Method for diagnosing short-circuit fault of surface-mounted permanent magnet synchronous motor under servo working condition

Abstract

The invention discloses a surface-mounted permanent magnet synchronous motor short-circuit fault diagnosis method under a servo working condition, belongs to the field of servo motor fault diagnosis, and aims to solve the problems that harmonic-wave-based surface-mounted permanent magnet synchronous motor short-circuit fault characteristics under the servo working condition are influenced by working conditions, the diagnosis speed is low, and the accuracy is not high. The method comprises the following steps: 1, constructing a motor mathematical model of a servo working condition, deducing a q-axis current analytic expression, and calculating and generating a decoupled theoretical current characteristic signal based on a theoretical coordinate transformation relation; 2, collecting the three-phase current and the rotor position of the motor in real time, and extracting an actual current characteristic signal through a coordinate transformation chain including Clarke transformation, Park transformation, coordinate stretching transformation and Servo-Park transformation; and 3, calibrating the average value and the standard deviation of the actual characteristic signals in the normal state of the motor as a health reference. And 4, during online diagnosis, comparing an actual characteristic signal extracted in real time with a health reference, and carrying out fault judgment according to a preset statistical threshold value.

Description

Technical Field

[0001] This invention relates to a method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions, belonging to the field of servo motor fault diagnosis. Background Technology

[0002] Surface-mount permanent magnet synchronous motors (SPMSMs) are widely used in precision servo applications such as robotics, rail transportation, and aerospace due to their simple structure, high efficiency, and rapid response. However, under complex and harsh operating conditions such as aerospace radiation, high temperatures, and humidity, their core components are prone to failure. This can not only cause motor performance degradation but may even lead to the shutdown of the entire servo system, seriously threatening the system's reliability and safety. Winding short-circuit faults are a typical and frequently occurring type of fault; accurate and timely diagnosis of these faults is crucial for preventing system accidents and ensuring stable operation.

[0003] In high-precision servo applications such as aerospace and robotics, servo motor systems need to smoothly reach a given position within a short time. Under servo conditions, the motor receives sinusoidal position commands and performs reciprocating motion. In this condition, the motor's position, speed, and current loops are coupled, resulting in complex system dynamics. When a winding short-circuit fault occurs, the current harmonic characteristics caused by the fault strongly couple with the time-varying operating condition signals introduced by the position servo commands. This leads to a significant deterioration in the performance of traditional fault diagnosis methods based on constant speed or steady-state harmonic analysis, resulting in difficulties in feature extraction, slow diagnosis speed, and high false alarm rates, severely impacting the reliability of the entire servo system.

[0004] Therefore, researching a method that can effectively decouple fault characteristics from operating conditions and achieve fast, accurate, and reliable online diagnosis of short-circuit faults, tailored to the characteristics of servo operating conditions, has significant engineering value for improving the operational safety and maintainability of high-precision servo systems. Summary of the Invention

[0005] To address the issues of the short-circuit fault characteristics of surface-mount permanent magnet synchronous motors based on harmonics being affected by operating conditions, resulting in slow diagnosis speed and low accuracy under servo conditions, this invention provides a method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo conditions.

[0006] The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions as described in this invention includes the following steps:

[0007] S1. Theoretical current characteristic calculation steps: Construct a mathematical model of the surface-mounted permanent magnet synchronous motor under servo conditions, obtain the analytical expression of the q-axis current, and calculate and generate the decoupled theoretical current characteristic signal based on the theoretical transformation relationship corresponding to the coordinate transformation used in the actual operation of the motor.

[0008] S2. Actual current feature extraction steps: Real-time acquisition of motor operating current and position signals, decoupling through coordinate transformation, and extraction of actual current feature signals;

[0009] S3. Calibration Step: Under normal operating conditions of the motor, calibrate its statistical benchmark under healthy conditions based on the actual current characteristic signal extracted in S2.

[0010] S4. Diagnostic Decision Steps: During online diagnosis, the actual current characteristic signal extracted in real time by S2 is compared with the statistical benchmark calibrated by S3, and the short-circuit fault diagnosis result is output based on the preset statistical threshold.

[0011] Preferably, step S1 specifically includes the following steps:

[0012] S11. Construct the motor kinematics equations and torque equations for servo operating conditions;

[0013] S12. By combining the kinematic equations and torque equations of the electric motor, the q-axis current can be obtained. The analytical expression serves as the mathematical model for a surface-mounted permanent magnet synchronous motor under servo operating conditions.

