical coupling disconnection effect of the torque motor based on the motor torque dynamic fluctuation exceeding the limit condition; and estimate the motor dynamic thermal load overload degree based on the electromechanical coupling disconnection effect of the torque motor;

[0140] The operation balance attenuation trend assessment module is used to assess the abnormal condition of the torque motor based on the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor; and to assess the operation balance attenuation trend of the torque motor based on the abnormal condition of the torque motor;

[0141] The torque motor optimization processing module is used to estimate the torque motor performance degradation based on the torque motor operation balance degradation trend; calculate the torque motor aging failure probability based on the torque motor performance degradation; and perform torque motor optimization processing based on the torque motor aging failure probability to obtain torque motor optimization data.

[0142] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting torque performance of a torque motor, characterized in that: The following steps are involved: Step S1: Obtain torque motor object data; Collect torque motor structure data according to torque motor object data; Evaluate the initial performance of the torque motor based on the torque motor structural data; Step S2: detecting the motor torque dynamic fluctuation exceeding the limit condition according to the initial performance of the torque motor; Evaluate the electromechanical coupling disconnection effect of the torque motor based on the motor torque dynamic fluctuation exceeding the limit condition; Estimate the degree of dynamic thermal overload of the motor based on the electromechanical coupling disconnection effect of the torque motor; Step S3: Evaluate the abnormal condition of the torque motor according to the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor; Evaluate the torque motor's operating balance attenuation trend based on the torque motor's abnormal condition; Step S4: estimating the performance degradation of the torque motor based on the torque motor operation balance degradation trend; Calculate the probability of torque motor aging failure based on the torque motor performance degradation; The torque motor is optimized based on the aging failure probability of the torque motor to obtain the torque motor optimization data.

2. The torque motor torque performance detection method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: acquiring torque motor object data; Step S12: collecting torque motor structure data based on the torque motor object data; Step S13: Evaluating the motor aerodynamic state according to the torque motor structure data; Step S14: Evaluate the initial performance of the torque motor based on the torque motor structural data and the motor aerodynamic state.

3. The torque motor torque performance detection method according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: extracting torque motor rotor structure data according to the torque motor structure data; Step S132: Calculating the motor rotor rotation radius data according to the torque motor rotor structure; Step S133: identifying the internal connectivity of the torque motor based on the torque motor rotor structure; Step S134: Calculating the air shear force of the torque motor using the electronic rotor rotation radius data and the internal connectivity of the torque motor; Step S135: estimating the increase degree of the motor air resistance according to the air shear force of the torque motor; Step S136 : Evaluate the motor aerodynamic state based on the motor air resistance growth degree and the torque motor air shear force.

4. The torque motor torque performance detection method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: when the internal pressure is between 3.2 Pa and 4 Pa, the motor aerodynamic state is used to detect airflow restriction inside the motor; Step S142: When the airflow restriction inside the motor is 25%, the heat dissipation capacity of the torque motor is evaluated; Step S143: identifying the dynamic motion characteristics of the torque motor based on the torque motor structure data; Step S144: detecting the smoothness of rotation of the motor components based on the dynamic motion characteristics of the torque motor; Step S145: collecting torque parameters of motor components according to the torque motor structure data; Step S147: evaluating the initial stability of the torque motor according to the torque parameters of the motor components and the rotation smoothness of the motor components; Step S148: When the heat dissipation capacity of the torque motor exceeds 300 kW and the initial stability of the torque motor is determined, the initial performance of the torque motor is evaluated.

5. The torque motor torque performance detection method according to claim 1, characterized in that: The motor torque dynamic fluctuation over-limit condition detection in step S2 includes: Simulate the torque motor load condition based on the initial performance of the torque motor; Detect the rated torque overload degree of the torque motor based on the load condition of the torque motor; Count the duration of torque motor overload according to the torque motor load condition; The torque motor magnetic field enhancement effect is detected by using the torque motor load overload duration and the torque motor rated torque overload degree; Predicting the magnetic saturation phenomenon of torque motors based on the magnetic field enhancement effect of torque motors; Estimating the increase in motor torque fluctuation based on the magnetic saturation phenomenon of the torque motor; Based on the increase of motor torque fluctuation and the magnetic saturation phenomenon of the torque motor, the excessive dynamic fluctuation of the motor torque is detected.

