Motor eccentric fault diagnosis method and system based on current harmonic analysis

Abstract

The application relates to the technical field of motor diagnosis, and discloses a motor eccentric fault diagnosis method and system based on current harmonic analysis. The method comprises the following steps: extracting a power frequency component of a three-phase stator current signal to obtain a residual current signal, and performing complex analytic envelope transformation on the residual current signal to obtain an envelope analytic signal; performing synchronous interpolation resampling on the envelope analytic signal to obtain a complex envelope signal; performing spectrum estimation on the complex envelope signal to obtain a discrete spectrum of a modulus sequence; performing equalization processing on the order spectrum line amplitude in the discrete spectrum to obtain a whitened spectrum, and inversely transforming and reconstructing the whitened spectrum into an angular domain amplitude signal of a target motor; performing cyclic stationary analysis on the angular domain amplitude signal to obtain a third-order cyclic cumulative quantity of the angular domain amplitude signal at a specific cycle frequency; and determining the type and severity level of the eccentric fault according to the third-order cyclic cumulative quantity. The application can improve the accuracy of motor eccentric fault diagnosis.

Description

Technical Field

[0001] This invention relates to the field of motor diagnostic technology, and in particular to a method and system for diagnosing motor eccentricity faults based on current harmonic analysis. Background Technology

[0002] In the field of motor eccentricity fault diagnosis, traditional methods based on current harmonic analysis generally suffer from insufficient signal preprocessing accuracy. Traditional methods do not thoroughly remove the dominant power frequency component from the three-phase stator current signal, and tend to over-filter out weak fault-related feature information during the denoising process, resulting in low purity of the residual current signal. At the same time, in the complex analytical envelope transformation stage, there is a lack of effective coupling analysis of the instantaneous phase and instantaneous amplitude envelope, which makes the generated envelope signal unable to accurately characterize the potential features of the eccentricity fault, thereby reducing the basic reliability of subsequent fault analysis.

[0003] Existing technologies also have significant limitations in the spectrum analysis and fault feature extraction stages. When estimating the spectrum of the magnitude sequence, the influence of boundary effects is not sufficiently suppressed, and the characterization of the intrinsic correlation of the signal is not comprehensive enough, making it difficult to effectively distinguish the order of fault features from diffuse background noise in the discrete spectrum. In addition, traditional methods lack targeted equalization processing for the discrete spectrum, and the spectral symmetry condition is not satisfied during the reconstruction of the angular domain signal, which limits the extraction accuracy of the third-order cyclic cumulant and makes it impossible to accurately capture fault features at specific cyclic frequencies. Ultimately, this leads to inaccurate judgment of eccentric fault types and ambiguous severity levels, making it difficult to meet the high-precision requirements of motor fault diagnosis in industrial scenarios. Therefore, how to improve the accuracy of motor fault diagnosis has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for diagnosing motor eccentricity faults based on current harmonic analysis, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for diagnosing motor eccentricity faults based on current harmonic analysis, comprising:

[0006] S1. Extract the power frequency component from the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor, and perform complex analytic envelope transformation on the residual current signal to obtain the envelope analytic signal of the target motor.

[0007] S2. Based on the rotor speed signal of the target motor, the real and imaginary components in the envelope analysis signal are synchronously interpolated and resampled to obtain the complex envelope signal of the target motor.

[0008] S3. Perform spectral estimation on the modulus sequence in the complex envelope signal to obtain the discrete spectrum of the modulus sequence;

[0009] S4. Equalize the order spectral line amplitudes in the discrete spectrum to obtain the whitened spectrum of the target motor, and reconstruct the whitened spectrum into the angular domain amplitude signal of the target motor by inverse transformation;

[0010] S5. Perform cyclic stationarity analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative amount of the angular domain amplitude signal at a specific cyclic frequency in the target motor;

[0011] S6. Based on the cumulative amount of the third-order cycle, determine the type and severity level of the eccentricity fault in the target motor.

[0012] In a preferred embodiment, the step of extracting the power frequency component of the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor includes:

[0013] Acquire the three-phase stator current signal of the target motor;

[0014] Based on the frequency, amplitude, and phase of the three-phase stator current signal, construct the power frequency quadrature reference signal of the target motor;

[0015] Based on the power frequency quadrature reference signal, the power frequency dominant component in the three-phase stator current signal is removed to obtain the preliminary residual current signal of the target motor.

[0016] The initial residual current signal is subjected to wavelet threshold denoising to obtain the residual current signal of the target motor.

[0017] In a preferred embodiment, performing a complex analytic envelope transform on the residual current signal to obtain the envelope analytic signal of the target motor includes:

[0018] The residual current signal is subjected to Hilbert transform to obtain the orthogonal Hilbert components of the target motor;

[0019] Using the residual current signal as the real part and the orthogonal Hilbert component as the imaginary part, construct the analytical signal of the target motor;

[0020] The analytical signal is subjected to phase dewinding processing to obtain the instantaneous phase information of the target motor;

[0021] The analytical signal is decomposed into modes to obtain the instantaneous amplitude envelope information of the analytical signal;

[0022] Based on the instantaneous amplitude envelope information, the instantaneous phase information is coupled and analyzed to obtain the envelope analytical signal of the target motor.

[0023] In a preferred embodiment, the step of synchronously interpolating and resampling the real and imaginary components of the envelope analytic signal based on the rotor speed signal of the target motor to obtain the complex envelope signal of the target motor includes:

[0024] The rotor speed signal of the target motor is digitally integrated to obtain the rotor phase information of the target motor;

[0025] Based on the rotor phase information, a pulse sequence with an equal angle to the mechanical rotation of the target motor is generated;

[0026] Based on the equal-angle incremental pulse sequence, cubic spline interpolation is performed on the real and imaginary components of the envelope analytic signal to obtain the real and imaginary components of the envelope analytic signal in the angular domain.

[0027] The real part signal and the imaginary part signal of the angular domain are synchronously combined to obtain the complex envelope signal of the target motor.

[0028] In a preferred embodiment, the step of performing spectral estimation on the modulus sequence in the complex envelope signal to obtain the discrete spectrum of the modulus sequence includes:

[0029] Boundary effect suppression is applied to the magnitude sequence in the complex envelope signal to obtain the target sequence of the complex envelope signal;

[0030] Statistical features are extracted from the target sequence to obtain an intrinsic correlation characterization of the target sequence;

[0031] Energy analysis is performed on the core parameters representing the intrinsic correlation to obtain the residual energy measure of the core parameters;

[0032] Based on the core parameters and the residual energy metric, the modulus sequence is parameterized to obtain the continuous spectral density of the modulus sequence;

[0033] Within a preset frequency range, the continuous spectral density is uniformly and discretely sampled to obtain the discrete spectrum of the modulus sequence.