[0014]

[0015] In the formula, For load torque, For extreme logarithms, It is a permanent magnet flux linkage. For motor inertia, For load inertia, The peak number of cycles of the sinusoidal motion at position. The frequency of the sinusoidal motion of the position;

[0016] S13. Based on the mathematical model, and according to the motor control method adopted... Strategy to obtain virtual d-axis current Virtual q-axis current ;

[0017]

[0018] In the formula, The position sinusoidal characteristic angle under servo operating conditions. , This represents the DC component of the q-axis current.

[0019] S14. Perform Servo-Park transformation to obtain the virtual d-axis current based on the mathematical model. Virtual q-axis current The theoretical current characteristic signal after decoupling in the dq-xy coordinate system was calculated. and :

[0020] ;

[0021] and These are the theoretical current characteristic signals for the dq-x axis and dq-y axis, respectively.

[0022] Preferably, step S2 specifically includes the following steps:

[0023] S21. Real-time acquisition of the motor's operating current and position signal, wherein the operating current is a three-phase current and the position signal is the sinusoidal characteristic angle of the position under servo conditions. ;

[0024] S22. Perform Clarke transformation on the real-time acquired three-phase currents to obtain the αβ axis currents in the two-phase stationary coordinate system.

[0025] S23. Perform Park transformation on the αβ axis current to obtain the dq axis current that rotates with the rotor;

[0026] S24. Perform coordinate scaling transformation on the dq-axis current to obtain the virtual dq-axis current;

[0027] S25. Perform Servo-Park transformation on the virtual dq-axis current to finally extract the actual current characteristic signal in the dq-xy coordinate system. and :

[0028] ;

[0029] and These are the actual current characteristic signals for the dq-x axis and dq-y axis, respectively.

[0030] Preferably, step S3 specifically includes the following steps:

[0031] S31. After confirming that the motor is operating normally and has no short-circuit fault, run step S2 to continuously collect data for at least one complete servo operating cycle. Actual current characteristic signal , ;

[0032] S32, Based on the acquired signal sequence Calculate the average value of the actual current characteristic signal. with standard deviation This serves as the statistical benchmark for calibration; among which For period The total number of sampling points within.

[0033] Preferably, in step S32, the average value is calculated. with standard deviation The formula is:

[0034] .

[0035] Preferably, the preset statistical thresholds in step S4 include a first threshold and a second threshold. The first threshold corresponds to the 3σ principle and is used for rapid diagnosis scenarios. The second threshold corresponds to the 6σ principle and is used for high-precision, low-false-report diagnosis scenarios.

[0036] Preferably, the discrimination logic based on the 3σ principle is as follows: if the real-time actual current characteristic signal... satisfy If so, it is determined to be a fault point.

[0037] Preferably, the discrimination logic based on the 6σ principle is as follows: if the real-time actual current characteristic signal... satisfy If so, it is determined to be a fault point.

[0038] Preferably, in the comparison and discrimination step, if no less than 3 consecutive sampling points are determined to be faulty, then the motor is finally confirmed to have a continuous short circuit fault.

[0039] The beneficial effects of this invention are:

[0040] 1. Effective signal separation under varying operating conditions: The diagnostic method provided by this invention can decompose the current signal strongly coupled to the motor motion under servo operating conditions into deterministic components reflecting the operating conditions and characteristic components characterizing the fault. By constructing a mathematical model specifically for the operating conditions and designing a corresponding coordinate transformation chain, the problem of fault characteristics being masked and difficult to extract in signals under varying operating conditions is fundamentally solved.

[0041] 2. A flexibly configurable dual diagnostic logic is provided: This method allows users to choose between a fast response mode and a high-reliability mode based on actual application needs. The two modes, based on different statistical thresholds (3σ or 6σ), optimize the requirements for diagnostic speed or diagnostic accuracy (low false alarm rate), respectively, enhancing the method's practicality and applicability.

[0042] 3. Improved engineering practicality of online diagnostics: The entire diagnostic process is computationally efficient and easily integrated into existing motor controllers for real-time operation. Furthermore, the method incorporates a fault confirmation mechanism based on continuous sampling points, effectively filtering out occasional false alarms caused by transient interference, significantly improving the stability and reliability of diagnostic results in engineering applications.