6. The torque motor torque performance detection method according to claim 1, characterized in that: The evaluation of the electromechanical coupling disconnection effect of the torque motor described in step S2 includes: The frequency of 10Hz is used to collect the degree of aggravation of the motor's local vibration when the dynamic fluctuation of the motor torque exceeds the limit; Estimate the overlapping impact of motor parts based on the degree of local vibration aggravation of the motor; Detect the mechanical stress growth of the torque motor based on the overlapping impact of motor parts; When the mechanical stress growth of the torque motor is ±2000N and the vibration frequency of the motor is 100Hz, the dynamic stability attenuation of the connection components is estimated; Calculate the resonance probability of the connection parts based on the attenuation of the dynamic stability of the connection parts and the degree of aggravation of the local vibration of the motor; Estimate the vibration transmission condition of the torque motor based on the resonance probability of the connection components; Detect wear trends of motor parts based on the probability of resonance of connected components and the probability of resonance of connected components; Calculate the probability of torque motor transmission component fracture based on the wear trend of motor parts; The electromechanical coupling break effect of the torque motor is evaluated based on the fracture probability of the torque motor transmission components and the wear trend of the motor bearings.

7. The torque motor torque performance detection method according to claim 1, characterized in that: The estimation of the motor dynamic thermal load overload degree in step S2 includes: Calculate the attenuation degree of the motor energy transmission efficiency based on the electromechanical coupling disconnection effect of the torque motor; Based on the degree of attenuation of motor energy transmission efficiency, it is estimated that the power consumption will increase excessively; Estimate the attenuation of the torque motor's coordination ability based on the electromechanical coupling disconnection effect of the torque motor; Detecting torque motor current overload conditions based on torque motor coordination capability attenuation conditions; Identify the increase in torque motor winding losses based on the torque motor current overload condition; Estimate the torque motor voltage regulation out of control condition based on the torque motor winding loss growth and excessive power consumption growth trend; Detecting torque motor voltage fluctuation data based on a torque motor voltage regulation out-of-control condition; The dynamic thermal overload degree of the motor is estimated based on the torque motor voltage fluctuation data and the torque motor winding loss growth.

8. The torque motor torque performance detection method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Evaluate the abnormal condition of the torque motor according to the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor; Step S32: detecting the mechanical fatigue degree of the torque motor based on the abnormal condition of the torque motor; Step S33: detecting the position offset of the mechanical components according to the mechanical fatigue degree of the torque motor; Step S34: Evaluate the torque motor's operating balance attenuation trend based on the position offset of the mechanical components and the degree of mechanical fatigue of the torque motor.

9. The method for detecting torque performance of a torque motor according to claim 1, wherein: Step S4 includes the following steps: Step S41: estimating the performance degradation of the torque motor based on the torque motor operation balance degradation trend; Step S42: detecting the degree of attenuation of the torque motor's operating accuracy according to the torque motor's performance attenuation and the torque motor's operating balance attenuation trend; Step S43: calculating the torque motor aging failure probability based on the torque motor operation accuracy attenuation degree and the torque motor performance attenuation condition; Step S44: performing torque motor optimization processing based on the torque motor aging failure probability to obtain torque motor optimization data.

10. A torque motor torque performance detection system, characterized in that: For executing the torque motor torque performance detection method according to claim 1, the torque motor torque performance detection system comprises: An initial performance evaluation module is used to obtain torque motor object data; collect torque motor structure data according to the torque motor object data; and evaluate the initial performance of the torque motor based on the torque motor structure data; The dynamic thermal load overload degree calculation module is used to detect the motor torque dynamic fluctuation exceeding the limit condition based on the initial performance of the torque motor; evaluate the electromechanical coupling disconnection effect of the torque motor based on the motor torque dynamic fluctuation exceeding the limit condition; and estimate the motor dynamic thermal load overload degree based on the electromechanical coupling disconnection effect of the torque motor; The operation balance attenuation trend assessment module is used to assess the abnormal condition of the torque motor based on the degree of dynamic thermal load overload of the motor and the electromechanical coupling disconnection effect of the torque motor; and to assess the operation balance attenuation trend of the torque motor based on the abnormal condition of the torque motor; The torque motor optimization processing module is used to estimate the torque motor performance degradation based on the torque motor operation balance degradation trend; calculate the torque motor aging failure probability based on the torque motor performance degradation; and perform torque motor optimization processing based on the torque motor aging failure probability to obtain torque motor optimization data.

Images