[0034] In a preferred embodiment, the formula for calculating the continuous spectral density is: ;

[0035] in, Indicates at angular frequency The continuous spectral density at that location, This refers to the core parameters. Represents the natural constant. Represents the imaginary unit. The basic time metric unit representing the modulus sequence. This represents the preset spectral smoothing adjustment factor. Indicates the first The complex conjugate of the aforementioned core parameters, This represents the residual energy metric.

[0036] In a preferred embodiment, the step of equalizing the amplitudes of the order spectral lines in the discrete spectrum to obtain the whitened spectrum of the target motor, and then reconstructing the whitened spectrum into the angular domain amplitude signal of the target motor by inverse transformation, includes:

[0037] Spectral structure analysis is performed on the spectral line energy distribution in the discrete spectrum to obtain the dominant characteristic order region and diffuse background noise region of the discrete spectrum;

[0038] Based on the dominant characteristic order region and the diffuse background noise region, a spectral line amplitude adjustment strategy for the discrete spectrum is formulated.

[0039] Based on the spectral line amplitude adjustment strategy, a decision mapping is performed on the order spectral line amplitudes of the discrete spectrum to obtain the whitening spectrum of the target motor.

[0040] The whitened spectrum is reconstructed from the frequency domain to the angular domain, and the spectral symmetry condition required for the signal reconstruction is satisfied to obtain the preliminary angular domain sequence of the whitened spectrum;

[0041] Phase trajectory calibration is performed on the preliminary angular domain sequence to obtain the angular domain amplitude signal of the target motor.

[0042] In a preferred embodiment, the step of reconstructing the whitened spectrum from the frequency domain to the angular domain, and satisfying the spectral symmetry condition required for the signal reconstruction, to obtain a preliminary angular domain sequence of the whitened spectrum, includes:

[0043] When the spectral amplitude distribution of the whitening spectrum does not satisfy the conjugate symmetry relationship, the spectral lines in the positive order region of the whitening spectrum are mirrored and copied to obtain the symmetrical spectrum of the whitening spectrum.

[0044] Perform an inverse transformation operation from the frequency domain to the angular domain on the symmetric spectrum and the whitened spectrum that satisfies the conjugate symmetry relationship to obtain the inverse transformation intermediate sequence of the whitened spectrum;

[0045] The imaginary part energy level of the inverse transform intermediate sequence is obtained by performing parameterized spectral estimation on the inverse transform intermediate sequence;

[0046] Based on the imaginary part energy level, the inverse transform intermediate sequence is fine-tuned with symmetry compensation to obtain the optimized intermediate sequence of the whitened spectrum;

[0047] The real part in the optimized intermediate sequence is used as the initial angular domain sequence of the whitening spectrum.

[0048] In a preferred embodiment, the step of performing cyclostationary analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative quantity of the angular domain amplitude signal at a specific cyclic frequency in the target motor includes:

[0049] Based on the rotation period of the target motor, the angular domain amplitude signal is segmented into angular domain analysis sub-segments with overlapping regions;

[0050] Based on the cyclic frequency of the corner domain analysis segment, the cyclic cumulative amount in the corner domain analysis segment is estimated point by point to obtain the initial cumulative amount spectrum corresponding to the cyclic frequency;

[0051] By filtering out noise from the initial cumulative spectrum, a refined cumulative spectrum of the angular domain analysis segment is obtained;

[0052] By performing feature mapping on the number of pole pairs and rotational frequency of the target motor, the fault characteristic cycle frequency of the target motor is obtained;

[0053] Based on the fault characteristic cycle frequency, the spectral lines of the refined cumulative quantity spectrum are read to obtain the third-order cyclic cumulative quantity of the target motor.

[0054] To address the aforementioned problems, this invention also provides a motor eccentricity fault diagnosis system based on current harmonic analysis, the system comprising:

[0055] The signal processing module is used to extract the power frequency component of the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor, and to perform complex analytic envelope transformation on the residual current signal to obtain the envelope analytic signal of the target motor.

[0056] The signal interpolation processing module is used to synchronously interpolate and resample the real and imaginary components in the envelope analysis signal based on the rotor speed signal of the target motor, so as to obtain the complex envelope signal of the target motor.

[0057] The spectrum estimation module is used to perform spectrum estimation on the magnitude sequence in the complex envelope signal to obtain the discrete spectrum of the magnitude sequence;

[0058] The spectrum transformation module is used to perform equalization processing on the order spectral line amplitudes in the discrete spectrum to obtain the whitened spectrum of the target motor, and to perform inverse transformation on the whitened spectrum to reconstruct the angular domain amplitude signal of the target motor.

[0059] The amplitude signal analysis module is used to perform cyclic stationarity analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative amount of the angular domain amplitude signal at a specific cyclic frequency in the target motor.

[0060] The fault severity analysis module is used to determine the type and severity level of the eccentric fault in the target motor based on the third-order cyclic cumulative amount.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. This invention significantly improves diagnostic efficiency through a precise signal processing procedure. It first extracts the power frequency component and performs wavelet threshold denoising on the three-phase stator current signal to effectively remove interference components. Then, through complex analytical envelope transformation and synchronous interpolation resampling, the signal is transformed into a more easily analyzable angular domain form. Subsequent spectrum estimation and whitened spectrum reconstruction steps further enhance the identification of fault features. The entire process requires no complex and redundant operations, greatly shortening the time from signal acquisition to feature extraction, and achieving efficient advancement of fault diagnosis.

[0063] 2. This invention significantly improves the accuracy and comprehensiveness of fault diagnosis by deeply mining the inherent characteristics of signals; by using cyclostationary analysis to obtain the third-order cyclic cumulative quantity at a specific cyclic frequency, it can accurately capture the unique characteristics of different types of eccentric faults. Combined with order spectral amplitude equalization processing and angular domain amplitude signal reconstruction, the severity level of the fault can be clearly defined, providing accurate and reliable decision-making basis for motor maintenance, effectively avoiding missed or misjudged cases, and ensuring the safety and stability of motor operation. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating a method for diagnosing motor eccentricity faults based on current harmonic analysis, provided in an embodiment of the present invention.

[0065] Figure 2 A functional block diagram of a motor eccentricity fault diagnosis system based on current harmonic analysis provided in an embodiment of the present invention;

[0066] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0068] This application provides a method for diagnosing motor eccentricity faults based on current harmonic analysis. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for diagnosing motor eccentricity faults based on current harmonic analysis can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0069] Reference Figure 1 The diagram shown is a flowchart illustrating a motor eccentricity fault diagnosis method based on current harmonic analysis according to an embodiment of the present invention. In this embodiment, the motor eccentricity fault diagnosis method based on current harmonic analysis includes:

[0070] S1. Extract the power frequency component from the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor, and perform complex analytic envelope transformation on the residual current signal to obtain the envelope analytic signal of the target motor.

