Fault online diagnosis method for low-speed large-torque motor control system

Abstract

The invention relates to the field of fault detection, and discloses a fault online diagnosis method for a low-speed large-torque motor control system, and the method comprises the steps: collecting the multi-dimensional parameters of the three-phase current of a motor, the voltage of a DC bus, the position of a motor rotor, the speed of the motor rotor, and the temperature of a radiator based on a sensor group; carrying out data preprocessing on the collected multi-dimensional parameters to remove noise interference; extracting fault features related to current, voltage, rotor motion and temperature from the preprocessed data; a dynamic threshold value is constructed based on fault features, non-fault parameter fluctuation and fault abnormity are distinguished through multi-feature correlation analysis, and online fault diagnosis is achieved; and finally, outputting a diagnosis result containing the fault type and the fault early warning information. Through multi-dimensional parameter acquisition and dynamic correlation diagnosis, the comprehensiveness and accuracy of fault diagnosis are improved, the false alarm rate is effectively reduced, and the method can adapt to the complex working conditions of load fluctuation and rotating speed switching of the low-speed large-torque motor.

Description

Technical Field

[0001] This invention relates to the field of fault detection, and more specifically to an online fault diagnosis method for a low-speed, high-torque motor control system. Background Technology

[0002] Low-speed, high-torque motors are a type of special motor that can directly output low speed and high torque without the need for additional reduction gears or only requiring simple reduction. Their core design goal is to simplify the transmission system and, through structural and control optimization, directly match the industrial application requirements of low-speed operation and high-load drive. However, fault diagnosis of their control systems faces unique challenges not present in traditional high-speed motor systems. Existing diagnostic methods have significant shortcomings when applied to this type of motor, mainly in the following aspects: Firstly, insufficient data acquisition dimensions lead to the omission of key fault characteristics strongly related to motor control. Existing diagnostic methods mostly only collect basic electrical quantities such as three-phase current or DC bus voltage, while neglecting multi-dimensional state parameters closely related to motor control performance under low-speed, high-torque operating modes. For example, during low-speed operation, the accuracy of rotor position detection directly affects the accuracy of field-oriented control. Even slight deviations in the position signal can lead to torque fluctuations or even loss of synchronization. If the control system does not collect and integrate rotor position signals, it cannot distinguish whether the abnormal current is caused by an electrical fault in the winding or by control misalignment caused by a position detection deviation. At the same time, under high torque load conditions, motor stall and overload faults are often accompanied by a sudden rise in the temperature of power modules (such as IGBTs) and heat sinks. However, traditional diagnostic strategies do not link temperature parameters with electrical parameters such as current and voltage, which can easily lead to only judging current overload without timely warning of serious faults such as IGBT module burnout due to overheating, resulting in missed diagnostics. In addition, low-frequency fluctuations in rotor speed signals often contain early mechanical fault information such as meshing faults in mechanical transmission components (such as integrated reduction mechanisms) and bearing wear. Existing single-parameter acquisition modes are difficult to effectively capture these mechanical fault characteristics that are closely coupled with electrical operating parameters, and cannot meet the needs of mechatronics diagnostics.

[0003] Secondly, the diagnostic algorithm's poor adaptability to complex operating conditions leads to a persistently high false alarm rate. Low-speed, high-torque motors often operate under conditions of severe load fluctuations and frequent speed changes. The current, torque, and other parameters of their control systems exhibit significant non-fault-related fluctuations. Existing diagnostic methods have obvious limitations: they generally use fixed threshold criteria and cannot dynamically track changes in operating conditions. Consequently, it is difficult to distinguish between instantaneous overcurrent caused by load impacts and continuous overcurrent caused by faults such as inter-turn short circuits, resulting in frequent false alarms in the control system and reducing diagnostic reliability and system availability.

[0004] In summary, existing fault diagnosis methods have shortcomings in terms of the comprehensiveness of data acquisition and the intelligence of diagnostic algorithms when applied to low-speed, high-torque motor control systems, making it difficult to meet the requirements of high-reliability control. Therefore, there is an urgent need for an online diagnostic solution that can deeply integrate multi-dimensional state parameters and has dynamic adaptability. Summary of the Invention

[0005] The purpose of this invention is to provide an online fault diagnosis method for a low-speed, high-torque motor control system, thereby solving at least one of the above-mentioned technical problems.

