A detection system for abnormal operation of electric drive axle motor in new energy commercial vehicles

Abstract

This invention discloses a detection system for abnormal operation of electric drive axle motors in new energy commercial vehicles, belonging to the field of motor anomaly detection technology. The system includes: a multi-source sensing module, a dynamic coupling analysis module, a harmonic distortion tracing module, a dynamic stability analysis module, an anomaly fusion decision module, an adaptive threshold generation module, and a fault tree reasoning module. This invention synchronously collects multi-dimensional signals such as current, vibration, temperature, and rotor position angle through the multi-source sensing module, and performs multi-physics field collaborative analysis through the dynamic coupling analysis module and the harmonic distortion tracing module. This improves the accuracy of fault diagnosis for abnormal operation of electric drive axle motors in new energy commercial vehicles, solving the problem of existing technologies relying solely on single physical quantity detection, which is susceptible to interference leading to misjudgments or missed detections.

Description

Technical Field

[0001] This invention relates to the field of motor anomaly detection technology, and in particular to a detection system for abnormal operation of electric drive axle motors in new energy commercial vehicles. Background Technology

[0002] As the core of the power system for new energy commercial vehicles, the accurate detection of the electric drive axle motor is crucial for driving safety and reliability. Existing motor anomaly detection technologies mostly rely on monitoring a single physical quantity, such as using three-phase current harmonic analysis to determine winding faults or identifying mechanical damage based on vibration acceleration spectrum. These solutions, due to their limited detection dimensions, cannot comprehensively characterize the complex operating states of the motor, especially under the variable operating conditions of commercial vehicles (such as heavy-load hill climbing and frequent start-stop cycles), revealing significant limitations.

[0003] The core drawback of single-physical-quantity detection lies in its insufficient anti-interference capability. Taking current detection as an example, grid harmonic pollution or power device switching noise can easily lead to current spectrum distortion, causing false alarms in fault diagnosis based on current harmonics. Vibration detection, on the other hand, is affected by non-fault factors such as road bumps and vibration transmission from the suspension system, causing real faults such as bearing wear to be masked by noise and missed. A single parameter cannot reflect the early fault characteristics under the combined effect of multiple fields, resulting in weak identification capabilities of existing technologies in the early stages of anomalies, making it difficult to meet the high reliability requirements of commercial vehicles. Summary of the Invention

[0004] This application provides a detection system for abnormal operation of electric drive axle motors in new energy commercial vehicles. This system solves the problem in the prior art that relies on the detection of only a single physical quantity, which is susceptible to interference and may lead to misjudgment or missed detection. It improves the accuracy of fault diagnosis for abnormal operation of electric drive axle motors in new energy commercial vehicles.

[0005] This application provides a detection system for abnormal operation of electric drive axle motor in new energy commercial vehicles, including: a multi-source sensing module, a dynamic coupling analysis module, a harmonic distortion tracing module, a dynamic stability analysis module, an anomaly fusion decision module, an adaptive threshold generation module, and a fault tree reasoning module.

[0006] The multi-source sensing module is used to collect data in real time on the three-phase current, speed, load torque, rotor position angle, winding temperature and bearing vibration acceleration of the electric drive axle motor of new energy commercial vehicles.

[0007] The dynamic coupling analysis module is used to generate magnetic field asymmetry and stress concentration factor based on rotor position angle, winding temperature and bearing vibration acceleration.

[0008] The harmonic distortion tracing module is used to calculate the harmonic distortion factor based on the three-phase current.

[0009] The dynamic stability analysis module is used to calculate the Lyapunov exponent based on the rotor position angle and rotational speed;

[0010] The anomaly fusion decision module is used to output a comprehensive anomaly index based on magnetic field asymmetry, stress concentration factor, harmonic distortion factor and Lyapunov index.

[0011] The adaptive threshold generation module is used to adjust the adaptive threshold based on the rotational speed and load torque;

[0012] The fault tree reasoning module is used to trigger an anomaly warning when the comprehensive anomaly index is greater than the anomaly threshold, and to associate the anomaly with the minimum fault cut set.

[0013] Furthermore, the steps for obtaining the magnetic field asymmetry in the dynamic coupling analysis module include:

[0014] The air gap magnetic flux density distribution is collected by the rotor position angle. The circumference is divided into several monitoring points. The relative deviation between the magnetic flux density of each monitoring point and the average magnetic flux density is calculated. The square root of the average of the squares is then taken to obtain the basic component of the magnetic flux density asymmetry.