[0071] In this embodiment of the invention, the step of extracting the power frequency component of the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor includes:

[0072] Acquire the three-phase stator current signal of the target motor;

[0073] Based on the frequency, amplitude, and phase of the three-phase stator current signal, construct the power frequency quadrature reference signal of the target motor;

[0074] Based on the power frequency quadrature reference signal, the power frequency dominant component in the three-phase stator current signal is removed to obtain the preliminary residual current signal of the target motor.

[0075] The initial residual current signal is subjected to wavelet threshold denoising to obtain the residual current signal of the target motor.

[0076] The step of performing a complex analytic envelope transform on the residual current signal to obtain the envelope analytic signal of the target motor includes:

[0077] The residual current signal is subjected to Hilbert transform to obtain the orthogonal Hilbert components of the target motor;

[0078] Using the residual current signal as the real part and the orthogonal Hilbert component as the imaginary part, construct the analytical signal of the target motor;

[0079] The analytical signal is subjected to phase dewinding processing to obtain the instantaneous phase information of the target motor;

[0080] The analytical signal is decomposed into modes to obtain the instantaneous amplitude envelope information of the analytical signal;

[0081] Based on the instantaneous amplitude envelope information, the instantaneous phase information is coupled and analyzed to obtain the envelope analytical signal of the target motor.

[0082] By installing high-precision current sensors at the output terminals of the three-phase stator windings of the target motor, the current changes in the three-phase stator windings during motor operation are captured in real time, and the specific values ​​of the current in each phase at different time points are continuously recorded to form a complete and continuous three-phase stator current signal of the target motor.

[0083] Based on the collected three-phase stator current signal, the specific frequency, amplitude and phase parameters are determined by signal feature extraction. Based on these determined parameters, two signals with frequencies that are completely consistent with the power frequency and are perpendicular to each other are constructed. These two perpendicular signals are the power frequency orthogonal reference signals of the target motor.

[0084] The constructed power frequency orthogonal reference signal is compared with the original three-phase stator current signal. Using signal separation technology, the power frequency dominant component corresponding to the power frequency orthogonal reference signal in the three-phase stator current signal is completely separated. The remaining signal after separation is the preliminary residual current signal of the target motor.

[0085] The initial residual current signal is decomposed into multiple wavelet coefficients of different scales. A fixed threshold is set according to the characteristics of noise in the signal. All wavelet coefficients with values ​​less than the threshold are set to zero, while wavelet coefficients with values ​​greater than the threshold are retained. The processed wavelet coefficients are then subjected to inverse wavelet transform. The residual current signal of the target motor with noise interference removed is reconstructed through the inverse transform.

[0086] The obtained residual current signal is integrated, and a signal with a phase difference of exactly 90 degrees from the residual current signal is obtained through integration. This signal with a phase difference of 90 degrees is the orthogonal Hilbert component of the target motor.

[0087] The original residual current signal is used directly as the real part, and the newly acquired orthogonal Hilbert component is used as the imaginary part. The real and imaginary parts are combined according to the rules of complex number construction to form the analytical signal of the target motor in complex form.

[0088] First, the original phase data of the analytical signal is extracted. When the original phase data shows a jump amplitude exceeding π, the phase jump is corrected by accumulating the corresponding multiple of 2π to eliminate the phase jump phenomenon, so that the phase data can show a smooth and continuous change trend, and finally the instantaneous phase information of the target motor is obtained.

[0089] The analytical signal is decomposed using empirical mode decomposition, which breaks it down into several intrinsic mode functions. The amplitude changes of each intrinsic mode function are tracked and analyzed to extract the instantaneous amplitude envelope information of the target motor that can accurately reflect the overall amplitude change law of the analytical signal.

[0090] The extracted instantaneous amplitude envelope information and instantaneous phase information are synchronously correlated, and their mutual influence and interaction in the time dimension are analyzed in depth. Based on this interaction, the two types of information are integrated and processed to finally obtain the envelope analytical signal of the target motor that can comprehensively reflect the signal characteristics.

[0091] The beneficial effects are as follows: the above implementation process accurately acquires three-phase stator current signals and constructs a power frequency orthogonal reference signal based on the signal's own parameters, achieving complete removal of the dominant power frequency component. Simultaneously, wavelet threshold denoising effectively removes noise while preserving weak fault-related features, solving the problems of incomplete power frequency component removal and excessive denoising in traditional methods. In the complex analytical envelope transformation process, steps such as Hilbert transform, phase decoupling, mode decomposition, and coupling analysis fully explore the correlation between instantaneous phase information and instantaneous amplitude envelope information. The generated envelope analytical signal can accurately characterize the potential features of eccentric faults, improving the basic reliability of subsequent fault analysis and providing strong support for the accuracy of the entire motor eccentric fault diagnosis method.

[0092] S2. Based on the rotor speed signal of the target motor, the real and imaginary components in the envelope analysis signal are synchronously interpolated and resampled to obtain the complex envelope signal of the target motor.

[0093] In this embodiment of the invention, the step of synchronously interpolating and resampling the real and imaginary components of the envelope parse signal based on the rotor speed signal of the target motor to obtain the complex envelope signal of the target motor includes:

[0094] The rotor speed signal of the target motor is digitally integrated to obtain the rotor phase information of the target motor;

[0095] Based on the rotor phase information, a pulse sequence with an equal angle to the mechanical rotation of the target motor is generated;

[0096] Based on the equal-angle incremental pulse sequence, cubic spline interpolation is performed on the real and imaginary components of the envelope analytic signal to obtain the real and imaginary components of the envelope analytic signal in the angular domain.

[0097] The real part signal and the imaginary part signal of the angular domain are synchronously combined to obtain the complex envelope signal of the target motor.

[0098] The continuous values ​​of the rotor speed signal during the operation of the target motor are obtained, and these speed values ​​are accumulated at fixed time intervals. By continuously accumulating these values, the rotor rotation angle data that changes dynamically over time is obtained, which is the rotor phase information of the target motor.

[0099] Based on the obtained rotor phase information, a fixed angle increment value is set. When the change in rotor phase reaches the fixed angle increment value, a pulse signal is generated. As the rotor continues to rotate, pulse signals are continuously generated, eventually forming an equal angle increment pulse sequence that perfectly matches the mechanical rotation rhythm of the motor.

[0100] Using the time nodes corresponding to the generated equiangular increment pulse sequence as a reference, smooth cubic polynomial curves are constructed between two adjacent known data points for the real and imaginary components of the envelope analytic signal. Based on these curves, the signal values ​​at the corresponding angular time for each pulse are calculated. In this way, the angular real and angular imaginary components of the envelope analytic signal are obtained respectively.

[0101] The values ​​corresponding to the same rotation angle in the real part signal and the imaginary part signal of the angular domain are paired. The value of the real part signal of the angular domain is used as the real part of the complex number, and the value of the imaginary part signal of the angular domain is used as the imaginary part of the complex number. Each pair of values ​​is combined according to the rules of complex number construction to form a complete complex number signal. This signal is the complex envelope signal of the target motor.