[0006] The objective of this invention can be achieved through the following technical solutions: An online fault diagnosis method for a low-speed, high-torque motor control system includes the following steps: S1. Collect the three-phase current, DC bus voltage, motor rotor position, motor rotor speed and radiator temperature of the motor based on the sensor group; S2. Perform data preprocessing on the multi-dimensional parameters collected in step S1 to remove noise interference; S3. Extract fault features from the preprocessed multi-dimensional parameters, including current features, voltage features, rotor motion features, and temperature features. S4. Construct a dynamic threshold based on the fault characteristics, and realize online fault diagnosis through multi-feature correlation analysis; S5. Output the fault diagnosis results, which include the fault type and fault warning information.

[0007] As a further technical solution, the dynamic threshold is used to distinguish between non-fault parameter fluctuations caused by load shocks and persistent parameter anomalies caused by faults. The multi-feature correlation analysis is used to correlate current features, voltage features, rotor motion features and temperature features to distinguish between electrical faults, mechanical faults and thermal faults.

[0008] As a further technical solution, the data preprocessing in step S2 includes filtering and dimensionless processing. The filtering uses Kalman filtering or moving average filtering to eliminate high-frequency noise in multi-dimensional parameters. The dimensionless processing uses maximum-minimum normalization or Z-score normalization to unify the numerical magnitude of multi-dimensional parameters.

[0009] As a further technical solution, the fault characteristics mentioned in step S3 specifically include: the current imbalance of the three-phase current, the voltage fluctuation coefficient of the DC bus voltage, the position deviation rate of the motor rotor position, the speed fluctuation rate of the motor rotor speed, and the temperature change rate of the radiator temperature.

[0010] As a further technical solution, three-phase current Current imbalance The calculation method is as follows: ;in, It is the arithmetic average of the three-phase currents over one electrical cycle; Voltage fluctuation coefficient of DC bus voltage The calculation method is as follows: ;in, , These represent the peak and valley values ​​of the DC bus voltage within a set time window. This is the rated voltage of the DC bus. Position deviation rate of motor rotor position The calculation formula is: ;in, This refers to the actual rotor position collected. The expected rotor position is calculated from the instructions given by the controller; Speed ​​fluctuation rate of motor rotor speed The calculation formula is: ; To set the standard deviation of rotor speed within the time window, The current speed reference value; Temperature change rate of radiator temperature The calculation formula is: ;in, The temperature at the current sampling time. The temperature at the previous sampling time. The sampling interval is denoted as .

[0011] As a further technical solution, the dynamic threshold is used to distinguish between non-fault parameter fluctuations caused by load shocks and persistent parameter anomalies caused by faults. The distinction logic is implemented in the following way: When any fault characteristic quantity exceeds the corresponding dynamic threshold for the first time, a delay timer is started, and the value of the fault characteristic quantity is continuously monitored; If the value of the fault characteristic quantity falls back to within the dynamic threshold before the delay timer expires, it is determined that the threshold exceedance is a non-fault parameter fluctuation caused by load impact, and the timer is cleared. If the value of the fault characteristic quantity continues to exceed the dynamic threshold until the delay timer expires, the threshold exceedance is determined to be a continuous parameter abnormality caused by the fault, triggering the fault diagnosis and alarm process.

[0012] As a further technical solution, the multi-feature association analysis in step S4 specifically includes: If the current imbalance is greater than the corresponding dynamic threshold, and the position deviation rate is less than or equal to the corresponding dynamic threshold and the temperature change rate is less than or equal to the corresponding dynamic threshold, then it is determined to be an electrical fault. If the speed fluctuation rate is greater than the corresponding dynamic threshold, and the position deviation rate is greater than the corresponding dynamic threshold and the current imbalance is less than or equal to the corresponding dynamic threshold, then it is determined to be a mechanical fault. If the rate of temperature change is greater than the corresponding dynamic threshold, and the current imbalance is greater than the corresponding dynamic threshold and the voltage fluctuation coefficient is greater than the corresponding dynamic threshold, then it is determined to be a thermal fault.

