A transformer winding structure looseness diagnosis method based on vibration side frequency characteristics

Abstract

The application discloses a winding structure looseness diagnosis method based on side frequency vibration characteristics. The application firstly calculates the cycle autocorrelation function of the vibration signal and carries out Fourier transform to obtain a spectrum correlation function; the side frequency characteristics in the vibration signal are identified by analyzing the spectrum correlation mean value under different cycle frequencies; then, based on the identified side frequency characteristics, the energy value of the first-order side frequency component within 300 Hz is counted, the sum of all the first-order side frequency energy is calculated, and the side frequency energy total index is obtained; finally, the extracted side frequency characteristic frequency, the spectrum correlation mean value and the side frequency energy total index are compared and analyzed with the reference side frequency characteristic parameters under the normal state of the transformer winding, and the winding structure state is comprehensively evaluated. The method of the application adopts the spectrum correlation analysis method, utilizes the cycle stationary characteristics of the side frequency component, can effectively enhance the weak side frequency signal submerged by noise, and improves the detection reliability in the complex field environment.

Description

Technical Field

[0001] This invention belongs to the field of power transformer diagnostic technology, specifically relating to a method for diagnosing loose winding structures based on side-frequency vibration characteristics. Background Technology

[0002] Power transformers are core equipment in the power grid, and the windings, as key components of the transformer, directly affect equipment safety and grid stability. Statistics show that approximately 43% of transformer faults are related to the windings. Even slight deformation of the windings can significantly reduce their short-circuit impact withstand capability; therefore, accurate and real-time diagnosis of the winding operating status is crucial.

[0003] Existing methods for detecting the condition of transformer windings have many shortcomings: traditional methods such as frequency response analysis and short-circuit impedance measurement require power outages, which are difficult to meet the needs of real-time power grid monitoring; new detection methods such as online impedance methods and fiber optic sensing methods are still in the experimental verification stage and have not yet been applied in engineering; although oil chromatography analysis technology can effectively identify faults such as partial discharge and overheating, its sensitivity to winding mechanical deformation is extremely low, and it cannot detect mechanical defects such as winding loosening and structural deformation.

[0004] Vibration-based transformer condition monitoring technology has become a research hotspot for online diagnostics of power equipment due to its advantages of not requiring power outages and strong real-time performance. Furthermore, vibration sidefrequency characteristics are less affected by interference from load current and excitation components, feature extraction is stable, and criteria are clear, facilitating quantitative analysis and intelligent diagnosis. This significantly improves the accuracy, real-time performance, and safety of transformer winding mechanical condition monitoring, providing strong technical support for the safe and stable operation of power transformers.

[0005] However, existing vibration analyses mostly rely on overall vibration amplitude, time-domain statistical indicators, or basic frequency-domain characteristics. They have low sensitivity to early mechanical defects such as local winding deformation and slight loosening. Fault characteristics are easily masked by factors such as core vibration, load disturbance, and housing propagation attenuation, making it difficult to effectively highlight weak defect signals. At the same time, traditional vibration characteristics are greatly affected by operating conditions, exhibiting poor stability under different loads and voltage excitations, and lacking robust diagnostic indicators. Summary of the Invention

[0006] To address the challenges in evaluating the condition of operating transformer windings, this invention proposes a method for diagnosing transformer winding structural loosening based on vibration sidefrequency characteristics, thus overcoming the shortcomings of conventional winding detection methods.

[0007] This invention includes the following steps:

[0008] The cyclic autocorrelation function of the vibration signal is calculated and Fourier transform is performed to obtain the spectral correlation function;

[0009] By analyzing the mean spectral correlation at different cycle frequencies, the sideband characteristics in the vibration signal are identified.

[0010] Based on the identified sideband characteristics, the energy values ​​of the first-order sideband components within 300Hz are statistically analyzed, and the sum of the energies of all first-order sidebands is calculated to obtain the total sideband energy index.

[0011] The extracted sideband characteristic frequencies, the mean value of the spectrum correlation, and the total index of the sideband energy are compared and analyzed with the reference sideband characteristic parameters of the transformer winding under normal conditions to comprehensively evaluate the winding structure status.