[0015] The spatial gradient of the winding temperature is obtained based on the winding temperature.

[0016] Obtain the thermomagnetic coupling coefficient and calculate the magnetic field asymmetry.

[0017] The specific calculation formula is as follows:

[0018]

[0019] Where Ψ is the magnetic field asymmetry, θ is the rotor position angle, N is the total number of monitoring points, k is the monitoring point number, and B k (θ) represents the magnetic flux density at the monitoring point. For the average magnetic flux density, T w For winding temperature, η represents the temperature spatial gradient of the winding, and η is the thermomagnetic coupling coefficient.

[0020] Furthermore, the method for obtaining the thermomagnetic coupling coefficient is as follows:

[0021] At the saturation magnetic flux density point of the motor core, the partial derivatives of the magnetic field asymmetry with respect to the winding temperature and the magnetic flux density are calculated respectively. The thermomagnetic coupling coefficient is then calculated based on these two sets of partial derivatives. The specific formula is as follows:

[0022]

[0023] Among them, B sat Where B is the saturation magnetic flux density, and A is the magnetic flux density. This is a partial derivative symbol.

[0024] Furthermore, the stress concentration factor is obtained as follows:

[0025] Under no-load conditions, the baseline value of bearing vibration acceleration is collected as a vibration reference during normal operation.

[0026] When the motor is under load, the real-time value of bearing vibration acceleration is collected, the relative deviation between the value and the baseline value of bearing vibration acceleration is calculated, and the average value is obtained by integrating over the time interval to obtain the stress concentration factor.

[0027] The specific calculation formula is as follows:

[0028]

[0029] Where σ is the stress concentration factor, μ a a is the baseline value of bearing vibration acceleration. v (t) represents the real-time value of the bearing vibration acceleration, [t1,t2] represents the time interval between the time start point t1 and the time end point t2, and t is the time variable.

[0030] Furthermore, the step of obtaining the harmonic distortion factor includes:

[0031] Spectral analysis of the three-phase current is performed to obtain the fault state energy and total harmonic energy under fault conditions, and their relative proportions are calculated.

[0032] By taking the logarithm of the ratio of fault-state energy to calibration-state energy, the difference in abnormal energy is amplified, and the harmonic distortion factor is obtained.

[0033] The formula for calculating the harmonic distortion factor is:

[0034]

[0035] Among them, Γ h Harmonic distortion factor Let k be the fault state energy. Total harmonic energy, The calibration state energy is given by h, where h is the fault harmonic order number and j is the number of all harmonic orders. max This represents the highest harmonic order across the entire frequency band.

[0036] Furthermore, the harmonic energy also includes a temperature compensation mechanism:

[0037] Using the reference temperature T0 as a benchmark, the effect of temperature deviation on the calibration state energy is calculated using the temperature coefficient of resistance ω, and the corrected calibration state energy is obtained.

[0038] The temperature compensation formula for the harmonic energy is:

[0039]

[0040] in, The corrected harmonic energy is T, where ò is the temperature coefficient of resistance. w T0 is the winding temperature, and T0 is the reference temperature.

[0041] Furthermore, the steps for obtaining the Lyapunov index include:

[0042] A three-dimensional phase space is constructed using q-axis current, rotational speed, and rotor position angle;

[0043] The Lyapunov exponent is calculated by iteratively calculating the ratio of the magnitudes of the offset vectors at adjacent time points and taking the natural logarithmic average. The formula is as follows:

[0044]

[0045] Where Λ is the Lyapunov exponent, δx(t) g Let δx(t) be the state offset vector at time g. g-1 Let be the state offset vector at time g-1, and M be the total number of evolution steps of the phase space trajectory, representing the time from the initial time t0 to the final time t0. M The number of sampling points between them, g is the step index, and the phase space offset vector δx = [i q ,ω,θ] T i q Let ω be the q-axis current, ω be the rotational speed, and θ be the rotor position angle.

[0046] Furthermore, the steps for obtaining the comprehensive anomaly index include:

[0047] By weighting and summing the magnetic field asymmetry, stress concentration factor, critical harmonic distortion factor, and Lyapunov exponent, we obtain the formula for the comprehensive anomaly index:

[0048]

[0049] Where Φ is the comprehensive anomaly index, Ψ is the magnetic field asymmetry, and σ is the stress concentration factor. Here, h represents the critical harmonic distortion factor, and h is the fault harmonic order number. max e represents the total number of fault harmonic orders. Λ Let α, β, γ, and κ be the exponent terms of the Lyapunov exponent, and let α, β, γ, and κ be the optimal weights.