[0013] As a further technical solution, the formula for calculating the dynamic threshold is: ,in, The dynamic threshold for any fault characteristic. As the baseline threshold, The operating condition correction factor is composed of key real-time operating condition parameters corresponding to the fault characteristics and experimentally calibrated correction coefficients. Its specific expression is as follows: ,in, The number of key operating condition parameters corresponding to the fault characteristics. , For the first Correction coefficients for each operating condition parameter For the first A dimensionless key real-time operating parameter.

[0014] The beneficial effects of this invention are: (1) This invention breaks through the limitation of the single dimension of traditional diagnostic data. It collects the three-phase current, DC bus voltage, rotor position, rotor speed and radiator temperature of the motor through a sensor group, covering key parameters of electrical, mechanical and thermal systems. When running at low speed, the rotor position deviation rate can distinguish whether the current abnormality is an electrical fault or a mechanical deviation. Under high torque load, the temperature change rate is linked with the electrical parameters to avoid missing the risk of IGBT overheating when only judging current overload. At the same time, combined with the characteristics of current imbalance, speed fluctuation rate and multi-feature correlation analysis, it can accurately distinguish electrical, mechanical and thermal faults without missing key fault modes. It is adapted to the multi-system coupling characteristics of the motor and ensures safe operation. (2) This invention addresses the complex operating conditions of motor load fluctuation and speed switching. It constructs a dynamic threshold based on fault characteristics and incorporates parameters such as real-time load rate and speed through operating condition correction factors, so that the threshold is dynamically adjusted according to the operating conditions, avoiding misjudgment of fixed thresholds. With the addition of delay timing logic, a fault is only determined when the parameter continuously exceeds the threshold, eliminating the interference of instantaneous fluctuations. Multi-feature correlation analysis requires multi-feature coordinated anomalies, such as not determining a fault for a single current fluctuation, further reducing false alarms. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, the present invention provides an online fault diagnosis method for a low-speed, high-torque motor control system, comprising the following steps: S1. Based on the sensor group, collect the three-phase current of the motor, DC bus voltage, motor rotor position, motor rotor speed and radiator temperature; specifically, current sensor to collect the three-phase current, voltage sensor to collect the DC bus voltage, position sensor to collect the rotor position, speed sensor to collect the rotor speed and temperature sensor to collect the radiator temperature. S2. Perform data preprocessing on the multi-dimensional parameters collected in step S1 to remove noise interference; S3. Extract fault features from the preprocessed multi-dimensional parameters, including current features, voltage features, rotor motion features, and temperature features. S4. Construct a dynamic threshold based on the fault characteristics, and realize online fault diagnosis through multi-feature correlation analysis; S5. Output the fault diagnosis results, which include the fault type and fault warning information.

[0019] In this embodiment, the comprehensiveness and accuracy of fault diagnosis are achieved through multi-dimensional parameter acquisition, data preprocessing, fault feature extraction, dynamic threshold diagnosis, and result output. Specifically, in the parameter acquisition stage, it breaks through the limitations of traditional methods that only collect three-phase current or DC bus voltage, and includes motor rotor position, rotor speed, and radiator temperature in the acquisition range, covering key parameters of the electrical, mechanical, and thermal systems. This provides a complete data source for subsequent multi-dimensional diagnosis. When running at low speed, the rotor position signal can be correlated with mechanical structure deviations, avoiding the problem of not being able to distinguish between electrical and mechanical faults due to current abnormalities. Under high torque loads... The radiator temperature can provide early warning of IGBT overheating risks, compensating for the shortcomings of traditional diagnostic leakage and thermal fault signals; the data preprocessing stage removes noise interference to ensure the reliability of subsequent feature extraction; fault feature extraction focuses on four core features: current, voltage, rotor motion, and temperature, providing quantitative basis for accurate diagnosis; the combination of dynamic thresholds and multi-feature correlation analysis adapts to changes in operating conditions while avoiding misjudgment based on a single feature; finally, the fault type and early warning information are output, providing clear guidance for operation and maintenance; thus, it effectively adapts to the electrical, mechanical, and thermal coupling fault characteristics of low-speed, high-torque motors, providing comprehensive technical support for the safe and stable operation of motors.