[0102] The beneficial effects are that the above implementation process accurately obtains rotor phase information by digitally integrating the rotor speed signal. The equal-angle incremental pulse sequence generated based on this information can be precisely synchronized with the mechanical rotation of the motor. Then, the real and imaginary components of the envelope analysis signal are accurately resampled through cubic spline interpolation, ensuring the accuracy and smoothness of the real and imaginary signals in the angular domain. Finally, the complex envelope signal obtained by synchronous composite can accurately reflect the signal characteristics during the motor rotation process, effectively avoiding the influence of speed fluctuations on signal analysis. This lays a high-quality signal foundation for subsequent spectrum estimation and fault feature extraction, and improves the accuracy of motor eccentricity fault diagnosis.

[0103] S3. Perform spectral estimation on the modulus sequence in the complex envelope signal to obtain the discrete spectrum of the modulus sequence;

[0104] In this embodiment of the invention, the step of performing spectral estimation on the modulus sequence in the complex envelope signal to obtain the discrete spectrum of the modulus sequence includes:

[0105] Boundary effect suppression is applied to the magnitude sequence in the complex envelope signal to obtain the target sequence of the complex envelope signal;

[0106] Statistical features are extracted from the target sequence to obtain an intrinsic correlation characterization of the target sequence;

[0107] Energy analysis is performed on the core parameters representing the intrinsic correlation to obtain the residual energy measure of the core parameters;

[0108] Based on the core parameters and the residual energy metric, the modulus sequence is parameterized to obtain the continuous spectral density of the modulus sequence;

[0109] Within a preset frequency range, the continuous spectral density is uniformly and discretely sampled to obtain the discrete spectrum of the modulus sequence.

[0110] The formula for calculating the continuous spectral density is:

[0111] ;

[0112] in, Indicates at angular frequency The continuous spectral density at that location, This refers to the core parameters. Represents the natural constant. Represents the imaginary unit. The basic time metric unit representing the modulus sequence. This represents the preset spectral smoothing adjustment factor. Indicates the first The complex conjugate of the aforementioned core parameters, This represents the residual energy metric.

[0113] When suppressing boundary effects in the modulus sequence of a complex envelope signal, based on the data change trends at both ends of the modulus sequence, a certain length of extended data is added to the beginning and end of the sequence. The extended data is consistent with the numerical change pattern of the edge data of the original sequence, so that the beginning and end of the entire sequence can transition smoothly, avoiding signal distortion caused by abrupt changes in the data at both ends, thereby effectively suppressing the influence of boundary effects and finally obtaining the target sequence of the complex envelope signal.

[0114] When extracting statistical features from the obtained target sequence, we analyze the numerical correlation between data points at different positions in the target sequence, sort out the similarity and synchronicity of changes between adjacent data points and data points at different distances, and integrate these data correlation information to extract the content that can comprehensively reflect the interaction and mutual influence of data within the target sequence, which is the intrinsic correlation representation of the target sequence.

[0115] When performing energy analysis on the core parameters representing intrinsic correlation, the core parameters that best determine the strength and characteristics of sequence correlation are selected from the intrinsic correlation representations. The signal energies corresponding to these core parameters are quantified and statistically analyzed. The statistical results are compared with the standard energy values ​​under theoretically error-free conditions, and the energy difference between the two is calculated. This energy difference is the residual energy measure of the core parameters.

[0116] When performing parameterized estimation of the modulus sequence based on core parameters and residual energy measure, a model that can continuously describe the frequency distribution characteristics of the modulus sequence is constructed by combining the inherent correlation law of the target sequence reflected by the core parameters and the energy deviation reflected by the residual energy measure. This model can accurately capture the energy distribution state in different frequency ranges, fully characterize the continuous correspondence between frequency and energy, and thus obtain the continuous spectral density of the modulus sequence.

[0117] When uniformly discretely sampling the continuous spectral density within a preset frequency range, based on the actual application scenario and requirements of motor eccentricity fault diagnosis, a reasonable frequency range containing the fault characteristic frequency is determined as the preset frequency range. Within this frequency range, several sampling points are selected at equal frequency intervals, and the continuous spectral density value corresponding to each sampling point is read sequentially. These values ​​are arranged according to the corresponding sampling frequency order to finally obtain the discrete spectrum of the modulus sequence.

[0118] Boundary effect suppression is performed on the magnitude sequence in the complex envelope signal to obtain the target sequence. Statistical feature extraction is performed on the target sequence to obtain the intrinsic correlation characterization of the target sequence. The key data obtained from this intrinsic correlation characterization are the core parameters.

[0119] Energy analysis is performed on the core parameters that characterize intrinsic correlation. The measurement result obtained by analyzing the energy distribution and residual energy of the core parameters is the residual energy measurement.

[0120] The basic time measurement unit of a modulus sequence is the standard inherent in the modulus sequence itself in a complex envelope signal, used to measure the time interval of the sequence.

[0121] The preset spectral smoothing adjustment factor is a fixed value that is pre-set according to actual diagnostic needs and signal characteristics before performing spectrum estimation to adjust the degree of spectral smoothing.

[0122] No. The complex conjugation of the first core parameter is achieved by applying the first... The core parameters are obtained by performing complex conjugation operations. The specific process of complex conjugation operations is to keep the real part of the core parameter unchanged and change the imaginary part to its opposite.

[0123] Natural constants are fixed constants in the field of mathematics, and their values ​​are fixed and unchanging.

[0124] The imaginary unit is a basic unit defined in mathematics to represent imaginary numbers, and its value and definition are fixed.

[0125] The core of this operation process is to combine the intrinsic correlation of the target sequence to characterize the corresponding core parameters, the residual energy metric obtained by energy analysis of the core parameters, the inherent basic time metric unit of the modulus sequence, the pre-set spectral smoothing adjustment factor, and the... The results obtained by performing complex conjugate operations on the core parameters, along with the fixed natural constants and imaginary units in mathematics, are used to achieve parameterized estimation of the modulus sequence through a series of specific operational steps.

[0126] The specific process of parameter estimation is to use the above data and the operation relationship between the numerator and denominator to accurately calculate the signal spectral density at a specific circular frequency. The final result is the continuous spectral density, which is a key prerequisite for obtaining the discrete spectrum of the modulus sequence by uniformly and discretely sampling it within a preset frequency range.

[0127] As the value of the residual energy metric increases, the value of the numerator will increase accordingly. With the value of the denominator remaining unchanged, the value of the final continuous spectral density will also increase.

[0128] When the values ​​of the core parameters change, they affect the two results in the denominator through summation. If the increase in the value of the core parameters leads to an increase in the overall value of the denominator, while the residual energy metric in the numerator remains unchanged, then the final value of the continuous spectral density will decrease. Conversely, if the increase in the value of the core parameters leads to a decrease in the overall value of the denominator, while the residual energy metric in the numerator remains unchanged, then the final value of the continuous spectral density will increase.