[0050] Furthermore, the adaptive threshold adjustment step includes:

[0051] Based on the baseline threshold, and taking into account the normalized speed and load torque, combined with speed weights and load weights, the adaptive threshold is adjusted using the following formula:

[0052]

[0053] Where, Φ th For adaptive thresholding, Φ0 is the baseline threshold, and ω is the rotational speed. max For the rated speed, As for rotational speed weighting, Let τ be the load weight, and τ be the load torque. max This represents the maximum torque.

[0054] Furthermore, the minimum fault cut set is:

[0055] When the ratio of the fifth harmonic distortion factor to the third harmonic distortion factor is significantly higher than the baseline ratio during normal operation, and the stress concentration factor exceeds the preset abnormal vibration stress threshold, bearing wear is determined.

[0056] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0057] 1. By synchronously collecting multi-dimensional signals such as current, vibration, temperature, and rotor position angle through multi-source sensing modules, and conducting multi-physics field collaborative analysis through dynamic coupling analysis modules and harmonic distortion tracing modules, the accuracy of fault diagnosis of abnormal operation of electric drive axle motors in new energy commercial vehicles is improved. This effectively solves the problem that existing technologies rely on the detection of only a single physical quantity, which is easily affected by interference and leads to misjudgment or missed detection.

[0058] 2. By collecting the air gap magnetic flux density distribution through the rotor position angle, the winding temperature spatial gradient and thermomagnetic coupling coefficient are introduced to construct a magnetic field asymmetry calculation model; at the same time, the harmonic energy reference value is corrected based on the temperature coefficient of resistance to achieve bidirectional temperature compensation of electromagnetic characteristics.

[0059] 3. By calculating the stress concentration factor through the unloaded vibration baseline and the load vibration integral, and combining the ratio of the fifth and third harmonic distortion factors, a dual-condition minimum cut set judgment rule for faults is constructed. This enables multi-index cross-verification of mechanical faults and avoids misjudgment or missed detection caused by the susceptibility of a single vibration or harmonic index to interference from non-fault factors.

[0060] 4. The abnormal threshold is dynamically adjusted based on the rotational speed and load torque. At the same time, the phase space is constructed by q-axis current, rotational speed and rotor position angle. The Lyapunov exponent is used to quantify the system trajectory divergence rate to realize dynamic stability assessment under operating condition fluctuations. This solves the problems that fixed thresholds cannot adapt to variable operating conditions and traditional analysis methods are difficult to capture the early chaotic characteristics of the system. Attached Figure Description

[0061] Figure 1 This is a structural diagram of a detection system for abnormal operation of an electric drive axle motor in a new energy commercial vehicle, provided in an embodiment of this application. Detailed Implementation

[0062] This application provides a detection system for abnormal operation of electric drive axle motors in new energy commercial vehicles. It solves the problem that existing technologies rely on the detection of only a single physical quantity, which is susceptible to interference and may lead to misjudgment or missed detection. By using a multi-source sensing module to synchronously collect multi-dimensional signals such as current, vibration, temperature, and rotor position angle, and by using a dynamic coupling analysis module and a harmonic distortion tracing module to perform multi-physical field collaborative analysis, the system improves the accuracy of fault diagnosis for abnormal operation of electric drive axle motors in new energy commercial vehicles.

[0063] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0064] like Figure 1 As shown in the figure, this application provides a detection system for abnormal operation of electric drive axle motor in new energy commercial vehicles, including: a multi-source sensing module, a dynamic coupling analysis module, a harmonic distortion tracing module, a dynamic stability analysis module, an anomaly fusion decision module, an adaptive threshold generation module, and a fault tree reasoning module;

[0065] The multi-source sensing module is used to collect data in real time on the three-phase current, speed, load torque, rotor position angle, winding temperature and bearing vibration acceleration of the electric drive axle motor of new energy commercial vehicles.

[0066] The dynamic coupling analysis module is used to generate magnetic field asymmetry and stress concentration factor based on rotor position angle, winding temperature and bearing vibration acceleration.

[0067] The harmonic distortion tracing module is used to calculate the harmonic distortion factor based on the three-phase current.