[0020] The dynamic threshold is used to distinguish between non-fault parameter fluctuations caused by load shocks and persistent parameter anomalies caused by faults. The multi-feature correlation analysis is used to correlate current features, voltage features, rotor motion features and temperature features to distinguish between electrical faults, mechanical faults and thermal faults.

[0021] In this embodiment, by clarifying the core functions of dynamic threshold and multi-feature correlation analysis, the accuracy of fault diagnosis and the ability to distinguish fault types are further improved. Regarding dynamic threshold, the aim is to solve the problem that traditional fixed thresholds cannot adapt to fluctuations in operating conditions. It explicitly aims to distinguish between non-fault fluctuations caused by load impacts and persistent fault anomalies. When the load on a low-speed, high-torque motor increases suddenly or the speed changes, instantaneous fluctuations in current and voltage are likely to occur. Dynamic threshold can avoid misjudging such non-fault signals as faults, reducing unnecessary downtime. Regarding multi-feature correlation analysis, by establishing the correspondence between current, voltage, rotor motion, temperature characteristics and fault types, accurate fault type classification is achieved: electrical faults are only associated with abnormal current and voltage, mechanical faults are only associated with abnormal rotor motion parameters, and thermal faults are associated with abnormal temperature and electrical parameters in tandem. This solves the problem that traditional diagnosis cannot clearly distinguish the source of the fault.

[0022] The data preprocessing in step S2 includes filtering and dimensionless processing. The filtering uses Kalman filtering or moving average filtering to eliminate high-frequency noise in the multi-dimensional parameters. The dimensionless processing uses max-min normalization or Z-score normalization to unify the numerical magnitude of the multi-dimensional parameters. In this embodiment, by limiting the specific method of data preprocessing, a key guarantee is provided for the accuracy of subsequent fault feature extraction and diagnosis, solving the problem of diagnostic bias caused by noise interference in the original acquired data.

[0023] As a further technical solution, the fault characteristics mentioned in step S3 specifically include: the current imbalance of the three-phase current, the voltage fluctuation coefficient of the DC bus voltage, the position deviation rate of the motor rotor position, the speed fluctuation rate of the motor rotor speed, and the temperature change rate of the radiator temperature.

[0024] Among them, the three-phase current imbalance reflects the degree of symmetry of the three-phase current. The higher the imbalance, the more likely there are electrical faults such as inter-turn short circuits in the windings and current sensor deviations. The DC bus voltage fluctuation coefficient reflects the stability of the bus voltage. If the fluctuation coefficient is too high, there may be electrical faults such as inverter switch failures and unstable power supply voltage. The motor rotor position deviation rate reflects the deviation between the actual rotor position and the ideal position. If the deviation rate is too high, there may be mechanical faults such as gearbox meshing deviations and bearing wear. The motor rotor speed fluctuation rate reflects the stability of the rotor speed. If the fluctuation rate is too high, there may be mechanical faults such as uneven mechanical loads and bearing jamming. The radiator temperature change rate reflects the rate of increase / decrease of the radiator temperature. A sudden increase in the change rate may indicate thermal faults such as radiator blockage and IGBT power module overheating.

[0025] In this embodiment, by clarifying the specific types of fault characteristics, the abstract diagnostic dimensions are transformed into quantifiable and analyzable specific indicators, providing a clear carrier for dynamic threshold construction and multi-feature correlation analysis, and solving the problem of vague traditional diagnostic features and inability to accurately correlate faults.

[0026] Three-phase current Current imbalance The calculation method is as follows: ;in, It is the arithmetic mean of the three-phase currents over one electrical cycle; specifically, , One electrical cycle of a motor is the time it takes for the current to complete one cycle of change. Voltage fluctuation coefficient of DC bus voltage The calculation method is as follows: ;in, , These represent the peak and valley values ​​of the DC bus voltage within a set time window. This is the rated voltage of the DC bus. Position deviation rate of motor rotor position The calculation formula is: ;in, This refers to the actual rotor position collected. The expected rotor position is calculated from the instructions given by the controller; Speed ​​fluctuation rate of motor rotor speed The calculation formula is: ; To set the standard deviation of rotor speed within the time window, The current speed reference value is used; the time window size needs to be matched with the motor's electrical cycle or speed to avoid the window being too large, causing delayed detection of fault characteristics, or too small, causing noise interference. Current and voltage parameters: Set the time window to 1-2 motor electrical cycles. For example, if the motor electrical cycle is 0.02s, set the window to 0.02-0.04s to ensure coverage of the complete cycle change of current / voltage. Rotor speed and position parameters: The time window is set to 5-10 sampling periods to balance fluctuation capture and noise suppression; Temperature parameter: The time window is set to 10-30 seconds. Because the temperature changes slowly, a window that is too short cannot reflect the trend.