[0043] 4. Possesses a clear theoretical foundation and physical interpretability: Each step of the method is derived from explicit electrical principles and mathematical models, and the decoupling process and fault criteria have clear physical meaning. This model-based design approach makes the diagnostic process transparent, the results traceable, and facilitates understanding, debugging, and verification by engineers. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the diagnostic process for short-circuit fault diagnosis of a surface-mount permanent magnet synchronous motor under servo operating conditions, as described in this invention.

[0045] Figure 2 This is a waveform diagram of the position under servo operating conditions according to the present invention;

[0046] Figure 3 This is a waveform diagram of the rotational speed under servo operating conditions according to the present invention;

[0047] Figure 4 This is an analytical waveform diagram of the q-axis current under servo operating conditions according to the present invention;

[0048] Figure 5 This is an analytical waveform diagram of the three-phase current under servo operating conditions according to the present invention;

[0049] Figure 6 This is a diagnostic effect diagram of the method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to the present invention. Detailed Implementation

[0050] 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.

[0051] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0052] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0053] Specific Implementation Method 1: The following is combined with... Figures 1 to 6 This embodiment describes a method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions. The core architecture of this invention is as follows: Figure 1As shown, its innovation lies in constructing a dual-parallel signal processing and decision-making process: one path generates a "theoretical current characteristic signal" based on theoretical models and parameters, serving as the benchmark for diagnosis and the basis for design; the other path extracts an "actual current characteristic signal" based on real-time sensor data, serving as the object of online monitoring. The two signals are combined through subsequent calibration and statistical decision-making steps to jointly achieve rapid and accurate diagnosis of short-circuit faults.

[0054] In this embodiment, the motor is a surface-mounted permanent magnet synchronous motor (SPMSM) and operates in sinusoidal position tracking mode.

[0055] I. Generation of theoretical current characteristic signals (corresponding to step S1)

[0056] This step is performed during the system design, debugging, or initialization phase. Its purpose is not to process real-time signals, but rather to derive and calculate, based on first principles and known parameters, the theoretical value that the current signal should exhibit after the specific decoupling transformation of this invention under ideal, fault-free conditions. This theoretical value is the cornerstone of the entire diagnostic method.

[0057] 1. Constructing a mathematical model:

[0058] First, the input commands for the servo operating conditions are defined. In this invention, the position command is a sinusoidal signal, such as... Figure 2 As shown. Motor mechanical position. Represented as:

[0059] (1)

[0060] In the formula, and These are the mechanical position and time of the motor, respectively. and These are the peak number of cycles and the frequency of the sinusoidal motion at position, respectively, which are known parameters given by the servo system.

[0061] Secondly, by differentiating the position, the mechanical angular velocity of the motor can be obtained. The corresponding speed waveform is as follows Figure 3 As shown:

[0062] (2)

[0063] Next, the motor kinematics equations and torque equations for the position servo working condition are constructed.

[0064] When operating under servo load conditions, neglecting the effects of viscous friction, the kinematic equations of the permanent magnet synchronous motor are:

[0065] (3)

[0066] In the formula, and These are the motor inertia and the load inertia, respectively. and These are electromagnetic torque and load torque, respectively.

[0067] Considering that the reluctance torque component of the surface-mount motor can be ignored, the motor torque equation is:

[0068] (4)

[0069] In the formula, For extreme logarithms, It is a permanent magnet flux linkage. This is the q-axis current.

[0070] By combining the kinematic equations (3) and torque equations (4) of the motor, the theoretical analytical expression for the q-axis current required for the drive motor to track a given position command can be obtained:

[0071] (5)

[0072] Equation (5) is the mathematical model of the surface-mounted permanent magnet synchronous motor under servo conditions.

[0073] 2. Based on model decoupling calculation, theoretical current characteristics are generated.

[0074] The purpose of this step is to pre-calculate, based on known operating conditions and motor system parameters, the theoretical value that the decoupled current characteristics should exhibit under absolutely ideal, fault-free conditions during the offline or initialization phase. This theoretical value serves as the design basis and verification benchmark for the entire diagnostic method.