[0068] The dynamic stability analysis module is used to calculate the Lyapunov exponent based on the rotor position angle and rotational speed;

[0069] The anomaly fusion decision module is used to output a comprehensive anomaly index based on magnetic field asymmetry, stress concentration factor, harmonic distortion factor and Lyapunov index.

[0070] The adaptive threshold generation module is used to adjust the adaptive threshold based on the rotational speed and load torque;

[0071] The fault tree reasoning module is used to trigger an anomaly warning when the comprehensive anomaly index is greater than the anomaly threshold, and to associate the anomaly with the minimum fault cut set.

[0072] Furthermore, the steps for obtaining the magnetic field asymmetry in the dynamic coupling analysis module include:

[0073] The air gap magnetic flux density distribution is collected by the rotor position angle. The circumference is divided into several monitoring points. The relative deviation between the magnetic flux density of each monitoring point and the average magnetic flux density is calculated. The square root of the average of the squares is then taken to obtain the basic component of the magnetic flux density asymmetry.

[0074] The spatial gradient of the winding temperature is obtained based on the winding temperature.

[0075] Obtain the thermomagnetic coupling coefficient and calculate the magnetic field asymmetry.

[0076] The specific calculation formula is as follows:

[0077]

[0078] Where Ψ is the magnetic field asymmetry, θ is the rotor position angle, N is the total number of monitoring points, k is the monitoring point number, and B k (θ) represents the magnetic flux density at the monitoring point. For the average magnetic flux density, T w For winding temperature, η represents the temperature spatial gradient of the winding, and η is the thermomagnetic coupling coefficient.

[0079] By introducing a thermomagnetic coupling coefficient, the spatial gradient of winding temperature is incorporated into the calculation. This is because the temperature gradient leads to non-uniformity in the core permeability, exacerbating magnetic field distortion. A dual structure of "statistical magnetic flux density deviation + thermomagnetic coupling correction" is used to achieve dynamic characterization of magnetic field asymmetry.

[0080] Furthermore, the method for obtaining the thermomagnetic coupling coefficient is as follows:

[0081] At the saturation magnetic flux density point of the motor core, the partial derivatives of the magnetic field asymmetry with respect to the winding temperature and the magnetic flux density are calculated respectively. The thermomagnetic coupling coefficient is then calculated based on these two sets of partial derivatives. The specific formula is as follows:

[0082]

[0083] Among them, B sat Where B is the saturation magnetic flux density, and A is the magnetic flux density. This is a partial derivative symbol.

[0084] The ratio of the two reflects the relative influence of temperature change on magnetic field distortion. When the magnetic flux density is saturated, the change in permeability caused by temperature is more significant. At this time, η can accurately characterize the thermomagnetic coupling strength.

[0085] Furthermore, the stress concentration factor is obtained as follows:

[0086] Under no-load conditions, the baseline value of bearing vibration acceleration is collected as a vibration reference during normal operation.

[0087] When the motor is under load, the real-time value of bearing vibration acceleration is collected, the relative deviation between the value and the baseline value of bearing vibration acceleration is calculated, and the average value is obtained by integrating over the time interval to obtain the stress concentration factor.

[0088] The specific calculation formula is as follows:

[0089]

[0090] Where σ is the stress concentration factor, μ a a is the baseline value of bearing vibration acceleration. v (t) represents the real-time value of the bearing vibration acceleration, [t1,t2] represents the time interval between the time start point t1 and the time end point t2, and t is the time variable.

[0091] By using a "baseline comparison-time integration" method, instantaneous vibration anomalies are transformed into cumulative stress concentration indicators, effectively filtering high-frequency noise interference and highlighting the vibration characteristics of progressive faults such as bearing wear.

[0092] Furthermore, the step of obtaining the harmonic distortion factor includes:

[0093] Spectral analysis of the three-phase current is performed to obtain the fault state energy and total harmonic energy under fault conditions, and their relative proportions are calculated.

[0094] By taking the logarithm of the ratio of fault-state energy to calibration-state energy, the difference in abnormal energy is amplified, and the harmonic distortion factor is obtained.

[0095] The formula for calculating the harmonic distortion factor is:

[0096]

[0097] Among them, Γ h Harmonic distortion factor Let k be the fault state energy. Total harmonic energy, The calibration state energy is given by h, where h is the fault harmonic order number and j is the number of all harmonic orders. max This represents the highest harmonic order across the entire frequency band.