[0027] Temperature change rate of radiator temperature The calculation formula is: ;in, The temperature at the current sampling time. The temperature at the previous sampling time. The sampling interval is set based on the rate of change of the parameters.

[0028] In this embodiment, by providing specific calculation formulas for each fault characteristic, the fault characteristics are transformed from qualitative descriptions into quantitative calculation indicators, providing a standardized basis for the consistency and accuracy of diagnostic results. This solves the problems of vague calculation methods and large differences in results across different scenarios in traditional diagnostic characteristic calculation methods. Specifically, for current imbalance, the difference between the maximum and minimum three-phase current values ​​is divided by the periodic average value to accurately quantify the degree of current symmetry, avoiding the neglect of the influence of periodic fluctuations due to simple comparison of current values. The voltage fluctuation coefficient reflects the bus voltage stability by using the ratio of the voltage peak-to-valley difference to the rated voltage within a set time window, adapting to motors of different voltage levels. The diagnostic requirements are met; the position deviation rate is calculated by dividing the difference between the actual and ideal positions by the circumferential angle, converting the position deviation into a percentage for easier standardization; the speed fluctuation rate eliminates differences in speed magnitude by using the ratio of the speed standard deviation to the reference speed, ensuring accurate measurement of speed stability under both high and low speed conditions; the temperature change rate reflects the temperature change trend in real time by using the ratio of the temperature difference between adjacent sampling times to the sampling interval, avoiding overlooking the risk of rapid temperature rise by only looking at the absolute temperature value; all the above formulas consider the physical meaning of the parameters and the operating characteristics of the motor, and the calculation results can accurately map the fault state, providing accurate quantitative data for subsequent dynamic threshold adaptation and correlation analysis.

[0029] The dynamic threshold is used to distinguish between non-fault parameter fluctuations caused by load shocks and persistent parameter anomalies caused by faults. The distinction logic is implemented in the following way: When any fault characteristic quantity exceeds the corresponding dynamic threshold for the first time, a delay timer is started, and the value of the fault characteristic quantity is continuously monitored; If the value of the fault characteristic quantity falls back to within the dynamic threshold before the delay timer expires, the over-threshold is determined to be a non-fault parameter fluctuation caused by load impact, and the timer is reset to zero. The fall-back determination must meet the requirement of continuity, that is, the fault characteristic quantity must be stable within the threshold for 2-3 consecutive sampling cycles, rather than an instantaneous fall. For example, if the current instantly exceeds the threshold and then falls back immediately, but only lasts for 1 sampling cycle, it is not determined to be a fall-back, so as to avoid misjudgment due to signal jitter. If the value of the fault characteristic quantity continues to exceed the dynamic threshold until the delay timer expires, the threshold exceedance is determined to be a persistent parameter abnormality caused by a fault, triggering the fault diagnosis and alarm process. The timeout time needs to be set based on the typical duration of load impact on a low-speed, high-torque motor. Through experimental statistics, the maximum duration of parameter fluctuations for this type of motor under normal load fluctuations, such as a sudden increase in load from 50% to 100%, should be determined. The timeout time should be set to 1.2-1.5 times this maximum duration. For example, if the experimentally measured maximum fluctuation lasts 2 seconds, the timeout time should be set to 2.4-3 seconds to ensure that non-fault fluctuations are excluded without delaying fault determination.