[0075] The theoretical transformation relationship corresponding to the coordinate transformation used in the actual operation of the motor is as follows:

[0076] First, based on coordinate scaling transformation, the dq current is transformed to a virtual dq coordinate system, specifically as follows:

[0077] (6)

[0078] In the formula, and These are the virtual dq-axis currents, This represents the DC component of the q-axis current.

[0079] Then, the surface-mount permanent magnet synchronous motor adopts =0 control, the virtual dq axis current satisfies the following relationship.

[0080] (7)

[0081] Finally, the virtual dq-axis current is transformed from the rotated virtual dq coordinate system to the twice-rotated dq-xy coordinate system according to the Servo-Park transformation. The Servo-Park transformation is as follows:

[0082] (8)

[0083] In the formula, and These are the currents in the dq-xy coordinate system after two rotations.

[0084] Based on the theoretical transformation relationship corresponding to the coordinate transformation used in the actual operation of the motor, the decoupled theoretical current characteristic signal is calculated and generated. Substituting the aforementioned theoretical relationship into this series of theoretical transformation formulas, a rigorous mathematical derivation is performed. Ultimately, all alternating terms related to time t and operating conditions are analytically canceled out, yielding a concise analytical solution for the theoretical current characteristic signal in the dq-xy coordinate system.

[0085] Specifically, without considering current harmonics and error interference, the currents in the decoupled dq-xy coordinate system after two rotations are both DC, and the mathematical model formula (5) is... Substitute into formula (7), and then convert formula (7) to... , Substituting into formula (8), the theoretical current characteristic signal after decoupling in the dq-xy coordinate system is obtained. and :

[0086] (9)

[0087] The above process is Figure 1 On the right side, the load component is separated based on a mathematical model to obtain the theoretical current characteristics of decoupled operating condition information. Using the decoupling method designed in this invention, under ideal fault-free conditions, the decoupled signal... The above rules should be strictly followed, and It should always be zero. This provides a fundamental theoretical basis for subsequent diagnostic criteria.

[0088] II. Steps for calculating actual current characteristics (corresponding to step S2)

[0089] This step is performed online and in real time while the motor is running, and it is the core of the diagnostic system. Its purpose is to extract the pure "actual current characteristic signal" from the measured signal, which is mixed with dynamic operating conditions, noise, and potential fault components.

[0090] 1. Real-time signal acquisition

[0091] The three-phase stator current of the motor is sampled in real time using a current sensor. , , Simultaneously, the real-time mechanical position of the motor rotor is obtained through position sensors (such as photoelectric encoders). Based on the known servo command frequency Simultaneous calculation of the sinusoidal characteristic angle of the position .

[0092] Under servo conditions, the electrical position, mechanical position, and sinusoidal position characteristic angle of the permanent magnet synchronous motor should satisfy specific relationships, specifically:

[0093] (10)

[0094] In the formula, , These are the electric position of the motor and the mechanical position.

[0095] 2. Actual decoupling based on coordinate transformation chain

[0096] like Figure 1 As shown in the lower left diagram, the core of the decoupling method in this step is a coordinate transformation chain: Clarke transformation → Park transformation → coordinate scaling transformation → Servo-Park transformation. Through this transformation chain, the three-phase current strongly coupled to the operating condition is converted into a dq-xy coordinate system signal that theoretically contains only a DC component. When a short-circuit fault occurs, an abnormal AC component will appear in this signal.

[0097] The specific steps for performing a series of coordinate transformations on the acquired real-time signals are as follows:

[0098] (1) Perform Clarke transformation on the real-time acquired three-phase currents to obtain the αβ axis currents in the two-phase stationary coordinate system;

[0099] (11)

[0100] In the formula, and These are the α and β axis currents, respectively. , , It is a three-phase current;

[0101] The purpose of this Clarke transformation step is to reduce the dimensionality of the variables and eliminate the influence of the neutral point current.

[0102] (2) Perform Park transformation on the αβ axis current to obtain the dq axis current that rotates with the rotor;

[0103] (12)

[0104] In the formula, and These are the dq-axis currents, respectively.

[0105] (3) Perform coordinate scaling transformation on the dq-axis current to obtain the virtual dq-axis current;

[0106] (13)

[0107] In the formula, and These are the virtual dq-axis currents, This represents the DC component of the q-axis current.

[0108] Surface-mount permanent magnet synchronous motor adopts =0 control, the virtual dq axis current satisfies the following relationship.