[0098] This makes the distortion of specific harmonics (such as the 3rd, 5th, and 7th harmonics) more distinguishable in the comprehensive evaluation.

[0099] Furthermore, the harmonic energy also includes a temperature compensation mechanism:

[0100] Using the reference temperature T0 as a benchmark, the effect of temperature deviation on the calibration state energy is calculated using the temperature coefficient of resistance ω, and the corrected calibration state energy is obtained.

[0101] The temperature compensation formula for the harmonic energy is:

[0102]

[0103] in, The corrected harmonic energy is T, where ò is the temperature coefficient of resistance. w T0 is the winding temperature, and T0 is the reference temperature.

[0104] Since changes in winding temperature alter coil resistance and thus affect harmonic energy distribution, dynamic correction of the calibration-state harmonic energy is necessary. When the actual winding temperature exceeds the reference temperature, increased resistance causes harmonic energy attenuation; therefore, [1+ò(T] is used to adjust the harmonic energy level. w The -T0)] factor lowers the reference energy value to ensure consistency in the calculation reference of the harmonic distortion factor under different temperature conditions.

[0105] Furthermore, the steps for obtaining the Lyapunov index include:

[0106] A three-dimensional phase space is constructed using q-axis current, rotational speed, and rotor position angle;

[0107] The Lyapunov exponent is calculated by iteratively calculating the ratio of the magnitudes of the offset vectors at adjacent time points and taking the natural logarithmic average. The formula is as follows:

[0108]

[0109] Where Λ is the Lyapunov exponent, δx(t) g Let δx(t) be the state offset vector at time g. g-1 Let be the state offset vector at time g-1, and M be the total number of evolution steps of the phase space trajectory, representing the time from the initial time t0 to the final time t0. M The number of sampling points between them, g is the step index, and the phase space offset vector δx = [i q ,ω,θ] T i q Let ω be the q-axis current, ω be the rotational speed, and θ be the rotor position angle.

[0110] The Lyapunov exponent is the exponential divergence rate of the system trajectory. When Λ > 0, it indicates that the system has entered a chaotic state, which foreshadows mechanical failure or control instability.

[0111] Furthermore, the steps for obtaining the comprehensive anomaly index include:

[0112] By weighting and summing the magnetic field asymmetry, stress concentration factor, critical harmonic distortion factor, and Lyapunov exponent, we obtain the formula for the comprehensive anomaly index:

[0113]

[0114] Where Φ is the comprehensive anomaly index, Ψ is the magnetic field asymmetry, and σ is the stress concentration factor. Here, h represents the critical harmonic distortion factor, and h is the fault harmonic order number. max e represents the total number of fault harmonic orders. Λ Let α, β, γ, and κ be the exponent terms of the Lyapunov exponent, and let α, β, γ, and κ be the optimal weights.

[0115] The weighting coefficients are obtained by maximizing F. β Score optimization:

[0116]

[0117] Where e Λ The effect of stability degradation is amplified by exponential operations; with F β Using the score as the objective function, and based on the weight adjustment factor β, the precision and recall of the binary classification problem of motor operation abnormality are balanced. The optimal weights α, β, γ, and κ are determined through an optimization algorithm, realizing a three-level fusion of "multi-physical quantity coupling - nonlinear mapping - intelligent weight optimization" to improve the robustness of anomaly detection.

[0118] The steps for obtaining the weighting adjustment factor β include:

[0119] Collect motor operating data containing different fault types (such as bearing wear, winding short circuit), label real anomalies, and build a training set;

[0120] Iterate through the range of β values ​​and calculate F for each β. β For the fraction, select the β value corresponding to the maximum value as the optimal value;

[0121] In real-vehicle testing, the changes in precision and recall are monitored in real time using a confusion matrix, and the β value is dynamically fine-tuned based on false alarms / missed detections.

[0122] Furthermore, the adaptive threshold adjustment step includes:

[0123] Based on the baseline threshold, and taking into account the normalized speed and load torque, combined with speed weights and load weights, the adaptive threshold is adjusted using the following formula:

[0124]

[0125] Where, Φ th For adaptive thresholding, Φ0 is the baseline threshold, and ω is the rotational speed. max For the rated speed, As for rotational speed weighting, Let τ be the load weight, and τ be the load torque. max This represents the maximum torque.