[0129] When the value of the preset spectral smoothing adjustment factor increases, it will directly increase the value of the second term in the denominator. If the residual energy metric in the numerator remains unchanged, the overall value of the denominator will increase accordingly, and the value of the final continuous spectral density will decrease.

[0130] When the When the value of the complex conjugate of the core parameter changes, it affects the result of the second term in the denominator through summation. If the value of the complex conjugate increases, the value of the second term increases, the overall value of the denominator increases accordingly, while the residual energy metric of the numerator remains unchanged, and the final value of the continuous spectral density decreases; conversely, if the value of the complex conjugate increases, the value of the second term decreases, the overall value of the denominator decreases accordingly, while the residual energy metric of the numerator remains unchanged, and the final value of the continuous spectral density increases.

[0131] When the value of the basic time unit of the modulus sequence changes, it affects the summation of the two terms in the denominator through exponential operations, thereby changing the overall value of the denominator. If the value of the basic time unit increases, causing the overall value of the denominator to increase, while the residual energy metric in the numerator remains unchanged, then the final value of the continuous spectral density will decrease; conversely, if the value of the basic time unit increases, causing the overall value of the denominator to decrease, while the residual energy metric in the numerator remains unchanged, then the final value of the continuous spectral density will increase.

[0132] The beneficial effects are as follows: the above implementation process effectively avoids the impact of signal edge distortion on subsequent analysis through targeted boundary effect suppression, ensuring the integrity and stability of the target sequence; by deeply extracting the intrinsic correlation characterization of the target sequence and analyzing the residual energy measurement of the core parameters, the intrinsic characteristics of the sequence are fully captured, solving the problem of incomplete characterization of the intrinsic correlation of signals by traditional methods; based on the parameterized estimation of the core parameters and residual energy measurement, and subsequent uniform discrete sampling, the discrete spectrum is accurately obtained, enabling the fault feature order to be effectively distinguished from diffuse background noise, providing high-quality basic data for subsequent spectrum equalization processing and fault feature extraction, and improving the accuracy of motor eccentricity fault diagnosis.

[0133] S4. Equalize the order spectral line amplitudes in the discrete spectrum to obtain the whitened spectrum of the target motor, and reconstruct the whitened spectrum into the angular domain amplitude signal of the target motor by inverse transformation;

[0134] In this embodiment of the invention, the step of equalizing the amplitudes of the order spectral lines in the discrete spectrum to obtain the whitened spectrum of the target motor, and then reconstructing the whitened spectrum into the angular domain amplitude signal of the target motor by inverse transformation, includes:

[0135] Spectral structure analysis is performed on the spectral line energy distribution in the discrete spectrum to obtain the dominant characteristic order region and diffuse background noise region of the discrete spectrum;

[0136] Based on the dominant characteristic order region and the diffuse background noise region, a spectral line amplitude adjustment strategy for the discrete spectrum is formulated.

[0137] Based on the spectral line amplitude adjustment strategy, a decision mapping is performed on the order spectral line amplitudes of the discrete spectrum to obtain the whitening spectrum of the target motor.

[0138] The whitened spectrum is reconstructed from the frequency domain to the angular domain, and the spectral symmetry condition required for the signal reconstruction is satisfied to obtain the preliminary angular domain sequence of the whitened spectrum;

[0139] Phase trajectory calibration is performed on the preliminary angular domain sequence to obtain the angular domain amplitude signal of the target motor.

[0140] The step of reconstructing the whitened spectrum from the frequency domain to the angular domain, and satisfying the spectral symmetry condition required for the signal reconstruction, to obtain a preliminary angular domain sequence of the whitened spectrum, includes:

[0141] When the spectral amplitude distribution of the whitening spectrum does not satisfy the conjugate symmetry relationship, the spectral lines in the positive order region of the whitening spectrum are mirrored and copied to obtain the symmetrical spectrum of the whitening spectrum.

[0142] Perform an inverse transformation operation from the frequency domain to the angular domain on the symmetric spectrum and the whitened spectrum that satisfies the conjugate symmetry relationship to obtain the inverse transformation intermediate sequence of the whitened spectrum;

[0143] The imaginary part energy level of the inverse transform intermediate sequence is obtained by performing parameterized spectral estimation on the inverse transform intermediate sequence;

[0144] Based on the imaginary part energy level, the inverse transform intermediate sequence is fine-tuned with symmetry compensation to obtain the optimized intermediate sequence of the whitened spectrum;

[0145] The real part in the optimized intermediate sequence is used as the initial angular domain sequence of the whitening spectrum.

[0146] The energy values ​​corresponding to each order spectral line in the discrete spectrum are analyzed one by one. The energy concentration and distribution pattern of each order spectral line are statistically analyzed. The spectral line regions with energy values ​​significantly higher than other regions and exhibiting characteristics related to motor eccentricity faults are identified as the dominant characteristic order regions of the discrete spectrum. The spectral line regions with low energy values, scattered distribution and no obvious characteristic patterns are identified as the diffuse background noise regions of the discrete spectrum.

[0147] Based on the distribution range and energy differences between the dominant feature order region and the diffuse background noise region, the adjustment direction is clarified: for the dominant feature order region, the amplitude of the spectral lines in the region is increased by moderate amplification to enhance the identifiability of the fault features; for the diffuse background noise region, the amplitude of the spectral lines in the region is reduced by gradient attenuation to weaken the masking of fault features by noise interference, thus forming a complete discrete spectrum line amplitude adjustment strategy.

[0148] According to the established spectral amplitude adjustment strategy, the amplitude of each order of spectral lines in the discrete spectrum is precisely mapped and adjusted one by one to ensure that the amplitude of spectral lines in the dominant characteristic order region is effectively enhanced and the amplitude of spectral lines in the diffuse background noise region is reasonably suppressed. After adjustment, a whitened spectrum of the target motor with more balanced energy distribution of spectral lines of each order and clearer fault characteristics and noise boundaries is formed.

[0149] First, determine whether the amplitude distribution of the whitening spectrum satisfies the conjugate symmetry relationship. The criterion is whether the amplitude of any spectral line in the positive order region is equal to the amplitude of the corresponding negative order region spectral line and whether their phases are opposite. If the conjugate symmetry relationship is not satisfied, take each spectral line in the positive order region of the whitening spectrum as a reference, and copy the amplitude and phase characteristics of the positive order spectral line completely to the corresponding negative order position according to the symmetry principle to generate a symmetrical spectrum of the whitening spectrum whose amplitude distribution satisfies the conjugate symmetry relationship.

[0150] By employing inverse Fourier transform, the generated symmetrical spectrum and the whitened spectrum that inherently satisfy conjugate symmetry are transformed from the frequency domain to the time domain, converting the spectral signal in the frequency domain into a continuous sequence signal in the time domain. This sequence signal is the intermediate sequence of the inverse transform of the whitened spectrum.