[0012] Preferably, the vibration signal is collected by vibration sensors placed at the top, middle and bottom of the transformer winding to measure the axial vibration of the winding.

[0013] Preferably, when calculating the spectral correlation function, a short-time Fourier transform is used to perform time-frequency analysis on the vibration signal. The time window length is set so that the frequency resolution meets the requirements for sideband interval identification, and the transform result is normalized to suppress noise interference.

[0014] Preferably, identifying the sideband characteristics includes: finding the extreme points of the spectral correlation mean function within a specified frequency range, and determining the sideband characteristic frequency based on the significant peak value that appears when the cyclic frequency matches the winding sideband modulation interval.

[0015] Preferably, the first-order sideband component is a pair of sidebands with a center frequency of twice the power frequency and a spacing of the winding's natural frequency, including vibration components at twice the power frequency plus the natural frequency and twice the power frequency minus the natural frequency.

[0016] Preferably, the reference sideband characteristic parameters are obtained by means of the following method: under the condition that the transformer winding clamping force is normal and the structure is intact, the vibration signal under the short-circuit load test is collected, and the corresponding sideband characteristic frequency, spectral correlation mean and sideband energy index are extracted as reference values.

[0017] Preferably, evaluating the winding structure status includes: comparing the extracted sideband characteristic frequency with the reference sideband characteristic frequency; if the sideband characteristic frequency is significantly lower than the reference value, it is determined that the winding has a clamping force decay or local loosening defect.

[0018] Preferably, the evaluation of the winding structure status includes: analyzing the distribution of the spectrum correlation mean on the cyclic frequency axis; if a significant peak appears at a cyclic frequency that is not an integer multiple of the power frequency, it is determined that the winding has side-frequency vibration caused by parametric excitation; the higher the peak amplitude, the more severe the abnormality of the winding structure.

[0019] Preferably, evaluating the winding structure status includes: comparing the calculated total sideband energy index with a reference threshold under normal conditions; if the total sideband energy index exceeds the reference threshold and rises abnormally, it is determined that there is an abnormality in the winding structure.

[0020] Preferably, the method further includes collecting vibration signals under different load current conditions, analyzing the trend of the total sideband energy index with the load current, and showing that the sideband vibration amplitude increases accordingly when the load current increases, thereby distinguishing between normal electromagnetic force response and abnormal sideband vibration caused by structural loosening.

[0021] The beneficial effects of this invention are as follows:

[0022] 1. The method of this invention adopts spectral correlation analysis and utilizes the cyclic stationary characteristics of sideband components to effectively enhance weak sideband signals that are submerged by noise, thereby improving the detection reliability in complex field environments.

[0023] 2. The method of this invention constructs a complete winding condition evaluation system, which can effectively identify early structural loosening and reduce the risk of misjudgment and missed judgment.

[0024] 3. The method of the present invention can locate and diagnose local structural deformation or loosening by arranging multiple measuring points at different positions of the winding and comparing the sideband energy distribution of each measuring point. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the steps of the present invention;

[0026] Figure 2 Winding vibration testing platform;

[0027] Figure 3 Mean SC value under different clamping forces;

[0028] Figure 4 Mean SC values ​​under different loads;

[0029] Figure 5 Comparison of SC cumulative value distribution under different clamping forces;

[0030] Figure 6 The characteristic frequency of the sideband varies with the clamping force;

[0031] Figure 7 Vibration measurement diagram of the transformer at the site;

[0032] Figure 8 Steady-state vibration at different measuring points;

[0033] Figure 9 Mean SC values ​​at different measuring points;

[0034] Figure 10The relationship between the cumulative SC value at measuring point 1 and the load;

[0035] Figure 11 Phase A winding exhibits both deformation and loosening. Detailed Implementation

[0036] To describe the present invention in more detail, the method for diagnosing loose transformer winding structure according to the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] The specific steps of this invention are as follows:

[0038] (1) Vibration sensors and hydraulic jacks are installed on the windings of the power transformer.