[0126] When the motor approaches its rated speed ω max Or maximum torque τ max At this time, mechanical stress and electromagnetic loss intensify, and the range of fluctuations in physical quantities during normal operation expands. Therefore, the threshold is nonlinearly increased by the square term to avoid false alarms under high load conditions and achieve adaptive adjustment of the threshold.

[0127] Furthermore, the minimum fault cut set is:

[0128] When the ratio of the fifth harmonic distortion factor to the third harmonic distortion factor is significantly higher than the baseline ratio during normal operation, and the stress concentration factor exceeds the preset abnormal vibration stress threshold, bearing wear is determined.

[0129] Bearing wear can cause rotor eccentricity, significantly increasing the energy of the fifth harmonic relative to the third harmonic. Anomalies in magnetic flux density asymmetry can be identified by the ratio of these two harmonics deviating from the normal range. Mechanical shocks caused by wear can cause vibration acceleration to deviate from the baseline value; bearing damage is reflected when the accumulated stress concentration factor exceeds the vibration threshold. Both conditions must be met simultaneously to rule out misjudgments caused by a single factor (such as grid harmonic interference or instantaneous vibration).

[0130] In summary, the embodiments of this application synchronously collect multi-dimensional signals such as current, vibration, temperature, and rotor position angle through a multi-source sensing module, and perform multi-physics field collaborative analysis through a dynamic coupling analysis module and a harmonic distortion tracing module, thereby improving the fault diagnosis accuracy of abnormal operation of electric drive axle motors in new energy commercial vehicles.

[0131] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0136] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A new energy commercial vehicle electric drive axle motor operation abnormality detection system, characterized in that, The method comprises the following steps: A multi-source sensing module, a dynamic coupling analysis module, a harmonic distortion tracing module, a dynamic stability analysis module, an abnormality fusion decision module, an adaptive threshold generation module, and a fault tree reasoning module are included. The multi-source sensing module is configured to collect three-phase current, speed, load torque, rotor position angle, winding temperature, and bearing vibration acceleration in real time when the motor of the electric drive axle of the new energy commercial vehicle is running. The dynamic coupling analysis module is configured to generate magnetic field asymmetry and stress concentration coefficient based on the rotor position angle, winding temperature, and bearing vibration acceleration. The harmonic distortion tracing module is configured to calculate the harmonic distortion factor based on the three-phase current. The dynamic stability analysis module is configured to calculate the Lyapunov exponent based on the rotor position angle and speed. The abnormality fusion decision module is configured to output a comprehensive abnormality index based on the magnetic field asymmetry, stress concentration coefficient, harmonic distortion factor, and Lyapunov exponent. The adaptive threshold generation module is configured to adjust the adaptive threshold based on the speed and load torque. The fault tree reasoning module is configured to trigger an abnormality warning when the comprehensive abnormality index is greater than the abnormality threshold, and to associate the abnormality to a fault minimal cut set. The fault minimal cut set is: When the ratio of the fifth harmonic distortion factor to the third harmonic distortion factor is significantly higher than the baseline ratio under normal operation, and the stress concentration coefficient exceeds the pre-set vibration stress abnormality threshold, it is determined that the bearing is worn.

2. The detection system for abnormal operation of the motor of the electric drive axle of the new energy commercial vehicle according to claim 1, characterized in that, The magnetic field asymmetry in the dynamic coupling analysis module includes the following steps: The air gap magnetic flux density distribution is collected through the rotor position angle, the circumference is evenly divided into monitoring points, the relative deviation of the magnetic flux density of each monitoring point from the average magnetic flux density is calculated, the square sum is averaged, and the root is taken to obtain the basic component of the magnetic flux density asymmetry. The winding temperature spatial gradient is obtained according to the winding temperature. The thermal-magnetic coupling coefficient is obtained, and the magnetic field asymmetry is calculated. The specific calculation formula is: where, Ψ is the magnetic field asymmetry, θ is the rotor position angle, N is the total number of monitoring points, k is the monitoring point number, B k (θ) is the magnetic flux density of the monitoring point, is the average magnetic flux density, T w is the winding temperature, is the winding temperature spatial gradient, and η is the thermal-magnetic coupling coefficient.