[0151] A comprehensive analysis of the frequency components and energy composition of the inverse transform intermediate sequence is conducted. By sorting out the energy proportions corresponding to different frequency components in the sequence, the total energy carried by the imaginary part of the sequence is calculated. This total energy is the imaginary part energy level of the inverse transform intermediate sequence.

[0152] The symmetry of the inverse transform intermediate sequence is judged based on the obtained imaginary part energy level. If the imaginary part energy level is high, it indicates that the sequence symmetry is insufficient. Based on the distribution position and value of the imaginary part energy in the sequence, the corresponding numerical points in the sequence are slightly modified to gradually reduce the imaginary part energy level, improve the conjugate symmetry characteristics of the sequence, and finally obtain the optimized intermediate sequence of the whitening spectrum.

[0153] The real part values ​​of all data points in the optimized intermediate sequence are directly extracted and arranged in chronological order to form a preliminary angular domain sequence of the whitened spectrum that can reflect the correspondence between the motor rotation angle and the signal amplitude.

[0154] The phase change trajectory of the initial angular domain sequence is tracked and analyzed throughout to identify abnormal parts such as phase abrupt changes and deviations from the normal trend. For these abnormal parts, the phase values ​​of the corresponding data points are adjusted by linear correction to make the entire phase trajectory smooth, continuous and in line with the mechanical rotation law of the motor. After correction, the angular domain amplitude signal of the target motor is obtained.

[0155] The beneficial effects are as follows: the above implementation process effectively enhances fault characteristics and suppresses background noise by performing targeted spectral structure analysis and spectral line amplitude equalization on the discrete spectrum, solving the problem of traditional methods lacking targeted equalization processing of the discrete spectrum; during the signal reconstruction process, through steps such as conjugate symmetry verification, mirror copying, and symmetry compensation fine-tuning, the spectral symmetry condition is strictly met, which greatly improves the accuracy and reliability of the angular domain signal, avoiding the problem of insufficient fault feature extraction accuracy caused by non-standard angular domain signal reconstruction in traditional methods, and providing high-quality angular domain amplitude signals for subsequent cyclic stationary analysis and third-order cyclic cumulant extraction, thereby improving the accuracy of motor eccentricity fault diagnosis.

[0156] S5. Perform cyclic stationarity analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative amount of the angular domain amplitude signal at a specific cyclic frequency in the target motor;

[0157] In this embodiment of the invention, the step of performing cyclostationary analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative quantity of the angular domain amplitude signal at a specific cyclic frequency in the target motor includes:

[0158] Based on the rotation period of the target motor, the angular domain amplitude signal is segmented into angular domain analysis sub-segments with overlapping regions;

[0159] Based on the cyclic frequency of the corner domain analysis segment, the cyclic cumulative amount in the corner domain analysis segment is estimated point by point to obtain the initial cumulative amount spectrum corresponding to the cyclic frequency;

[0160] By filtering out noise from the initial cumulative spectrum, a refined cumulative spectrum of the angular domain analysis segment is obtained;

[0161] By performing feature mapping on the number of pole pairs and rotational frequency of the target motor, the fault characteristic cycle frequency of the target motor is obtained;

[0162] Based on the fault characteristic cycle frequency, the spectral lines of the refined cumulative quantity spectrum are read to obtain the third-order cyclic cumulative quantity of the target motor.

[0163] First, the rotation period of the target motor is calculated by the rotor speed. The length of each corner domain analysis segment is set to twice the rotation period. At the same time, the overlapping area of ​​adjacent segments is set to 50% of the segment length. Starting from the starting position of the corner domain amplitude signal, the signal is segmented segment by segment according to the set segment length and overlap ratio, and finally multiple corner domain analysis segments with overlapping areas are obtained.

[0164] Define the cyclic frequency range corresponding to the angular domain analysis segment. This range covers the characteristic frequency interval that may be caused by motor eccentricity fault. Within this frequency range, select each cyclic frequency point at fixed intervals. For each cyclic frequency point, perform mean correlation calculation on the signal data of the corresponding angular domain analysis segment using the product of three signal samples. Obtain the cyclic cumulative value corresponding to each cyclic frequency point by point. Arrange these values ​​in order of cyclic frequency to form the initial cumulative spectrum corresponding to the cyclic frequency.

[0165] The initial cumulative spectrum is filtered for noise using a moving average filter. The sliding window size is set to 5 adjacent data points. Taking each data point as the center, the average value of the two adjacent data points before and after it and the data point itself is calculated. The original data point value is replaced with this average value. All data points in the initial cumulative spectrum are processed sequentially to smooth spectral fluctuations and remove high-frequency noise interference, thus obtaining the refined cumulative spectrum of the angular domain analysis segment.

[0166] The inherent parameter of the target motor, the number of pole pairs, is obtained. The rotational frequency is calculated from the rotor speed signal. According to the occurrence mechanism of motor eccentricity fault, there is a fixed correlation between the characteristic cycle frequency of eccentricity fault and the number of pole pairs and the rotational frequency. The number of pole pairs and the rotational frequency are substituted into this correlation for mapping calculation to determine the specific cycle frequency value that can characterize the eccentricity fault, which is the fault characteristic cycle frequency of the target motor.

[0167] In the obtained refined cumulative quantity spectrum, based on the specific value of the fault characteristic cycle frequency, the corresponding position in the spectrum is located, and the cumulative quantity value at that position is accurately read. This value is the third-order cyclic cumulative quantity of the angular domain amplitude signal at a specific cycle frequency in the target motor.

[0168] The beneficial effects are as follows: the above implementation process ensures the integrity and continuity of signal analysis by reasonably segmenting the angular domain amplitude signal and setting overlapping regions; by estimating the cyclic cumulant point by point and combining it with targeted noise filtering, the purity and clarity of the cumulant spectrum are effectively improved; and by using the characteristic mapping between the pole pairs and the rotation frequency, the fault characteristic cyclic frequency is accurately locked, solving the problem that traditional methods are difficult to accurately locate the fault characteristic frequency. The third-order cyclic cumulant obtained in the end has extremely high accuracy and reliability, and can accurately capture the fault characteristics at a specific cyclic frequency, providing solid and accurate data support for the subsequent determination of the eccentric fault type and severity level.

[0169] In this embodiment of the invention, S6, the type and severity level of the eccentricity fault in the target motor are determined based on the third-order cyclic cumulative amount.

[0170] Data on the third-order cyclic cumulative quantities of different types of eccentric faults under the same operating conditions are collected in advance. Features are extracted from these data to clarify the unique performance of the third-order cyclic cumulative quantity of each fault type at a specific cyclic frequency. These feature data are classified and organized to construct a standard feature library of the third-order cyclic cumulative quantity of the target motor eccentric fault type.