[0039] Three vibration sensors were installed on the transformer windings, and vibration measurements were taken at three locations: top, middle, and bottom of the winding coil. An adjustable hydraulic jack was installed at the top of the windings.

[0040] (2) Record the vibration signal of the power transformer under short-circuit load test.

[0041] In the transformer short-circuit load test, the low-voltage side terminals are short-circuited, and a three-phase voltage is applied to the high-voltage side. After the clamping force is adjusted to the set value, vibration signals are collected.

[0042] (3) Construct a sideband feature extraction model based on spectral correlation

[0043] The cyclic autocorrelation function of the vibration signal is calculated and Fourier transform is performed to obtain the spectral correlation function. By analyzing the mean spectral correlation at different cyclic frequencies, the sideband characteristics in the vibration signal are identified.

[0044] (4) Extracting sideband eigenvalues ​​from vibration signals

[0045] Based on the identified winding vibration sideband characteristics, the energy values ​​of the first-order sideband components within 300Hz are statistically analyzed, and the sum of the energies of all first-order sidebands is calculated to obtain the total sideband energy index. This quantitatively characterizes the energy intensity of the winding sideband vibration, providing a quantitative basis for the energy dimension in subsequent winding operation status determination.

[0046] (5) Evaluate the winding structure status based on sideband characteristics

[0047] The extracted sideband feature frequencies and spectral correlation mean SC mean and the overall sideband energy index SC Total The winding structure status is comprehensively evaluated by comparing and analyzing the reference sideband characteristic parameters with those of the transformer winding under normal conditions.

[0048] (6) On-site measurement verification and application

[0049] On-site vibration monitoring was carried out on a 220kV power transformer in operation. Multiple vibration measuring points were arranged on the outer wall of the transformer tank to collect winding vibration signals under actual load conditions. The online diagnosis of the transformer winding structure was achieved by analyzing the signals, and the accuracy of the diagnosis was verified by combining the results of transformer disassembly and inspection.

[0050] Furthermore, in step (1):

[0051] The nonlinear stress-strain relationship of the insulating pad can be represented by the function ψ(•). Static stiffness k s It is proportional to the derivative of stress with respect to strain: .

[0052] In the formula: σ and ε represent the stress and strain of the insulating pad, respectively.

[0053] Static stiffness k s The dynamic stiffness Δk and the dynamic stiffness are respectively: .

[0054] Under the action of periodic electromagnetic force, both strain (Δε) and dynamic stiffness (Δk) exhibit periodic changes. When the clamping force on the insulating pad is sufficiently large, When the clamping force approaches zero, the dynamic stiffness becomes negligible. In contrast, when the clamping force is below 0.5 MPa, the nonlinearity of stress-strain becomes significant, which will affect the vibration response of the winding.

[0055] The static and dynamic stiffness of the insulating pad directly constitute the overall winding stiffness matrix K, and the basic equation for the multi-degree-of-freedom vibration of the winding is: Where M is the mass matrix, C is the damping matrix, and f is the electromagnetic force vector. The decay of the clamping force will cause the stiffness matrix K to exhibit time-varying characteristics, causing the winding to generate parametric excitation vibration.

[0056] Furthermore, in step (3): for the single-point winding vibration signal x(t), its cyclic autocorrelation function is defined as:

[0057] In the formula: T represents the length of the time window, α is the cycle frequency, τ represents the time shift, and the superscript (*) indicates the complex conjugate operation.

[0058] The spectral correlation function is obtained by performing a Fourier transform:

[0059]

[0060]

[0061] In the formula: Let X be the spectral correlation function of the vibration signal x(t) at cyclic frequencies α and ω; α is the cyclic frequency, corresponding to the periodic interval of the winding sideband modulation; ω is the conventional analysis frequency; X ΔT (t,ω) represents the short-time Fourier transform (STFT) of the vibration signal x(t), where ΔT is the length of the time window. Its complex conjugate; T is the total observation time. and These are the time-averaged limit and the time-window convergence limit, respectively. These are double normalization coefficients, ensuring that the spectral correlation function has the dimension of energy density.