[0030] In this embodiment, by refining the differentiation logic of dynamic thresholds, the ability to distinguish between instantaneous non-fault fluctuations and persistent fault anomalies is further enhanced, reducing the false alarm rate and solving the problem of traditional diagnostics that only alarms when parameters exceed the threshold, ignoring the persistence of fluctuations. Its core lies in the combination of delayed timing and continuous monitoring: when a fault characteristic quantity first exceeds the threshold, a fault is not immediately determined; instead, a delayed timer is started to allow the parameter time to fall back. During low-speed, high-torque motor load impacts, current and voltage often momentarily exceed the threshold, but return to normal within a short time. The above setting avoids false alarms triggered in such non-fault scenarios. Simultaneously, the fallback determination must meet the requirement of persistence to prevent false judgments caused by signal jitter. Only when the parameter continuously exceeds the threshold until the timer expires is it determined to be a fault, ensuring that only real and persistent fault anomalies are captured. This technical solution makes the application of dynamic thresholds more operable, accurately filtering non-fault fluctuations and triggering alarms only for real faults, ensuring that the motor is not interfered with by unnecessary alarms during operation, and improving the practicality and reliability of the diagnostic system.

[0031] The multi-feature association analysis mentioned in step S4 specifically includes: If the current imbalance is greater than the corresponding dynamic threshold, and the position deviation rate is less than or equal to the corresponding dynamic threshold and the temperature change rate is less than or equal to the corresponding dynamic threshold, then it is determined to be an electrical fault. Electrical faults such as winding short circuits and inverter faults only affect the current / voltage signals and will not directly cause rotor mechanical position deviation or a sudden rise in radiator temperature. If the position deviation rate exceeds the threshold, it indicates that the fault may be caused by mechanical structure such as bearing wear leading to position displacement. If the temperature change rate exceeds the threshold, it indicates that it may be accompanied by thermal faults such as IGBT overheating. Therefore, it is necessary to ensure that the position and temperature are normal in order to accurately determine it as a pure electrical fault. If the speed fluctuation rate is greater than the corresponding dynamic threshold, and the position deviation rate is greater than the corresponding dynamic threshold and the current imbalance is less than or equal to the corresponding dynamic threshold, then it is determined to be a mechanical fault. Mechanical faults such as poor gearbox meshing or bearing jamming only affect the speed / position of the rotor movement and will not directly cause three-phase current imbalance. If the current imbalance exceeds the threshold, it indicates that the fault may originate from the electrical system, such as a short circuit between winding turns causing current imbalance. Therefore, it is necessary to limit the current to normal in order to accurately determine it as a pure mechanical fault. If the rate of temperature change is greater than the corresponding dynamic threshold, and the current imbalance is greater than the corresponding dynamic threshold and the voltage fluctuation coefficient is greater than the corresponding dynamic threshold, then it is determined to be a thermal fault. Thermal faults of low-speed, high-torque motors, such as IGBT overheating and heat sink blockage, are mostly caused by abnormal electrical parameters leading to increased power loss. For example, current imbalance leads to increased winding losses, and voltage fluctuations lead to increased inverter losses. Therefore, thermal faults are often accompanied by abnormal current and voltage. If only the rate of temperature change exceeds the threshold but the current and voltage are normal, it may be a temperature sensor fault, i.e., a non-thermal fault. Therefore, it is necessary to limit the current and voltage to exceed the threshold and rule out sensor false alarms.

[0032] By clarifying the specific judgment rules for multi-feature correlation analysis, the logic of "multi-feature coordinated anomaly" is transformed into executable judgment conditions, enabling accurate differentiation of fault types and solving the problem of traditional diagnosis's "single feature judgment of faults, inability to locate the faulty system." Its judgment rules closely integrate the "feature correlation characteristics" of different fault types: electrical faults are only associated with abnormal current, because electrical problems such as winding short circuits and inverter faults do not directly affect the mechanical structure and temperature; therefore, the location and temperature are limited to normal to avoid misjudging mechanical or thermal faults as electrical faults. Mechanical faults are only associated with abnormal rotor motion parameters, because bearing wear and gearbox problems do not affect the electrical system; therefore, the current is limited to normal to eliminate electrical fault interference. Thermal faults require coordinated abnormalities in temperature and electrical parameters, because thermal problems such as IGBT overheating and heat sink blockage are mostly caused by increased power loss due to abnormal electrical parameters; abnormal temperature alone may be a sensor fault; therefore, current and voltage exceeding thresholds are limited to avoid false alarms. This rule design makes fault type determination more rigorous. For example, if only the current imbalance exceeds the threshold, it is clearly an electrical fault, and maintenance can focus on the windings and inverter; if only the speed fluctuation rate and position deviation rate exceed the threshold, it is determined to be a mechanical fault, and the bearings and gearbox can be inspected specifically; if both temperature and electrical parameters exceed the threshold, it is determined to be a thermal fault, and the cooling system can be repaired. Precise fault type differentiation significantly shortens maintenance troubleshooting time, improves fault handling efficiency, and reduces motor downtime losses.