3. The system for detecting abnormal operation of the motor of the electric drive axle of the new energy commercial vehicle according to claim 2, characterized in that, The method for obtaining the thermal-magnetic coupling coefficient is: At the saturation magnetic flux density point of the motor core, the partial derivative of the magnetic field asymmetry with respect to the winding temperature and the partial derivative of the magnetic field asymmetry with respect to the magnetic flux density are calculated respectively, and the thermal-magnetic coupling coefficient is calculated according to the two sets of partial derivatives. The specific formula is: where B sat is the saturation magnetic flux density, B is the magnetic flux density, is the partial derivative symbol.

4. The system for detecting abnormal operation of the motor of the electric drive axle of the new energy commercial vehicle according to claim 1, characterized in that, The stress concentration coefficient is obtained in the following way: Under no-load operating conditions, the baseline value of bearing vibration acceleration is collected as the vibration reference under normal operation. When the motor is under load, the real-time value of bearing vibration acceleration is collected, the relative deviation of the real-time value from the baseline value of bearing vibration acceleration is calculated, and the stress concentration coefficient is obtained by integrating and averaging the relative deviation in a time interval. The specific calculation formula is: wherein σ is a stress concentration factor, μ a is a bearing vibration acceleration baseline value, a v (t) is a bearing vibration acceleration real-time value, [t1, t2] is a time interval from a time start t1 to a time end t2, and t is a time variable.

5. The new energy commercial vehicle electric drive axle motor operation abnormality detection system of claim 1, wherein, The harmonic distortion factor is obtained in the following steps: The three-phase current is subjected to frequency spectrum analysis to obtain the fault state energy and total harmonic energy under fault state, and the relative proportion is calculated. The ratio of the fault state energy to the calibration state energy is taken as the logarithm to amplify the difference characteristics of abnormal energy, and the harmonic distortion factor is obtained. The harmonic distortion factor calculation formula is: where Γ h is the harmonic distortion factor, is the kth fault state energy, is the total harmonic energy, is the rated state energy, h is the fault harmonic order number, and j is all harmonic order numbers, j max is the highest harmonic order of the full frequency band.

6. The new energy commercial vehicle electric drive axle motor operation abnormality detection system of claim 5, wherein, The harmonic energy also includes a temperature compensation mechanism. With reference to the reference temperature T0, the temperature coefficient of resistance is used The influence of the temperature deviation on the calibration state energy is calculated to obtain a corrected calibration state energy. The temperature compensation formula of the harmonic energy is: wherein, is the corrected harmonic energy, is the temperature coefficient of resistance, T w is the winding temperature, T0 is the reference temperature.

7. The new energy commercial vehicle electric drive axle motor operation abnormality detection system of claim 1, wherein, The Lyapunov exponent is obtained in the following steps: A three-dimensional phase space is constructed with the q-axis current, speed, and rotor position angle. The Lyapunov exponent is calculated by iteratively calculating the ratio of the lengths of the offset vectors at adjacent time instants and taking the natural logarithm average, and the formula is: where Λ is the Lyapunov exponent, δx(t g ) is the state deviation vector at the gth moment, δx(t g-1 ) is the state deviation vector at the (g-1)th moment, M is the total number of evolution steps of the phase space trajectory, represents the number of sampling points between the initial moment t0 and the terminal moment t M , g is the step index, the phase space deviation vector δx=[i q ,ω,θ] T , i q is the q-axis current, ω is the rotational speed, and θ is the rotor position angle.

8. The new energy commercial vehicle electric drive axle motor operation abnormality detection system of claim 1, wherein, The step of obtaining the comprehensive abnormality index comprises: The formula of the comprehensive abnormality index is obtained by weighted summation of the magnetic field asymmetry, the stress concentration coefficient, the key harmonic distortion factor and the Lyapunov exponent index term: Wherein, Φ is the comprehensive abnormal index, Ψ is the magnetic field asymmetry, σ is the stress concentration coefficient, is the key harmonic distortion factor and h is the fault harmonic number, max is the total number of fault harmonics, e Λ is the Lyapunov index exponential term, α, β, γ, κ are the optimal weights.

9. The new energy commercial vehicle electric drive axle motor operation abnormality detection system of claim 1, wherein, The step of adjusting the adaptive threshold value comprises: The adaptive threshold value is adjusted on the basis of the reference threshold value, according to the normalized rotational speed and load torque, in combination with the rotational speed weight and the load weight, and the formula is: wherein Φ th is an adaptive threshold, Φ0 is a reference threshold, ω is a rotational speed, ω max is a rated rotational speed, θ1 is a rotational speed weight, θ2 is a load weight, τ is a load torque, τ max is a maximum torque.

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