[0109] (14)

[0110] The coordinate scaling transformation in this step is crucial. This transformation aims to proactively eliminate the effects introduced by the servo operating conditions themselves. and Periodic components in for The DC component can be obtained in real time by low-pass filtering the signal or by averaging it over a complete operating cycle. This step is the core of successfully decoupling operating conditions from fault characteristics.

[0111] (4) Perform Servo-Park transformation on the virtual dq-axis current to finally extract the actual current characteristic signal in the dq-xy coordinate system. and :

[0112] (15)

[0113] In the formula, and These are the currents in the dq-xy coordinate system after two rotations.

[0114] After the above four-step transformation, the actual current characteristic signal is extracted during motor operation. When the motor is healthy, the actual current characteristic signal should fluctuate slightly around the theoretical value calculated by S1; when a winding short-circuit fault occurs, an abnormal AC component appears in the decoupled dq-xy coordinate system current of the two rotations, and the DC component also differs significantly from the theoretical value of the decoupled dq-xy coordinate system current.

[0115] III. Health Status Assessment (corresponding to step S3)

[0116] To accommodate individual differences and inherent noise in specific motors and drives, and to make diagnostic thresholds more robust, offline calibration is required during initial system commissioning or regular maintenance.

[0117] 1. Calibration conditions: Ensure that the motor and driver are in perfect health and operating under typical, stable servo conditions.

[0118] 2. Data Acquisition: Perform the actual feature extraction step (S2) in Part Two, continuously collecting data for at least one complete operating cycle. Actual current characteristic signal , .

[0119] 3. Statistical baseline calculation: For the collected data... Data points Calculate its statistical characteristics (mean) according to the following formula. with standard deviation ):

[0120] (16)

[0121] average value The standard deviation represents the central location of a signal in a healthy state. This characterizes the normal fluctuation range of a signal under healthy conditions caused by various non-fault disturbances (such as noise and ripple). The calculated average value... with standard deviation As a "health fingerprint" or statistical benchmark for subsequent online diagnosis.

[0122] IV. Online Fault Diagnosis Decision (corresponding to step S4)

[0123] When running online, the system executes S2 (real-time feature extraction) and this step (S4, diagnostic decision) in parallel.

[0124] 1. Real-time feature sampling: In each control cycle, the actual current characteristic signal value at the current moment is obtained through step S2. .

[0125] 2. Threshold determination: Read the calibrated baseline value. and This invention provides two configurable, statistically based discrimination logics.

[0126] Fast Response Mode: If satisfied If so, the current point is determined to be the fault point. Therefore, the limitation and This mode simultaneously meets the requirements. Based on the 3σ principle, it features high sensitivity and fast response, making it suitable for scenarios with stringent requirements for diagnostic latency (such as high-end CNC machine tools).

[0127] High reliability mode: if satisfied If so, the current point is determined to be the fault point. Therefore, the limitation and This mode simultaneously meets the requirements. Based on the 6σ principle, the threshold is extremely strict, which can suppress false alarms to the greatest extent. It is suitable for scenarios with extremely high system reliability requirements and where a certain diagnostic delay is acceptable (such as spacecraft attitude control).

[0128] 3. Fault Confirmation and Output: To eliminate false alarms caused by momentary interference, this invention employs a continuous judgment mechanism. The diagnostic system maintains a counter; only when at least three consecutive sampling points are identified as fault points does the system finally confirm a continuous short-circuit fault in the motor, triggering a high-level alarm, recording the fault log, or initiating safety protection procedures (such as reduced power operation or safe shutdown). This mechanism greatly improves the engineering reliability of the diagnostic results.

[0129] Example effect verification:

[0130] The method of this invention was applied to a surface-mount permanent magnet synchronous motor on an experimental platform. Figure 6 This demonstrates typical diagnostic results. During the healthy operation phase, the actual current characteristic signal after decoupling is shown. Stable around the calibrated average value Minor fluctuations. After an artificial short-circuit fault was injected into the winding at time t=103.953s, The signal immediately exhibited significant abnormal jumps and oscillations, with its amplitude rapidly exceeding... and Upon reaching the threshold, the diagnostic system immediately (after confirmation through consecutive points) issues an accurate short-circuit fault alarm. Experimental results fully demonstrate that the method of this invention possesses excellent feature extraction capabilities, rapid diagnostic response speed, and extremely high reliability for short-circuit faults under the complex and variable operating conditions of position servo systems.