[0033] The formula for calculating the dynamic threshold is: ,in, The dynamic threshold for any fault characteristic. The maximum value of the non-fault parameter is obtained experimentally under standard operating conditions based on the corresponding fault characteristics, using the benchmark threshold. The operating condition correction factor is composed of key real-time operating condition parameters corresponding to the fault characteristics and experimentally calibrated correction coefficients. Its specific expression is as follows: ,in, The number of key operating condition parameters corresponding to the fault characteristics. , For the first The correction coefficients for each operating condition parameter were calibrated through multiple sets of corresponding operating condition experiments. For the first A dimensionless key real-time operating parameter.

[0034] , , , The specific definitions are as follows: when For the three-phase current imbalance hour: , This represents the maximum non-fault current imbalance under rated speed and rated load. , This is the load correction factor. , Tcurr is the real-time output torque, and Trated is the rated torque; DC bus voltage fluctuation coefficient hour: , This represents the maximum value of the non-fault voltage fluctuation coefficient under rated speed and no load fluctuation conditions. , This is the speed correction factor. , For real-time rotor speed, Rated rotor speed; when For the rotor position deviation rate of the motor hour: , This represents the maximum non-faulty position deviation rate under rated torque and stable low-speed operation. , This is the torque correction factor. , The torque threshold is determined through steady-state operating condition experiments, and when ≤ hour, ; when For the rotor speed fluctuation rate of the motor hour: , This represents the maximum non-fault-related speed fluctuation rate at rated speed without mechanical shock. , , is the speed adaptation coefficient. ,and , For low-speed base coefficient, All were calibrated through speed operating condition tests; when For the rate of change of radiator temperature hour: , This represents the maximum non-fault-dependent temperature change rate under no-load and standard ambient temperature conditions. , For load temperature correction factor, This is the ambient temperature correction factor. , Where Tenv is the real-time ambient temperature and Tstd is the standard ambient temperature.

[0035] In this embodiment, the design of a baseline threshold plus a working condition correction factor retains the basic judgment criteria for different fault characteristics while incorporating key parameters such as real-time load rate, speed, and ambient temperature through the working condition correction factor. When the load rate changes, the current imbalance correction factor can adjust the threshold to avoid misjudgment of instantaneous overcurrent caused by a sudden increase in load. When there is a speed deviation, the voltage fluctuation coefficient correction factor can be adapted to the threshold to eliminate non-fault fluctuations during speed switching. When the ambient temperature changes, the temperature change rate correction factor can be adjusted to the threshold to avoid misjudgment of normal temperature fluctuations caused by ambient temperature rise. At the same time, the introduction of correction coefficients and dimensionless working condition parameters makes the formula adaptable to different types of motors. Only by calibrating the baseline threshold and correction coefficients through experiments can it be applied to low-speed, high-torque motors with different rated torques and speeds without redesigning the threshold logic.

[0036] It should be noted that the calculation formulas and all parameters involved in the calculations in this invention have been dimensionless beforehand. The process of dimensionless processing is well known in the industry and will not be described here.

[0037] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for online fault diagnosis in a low-speed, high-torque motor control system, characterized in that, Includes the following steps: S1. Collect the three-phase current, DC bus voltage, motor rotor position, motor rotor speed and radiator temperature of the motor based on the sensor group; S2. Perform data preprocessing on the multi-dimensional parameters collected in step S1 to remove noise interference; S3. Extract fault features from the preprocessed multi-dimensional parameters, including current features, voltage features, rotor motion features, and temperature features. S4. Construct a dynamic threshold based on the fault characteristics, and realize online fault diagnosis through multi-feature correlation analysis; S5. Output the fault diagnosis results, which include the fault type and fault warning information.