[0171] The third-order cyclic cumulative quantity obtained by the current target motor at a specific cyclic frequency is compared with the third-order cyclic cumulative quantity characteristics corresponding to various eccentric faults in the standard feature library. The degree of fit between the two in terms of numerical characteristics and variation patterns is analyzed, and the fault type with the highest degree of fit is selected as the eccentric fault type of the current target motor.

[0172] Based on the safety standards for motor operation and the degree of impact of faults, three severity levels of faults are divided into mild, moderate and severe. Through statistical analysis of a large amount of experimental data, the threshold range of the third-order cyclic cumulative quantity corresponding to each level is determined to ensure that the boundaries of each range are clear, non-overlapping and cover all possible fault severity situations.

[0173] The specific value of the third-order cycle cumulative quantity of the current target motor is read, and compared with the value threshold range corresponding to the three preset severity levels, the specific range to which the value belongs is determined, thereby determining the severity level of the eccentricity fault in the current target motor.

[0174] The beneficial effects are that the above implementation process, by constructing a standardized three-order cyclic cumulative feature library and a clear severity threshold range, provides a clear and unified basis for fault judgment, solving the problems of ambiguous fault type identification and lack of quantitative standards for severity classification in traditional methods. The judgment process is logically rigorous and reproducible, and can accurately output the eccentricity fault type and severity level of the target motor, providing reliable support for staff to formulate targeted maintenance plans, effectively reducing the risk of motor operation caused by misjudgment or omission of faults, and ensuring the long-term stable operation of the motor.

[0175] like Figure 2 The diagram shown is a functional block diagram of a motor eccentricity fault diagnosis system based on current harmonic analysis provided in an embodiment of the present invention.

[0176] The motor eccentricity fault diagnosis system 100 based on current harmonic analysis described in this invention can be installed in electronic devices. Depending on the functions implemented, the motor eccentricity fault diagnosis system 100 may include a signal processing module 101, a signal interpolation processing module 102, a spectrum estimation module 103, a spectrum transformation module 104, an amplitude signal analysis module 105, and a fault severity analysis module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0177] In this embodiment, the functions of each module / unit are as follows:

[0178] The signal processing module 101 is used to extract the power frequency component of the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor, and to perform complex analytic envelope transformation on the residual current signal to obtain the envelope analytic signal of the target motor.

[0179] The signal interpolation processing module 102 is used to synchronously interpolate and resample the real and imaginary components in the envelope analysis signal based on the rotor speed signal of the target motor, so as to obtain the complex envelope signal of the target motor.

[0180] The spectrum estimation module 103 is used to perform spectrum estimation on the modulus sequence in the complex envelope signal to obtain the discrete spectrum of the modulus sequence;

[0181] The spectrum transformation module 104 is used to perform equalization processing on the order spectral line amplitudes in the discrete spectrum to obtain the whitened spectrum of the target motor, and to perform inverse transformation on the whitened spectrum to reconstruct the angular domain amplitude signal of the target motor.

[0182] The amplitude signal analysis module 105 is used to perform cyclic stationarity analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative amount of the angular domain amplitude signal at a specific cyclic frequency in the target motor.

[0183] The fault severity analysis module 106 is used to determine the type and severity level of the eccentricity fault in the target motor based on the third-order cyclic cumulative amount.

[0184] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0185] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0187] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0188] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for diagnosing motor eccentricity faults based on current harmonic analysis, characterized in that, The method includes: S1. Extract the power frequency component from the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor, and perform complex analytic envelope transformation on the residual current signal to obtain the envelope analytic signal of the target motor. S2. Based on the rotor speed signal of the target motor, the real and imaginary components in the envelope analysis signal are synchronously interpolated and resampled to obtain the complex envelope signal of the target motor. S3. Perform spectral estimation on the modulus sequence in the complex envelope signal to obtain the discrete spectrum of the modulus sequence, including: Boundary effect suppression is applied to the magnitude sequence in the complex envelope signal to obtain the target sequence of the complex envelope signal; Statistical features are extracted from the target sequence to obtain an intrinsic correlation characterization of the target sequence; Energy analysis is performed on the core parameters representing the intrinsic correlation to obtain the residual energy measure of the core parameters; Based on the core parameters and the residual energy metric, the modulus sequence is parametrically estimated to obtain the continuous spectral density of the modulus sequence, wherein the formula for calculating the continuous spectral density is: ； in, Indicates at angular frequency The continuous spectral density at that location, This refers to the core parameters. Represents the natural constant. Represents the imaginary unit. The basic time metric unit representing the modulus sequence. This represents the preset spectral smoothing adjustment factor. Indicates the first The complex conjugate of the aforementioned core parameters, This represents the residual energy metric; Within a preset frequency range, the continuous spectral density is uniformly and discretely sampled to obtain the discrete spectrum of the modulus sequence; S4. Equalize the order spectral line amplitudes in the discrete spectrum to obtain the whitened spectrum of the target motor, and reconstruct the whitened spectrum into the angular domain amplitude signal of the target motor by inverse transformation, including: Spectral structure analysis is performed on the spectral line energy distribution in the discrete spectrum to obtain the dominant characteristic order region and diffuse background noise region of the discrete spectrum; Based on the dominant characteristic order region and the diffuse background noise region, a spectral line amplitude adjustment strategy for the discrete spectrum is formulated. Based on the spectral line amplitude adjustment strategy, a decision mapping is performed on the order spectral line amplitudes of the discrete spectrum to obtain the whitening spectrum of the target motor. The whitened spectrum is reconstructed from the frequency domain to the angular domain, satisfying the spectral symmetry conditions required for the signal reconstruction, to obtain a preliminary angular domain sequence of the whitened spectrum, including: When the spectral amplitude distribution of the whitening spectrum does not satisfy the conjugate symmetry relationship, the spectral lines in the positive order region of the whitening spectrum are mirrored and copied to obtain the symmetrical spectrum of the whitening spectrum. Perform an inverse transformation operation from the frequency domain to the angular domain on the symmetric spectrum and the whitened spectrum that satisfies the conjugate symmetry relationship to obtain the inverse transformation intermediate sequence of the whitened spectrum; The imaginary part energy level of the inverse transform intermediate sequence is obtained by performing parameterized spectral estimation on the inverse transform intermediate sequence; Based on the imaginary part energy level, the inverse transform intermediate sequence is fine-tuned with symmetry compensation to obtain the optimized intermediate sequence of the whitened spectrum; The real part in the optimized intermediate sequence is used as the initial angular domain sequence of the whitening spectrum; Phase trajectory calibration is performed on the preliminary angular domain sequence to obtain the angular domain amplitude signal of the target motor; S5. Perform cyclic stationarity analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative amount of the angular domain amplitude signal at a specific cyclic frequency in the target motor; S6. Based on the cumulative amount of the third-order cycle, determine the type and severity level of the eccentricity fault in the target motor.