[0131] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions, characterized in that, Includes the following steps: S1. Theoretical current characteristic calculation steps: Construct a mathematical model of the surface-mounted permanent magnet synchronous motor under servo conditions, obtain the analytical expression of the q-axis current, and calculate and generate the decoupled theoretical current characteristic signal based on the theoretical transformation relationship corresponding to the coordinate transformation used in the actual operation of the motor. S2. Actual current feature extraction steps: Real-time acquisition of motor operating current and position signals, decoupling through coordinate transformation, and extraction of actual current feature signals; S3. Calibration Step: Under normal motor operation, calibrate the statistical benchmark of its healthy state based on the actual current characteristic signal extracted in S2. S4. Diagnostic Decision Steps: During online diagnosis, the actual current characteristic signal extracted in real time by S2 is compared with the statistical benchmark calibrated by S3, and the short-circuit fault diagnosis result is output based on the preset statistical threshold.

2. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11. Construct the motor kinematics equations and torque equations under servo operating conditions; S12. Solve the kinematic equations and torque equations of the electric motor simultaneously to obtain the q-axis current. The analytical expression serves as the mathematical model for a surface-mounted permanent magnet synchronous motor under servo operating conditions. In the formula, For load torque, For extreme logarithms, It is a permanent magnet flux linkage. For motor inertia, For load inertia, The peak number of cycles of the sinusoidal motion at position. The frequency of the sinusoidal motion of the position; S13. Based on the mathematical model, and according to the motor control method adopted... Strategy to obtain virtual d-axis current Virtual q-axis current ; In the formula, The position sinusoidal characteristic angle under servo operating conditions. , This represents the DC component of the q-axis current. S14. Perform Servo-Park transformation to obtain the virtual d-axis current based on the mathematical model. Virtual q-axis current The theoretical current characteristic signal after decoupling in the dq-xy coordinate system was calculated. and : ； and These are the theoretical current characteristic signals for the dq-x axis and dq-y axis, respectively.

3. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to claim 2, characterized in that, Step S2 specifically includes the following steps: S21. Real-time acquisition of the motor's operating current and position signal, wherein the operating current is a three-phase current and the position signal is the sinusoidal position characteristic angle under servo conditions. ; S22. Perform Clarke transformation on the real-time acquired three-phase currents to obtain the αβ axis currents in the two-phase stationary coordinate system. S23. Perform Park transformation on the αβ axis current to obtain the dq axis current that rotates with the rotor; S24. Perform coordinate scaling transformation on the dq-axis current to obtain the virtual dq-axis current; S25. Perform Servo-Park transformation on the virtual dq-axis current to finally extract the actual current characteristic signal in the dq-xy coordinate system. and : ； and These are the actual current characteristic signals for the dq-x axis and dq-y axis, respectively.

4. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to claim 3, characterized in that, Step S3 specifically includes the following steps: S31. After confirming that the motor is operating normally and has no short-circuit fault, run step S2 to continuously collect data for at least one complete servo operating cycle. Actual current characteristic signal , ; S32, Based on the acquired signal sequence Calculate the average value of the actual current characteristic signal. with standard deviation This serves as the statistical benchmark for calibration; among which For period The total number of sampling points within.

5. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to claim 4, characterized in that, In step S32, the average value is calculated. with standard deviation The formula is: 。 6. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to claim 5, characterized in that, The preset statistical thresholds in step S4 include a first threshold and a second threshold. The first threshold corresponds to the 3σ principle and is used for rapid diagnosis scenarios. The second threshold corresponds to the 6σ principle and is used for high-precision, low-false-report diagnosis scenarios.

7. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to claim 6, characterized in that, The discrimination logic based on the 3σ principle is as follows: if the real-time actual current characteristic signal... satisfy If so, it is determined to be a fault point.

8. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to claim 6, characterized in that, The discrimination logic based on the 6σ principle is as follows: if the real-time actual current characteristic signal... satisfy If so, it is determined to be a fault point.

9. The method for diagnosing short-circuit faults in surface-mount permanent magnet synchronous motors under servo operating conditions according to any one of claims 6 to 8, characterized in that, In the comparison and discrimination step, if no less than 3 consecutive sampling points are determined to be faulty, then it is finally confirmed that the motor has a continuous short circuit fault.

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