2. The online fault diagnosis method for a low-speed, high-torque motor control system according to claim 1, characterized in that, The dynamic threshold is used to distinguish between non-fault parameter fluctuations caused by load shocks and persistent parameter anomalies caused by faults. The multi-feature correlation analysis is used to correlate current features, voltage features, rotor motion features and temperature features to distinguish between electrical faults, mechanical faults and thermal faults.

3. The online fault diagnosis method for a low-speed, high-torque motor control system according to claim 1, characterized in that, The data preprocessing in step S2 includes filtering and dimensionless processing. The filtering uses Kalman filtering or moving average filtering to eliminate high-frequency noise in the multi-dimensional parameters. The dimensionless processing uses maximum-minimum normalization or Z-score normalization to unify the numerical magnitude of the multi-dimensional parameters.

4. The online fault diagnosis method for a low-speed, high-torque motor control system according to claim 3, characterized in that, The fault characteristics mentioned in step S3 specifically include: the current imbalance of the three-phase current, the voltage fluctuation coefficient of the DC bus voltage, the position deviation rate of the motor rotor position, the speed fluctuation rate of the motor rotor speed, and the temperature change rate of the radiator temperature.

5. The online fault diagnosis method for a low-speed, high-torque motor control system according to claim 4, characterized in that, Three-phase current Current imbalance The calculation method is as follows: ;in, It is the arithmetic average of the three-phase currents over one electrical cycle; Voltage fluctuation coefficient of DC bus voltage The calculation method is as follows: ;in, , These represent the peak and valley values ​​of the DC bus voltage within a set time window. This is the rated voltage of the DC bus. Position deviation rate of motor rotor position The calculation formula is: ;in, This refers to the actual rotor position collected. The expected rotor position is calculated from the instructions given by the controller; Speed ​​fluctuation rate of motor rotor speed The calculation formula is: ; To set the standard deviation of rotor speed within the time window, The current speed reference value; Temperature change rate of radiator temperature The calculation formula is: ;in, The temperature at the current sampling time. The temperature at the previous sampling time. The sampling interval is denoted as .

6. The online fault diagnosis method for a low-speed, high-torque motor control system according to claim 2, characterized in that, The dynamic threshold is used to distinguish between non-fault parameter fluctuations caused by load shocks and persistent parameter anomalies caused by faults. The distinction logic is implemented in the following way: When any fault characteristic quantity exceeds the corresponding dynamic threshold for the first time, a delay timer is started, and the value of the fault characteristic quantity is continuously monitored; If the value of the fault characteristic quantity falls back to within the dynamic threshold before the delay timer expires, it is determined that the threshold exceedance is a non-fault parameter fluctuation caused by load impact, and the timer is cleared. If the value of the fault characteristic quantity continues to exceed the dynamic threshold until the delay timer expires, the threshold exceedance is determined to be a continuous parameter abnormality caused by the fault, triggering the fault diagnosis and alarm process.

7. The online fault diagnosis method for a low-speed, high-torque motor control system according to claim 4, characterized in that, The multi-feature association analysis in step S4 specifically includes: If the current imbalance is greater than the corresponding dynamic threshold, and the position deviation rate is less than or equal to the corresponding dynamic threshold and the temperature change rate is less than or equal to the corresponding dynamic threshold, then it is determined to be an electrical fault. If the speed fluctuation rate is greater than the corresponding dynamic threshold, and the position deviation rate is greater than the corresponding dynamic threshold and the current imbalance is less than or equal to the corresponding dynamic threshold, then it is determined to be a mechanical fault. If the rate of temperature change is greater than the corresponding dynamic threshold, and the current imbalance is greater than the corresponding dynamic threshold and the voltage fluctuation coefficient is greater than the corresponding dynamic threshold, then it is determined to be a thermal fault.

8. The online fault diagnosis method for a low-speed, high-torque motor control system according to claim 2 or 6, characterized in that, The formula for calculating the dynamic threshold is: ,in, The dynamic threshold for any fault characteristic. As the baseline threshold, The operating condition correction factor is composed of key real-time operating condition parameters corresponding to the fault characteristics and experimentally calibrated correction coefficients. Its specific expression is as follows: ,in, The number of key operating condition parameters corresponding to the fault characteristics. , For the first Correction coefficients for each operating condition parameter For the first A dimensionless key real-time operating parameter.

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