2. The method for diagnosing motor eccentricity faults based on current harmonic analysis as described in claim 1, characterized in that, The step of extracting the power frequency component from the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor includes: Acquire the three-phase stator current signal of the target motor; Based on the frequency, amplitude, and phase of the three-phase stator current signal, construct the power frequency quadrature reference signal of the target motor; Based on the power frequency quadrature reference signal, the power frequency dominant component in the three-phase stator current signal is removed to obtain the preliminary residual current signal of the target motor. The initial residual current signal is subjected to wavelet threshold denoising to obtain the residual current signal of the target motor.

3. The method for diagnosing motor eccentricity faults based on current harmonic analysis as described in claim 1, characterized in that, The step of performing a complex analytic envelope transform on the residual current signal to obtain the envelope analytic signal of the target motor includes: The residual current signal is subjected to Hilbert transform to obtain the orthogonal Hilbert components of the target motor; Using the residual current signal as the real part and the orthogonal Hilbert component as the imaginary part, construct the analytical signal of the target motor; The analytical signal is subjected to phase dewinding processing to obtain the instantaneous phase information of the target motor; The analytical signal is decomposed into modes to obtain the instantaneous amplitude envelope information of the analytical signal; Based on the instantaneous amplitude envelope information, the instantaneous phase information is coupled and analyzed to obtain the envelope analytical signal of the target motor.

4. The method for diagnosing motor eccentricity faults based on current harmonic analysis as described in claim 1, characterized in that, The step of synchronously interpolating and resampling the real and imaginary components of the envelope analytic signal based on the rotor speed signal of the target motor to obtain the complex envelope signal of the target motor includes: The rotor speed signal of the target motor is digitally integrated to obtain the rotor phase information of the target motor; Based on the rotor phase information, a pulse sequence with an equal angle to the mechanical rotation of the target motor is generated; Based on the equal-angle incremental pulse sequence, cubic spline interpolation is performed on the real and imaginary components of the envelope analytic signal to obtain the real and imaginary components of the envelope analytic signal in the angular domain. The real part signal and the imaginary part signal of the angular domain are synchronously combined to obtain the complex envelope signal of the target motor.

5. The method for diagnosing motor eccentricity faults based on current harmonic analysis as described in claim 1, characterized in that, The step of performing cyclostationary analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative quantity of the angular domain amplitude signal at a specific cyclic frequency in the target motor includes: Based on the rotation period of the target motor, the angular domain amplitude signal is segmented into angular domain analysis sub-segments with overlapping regions; Based on the cyclic frequency of the corner domain analysis segment, the cyclic cumulative amount in the corner domain analysis segment is estimated point by point to obtain the initial cumulative amount spectrum corresponding to the cyclic frequency; By filtering out noise from the initial cumulative spectrum, a refined cumulative spectrum of the angular domain analysis segment is obtained; By performing feature mapping on the number of pole pairs and rotational frequency of the target motor, the fault characteristic cycle frequency of the target motor is obtained; Based on the fault characteristic cycle frequency, the spectral lines of the refined cumulative quantity spectrum are read to obtain the third-order cyclic cumulative quantity of the target motor.

6. A motor eccentricity fault diagnosis system based on current harmonic analysis, characterized in that, The system for implementing the motor eccentricity fault diagnosis method based on current harmonic analysis as described in claim 1 includes: The signal processing module is used to extract the power frequency component of the three-phase stator current signal of the target motor to obtain the residual current signal of the target motor, and to perform complex analytic envelope transformation on the residual current signal to obtain the envelope analytic signal of the target motor. The signal interpolation processing module is used to synchronously interpolate and resample the real and imaginary components in the envelope analysis signal based on the rotor speed signal of the target motor, so as to obtain the complex envelope signal of the target motor. The spectrum estimation module is used to perform spectrum estimation on the magnitude sequence in the complex envelope signal to obtain the discrete spectrum of the magnitude sequence, including: Boundary effect suppression is applied to the magnitude sequence in the complex envelope signal to obtain the target sequence of the complex envelope signal; Statistical features are extracted from the target sequence to obtain an intrinsic correlation characterization of the target sequence; Energy analysis is performed on the core parameters representing the intrinsic correlation to obtain the residual energy measure of the core parameters; Based on the core parameters and the residual energy metric, the modulus sequence is parametrically estimated to obtain the continuous spectral density of the modulus sequence, wherein the formula for calculating the continuous spectral density is: ； in, Indicates at angular frequency The continuous spectral density at that location, This refers to the core parameters. Represents the natural constant. Represents the imaginary unit. The basic time metric unit representing the modulus sequence. This represents the preset spectral smoothing adjustment factor. Indicates the first The complex conjugate of the aforementioned core parameters, This represents the residual energy metric; Within a preset frequency range, the continuous spectral density is uniformly and discretely sampled to obtain the discrete spectrum of the modulus sequence; The spectrum transformation module is used to perform equalization processing on the order spectral line amplitudes in the discrete spectrum to obtain the whitened spectrum of the target motor, and to reconstruct the whitened spectrum into the angular domain amplitude signal of the target motor by inverse transformation, including: Spectral structure analysis is performed on the spectral line energy distribution in the discrete spectrum to obtain the dominant characteristic order region and diffuse background noise region of the discrete spectrum; Based on the dominant characteristic order region and the diffuse background noise region, a spectral line amplitude adjustment strategy for the discrete spectrum is formulated. Based on the spectral line amplitude adjustment strategy, a decision mapping is performed on the order spectral line amplitudes of the discrete spectrum to obtain the whitening spectrum of the target motor. The whitened spectrum is reconstructed from the frequency domain to the angular domain, satisfying the spectral symmetry conditions required for the signal reconstruction, to obtain a preliminary angular domain sequence of the whitened spectrum, including: When the spectral amplitude distribution of the whitening spectrum does not satisfy the conjugate symmetry relationship, the spectral lines in the positive order region of the whitening spectrum are mirrored and copied to obtain the symmetrical spectrum of the whitening spectrum. Perform an inverse transformation operation from the frequency domain to the angular domain on the symmetric spectrum and the whitened spectrum that satisfies the conjugate symmetry relationship to obtain the inverse transformation intermediate sequence of the whitened spectrum; The imaginary part energy level of the inverse transform intermediate sequence is obtained by performing parameterized spectral estimation on the inverse transform intermediate sequence; Based on the imaginary part energy level, the inverse transform intermediate sequence is fine-tuned with symmetry compensation to obtain the optimized intermediate sequence of the whitened spectrum; The real part in the optimized intermediate sequence is used as the initial angular domain sequence of the whitening spectrum; Phase trajectory calibration is performed on the preliminary angular domain sequence to obtain the angular domain amplitude signal of the target motor; The amplitude signal analysis module is used to perform cyclic stationarity analysis on the angular domain amplitude signal to obtain the third-order cyclic cumulative amount of the angular domain amplitude signal at a specific cyclic frequency in the target motor. The fault severity analysis module is used to determine the type and severity level of the eccentric fault in the target motor based on the third-order cyclic cumulative amount.

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