[0062] Furthermore, in step (4), the spectral correlation energy identifies the frequency. and The correlation between two frequencies. When α equals At that time, this method can effectively identify and The correlation between the two signals is analyzed to enhance the signal. Different cycle frequencies α are selected, and the mean spectral correlation at that frequency is calculated. .

[0063] In the formula, SC mean The mean of the spectral correlation is obtained by adjusting the spectral correlation function. The arithmetic mean is taken within a specified frequency range and is used to suppress noise and highlight periodic sideband characteristics; when the cycle frequency α matches the winding sideband spacing, SC mean A significant peak appears, which is used to identify the sideband characteristic frequency.

[0064] At the same time, calculate 0 The sum of the energies of the first-order sideband components within 300Hz yields the total sideband energy index SC. Total Quantitative characterization of sideband vibration intensity.

[0065]

[0066] Example:

[0067] like Figure 1 As shown, this embodiment provides a method for diagnosing transformer winding structure loosening based on vibration sidefrequency characteristics, including the following steps:

[0068] (1) Arrange vibration measuring points

[0069] like Figure 2As shown, the specific experimental object was a 110kV three-phase transformer of model SZ9-50000 / 110. Each phase includes a high-voltage winding and a low-voltage winding. The voltage regulating winding was not installed in the experiment. Vibration measurements were only taken at three typical measuring points of phase A, specifically at wire discs #14, #44, and #78, and marked as P1, P2, and P3.

[0070] Vibration measurements were performed using an accelerometer with a sensitivity of 100 mV / g. The sensor, fixed to a non-magnetic plastic-steel material, was used to measure the axial vibration of the winding. An adjustable hydraulic jack was installed on top of the winding; the clamping force was marked as 100% when the compressive stress of the insulating pad reached 4.0 MPa. In the experiment, the initial clamping force was set to 0.5 MPa and gradually increased to 4.0 MPa.

[0071] (2) Obtain the winding vibration signal under short-circuit load

[0072] To obtain the vibration characteristics of the winding under different currents, a short-circuit load test method was adopted. Specifically, the low-voltage side terminals were short-circuited, and a three-phase voltage was applied to the high-voltage side. The rated current of the high-voltage winding was 262.4 A, corresponding to a 100% load condition. In the experiment, after the clamping force and current were adjusted to the set values, vibration signals were collected at a sampling frequency of 10 kHz.

[0073] (3) Construct a sideband feature extraction model based on spectral correlation

[0074] Under the action of periodic electromagnetic force, the winding exhibits time-varying dynamic stiffness due to the nonlinear mechanical characteristics of the insulating pad. The periodic change of stiffness with time causes the winding vibration to exhibit typical parametric excitation vibration, thereby generating sideband characteristics.

[0075] Treating the winding as an equivalent single-degree-of-freedom parametric excitation system, the homogeneous dynamic equation under dynamic stiffness conditions is:

[0076]

[0077] In the formula: ξ is the damping coefficient, λ is the natural frequency of the winding, and Δλ is the amplitude of the natural frequency change. At a frequency of... Under the influence of electromagnetic force, Δλ can be expressed as a cosine function with the same frequency and an amplitude of 2δ: .

[0078] Transform the above into the standard Mathieu equations and replace the displacement x with y:

[0079]

[0080]

[0081] Based on the above analysis, the equation of motion for a single-degree-of-freedom system can be expressed as the standard Mathieu equation. The solution to this equation is:

[0082]

[0083] In the formula: C A and C B The constant coefficients are ce(•) and se(•), which represent the cosine and sine functions, respectively.

[0084] For a single-point winding vibration signal x(t), its cyclic autocorrelation function is defined as:

[0085]

[0086] In the formula: T represents the length of the time window, α is the cycle frequency, τ represents the time shift, and the superscript (*) indicates the complex conjugate operation.

[0087] Performing a Fourier transform on the above equation yields the spectral correlation function:

[0088]

[0089] In the formula: Defined as the signal x(t) at its center frequency At this point, the short-time Fourier transform within a finite time window ΔT:

[0090]

[0091] (4) Extracting sideband eigenvalues ​​from vibration signals

[0092] Spectral correlation can identify frequencies and The correlation between two frequencies. When α equals At that time, this method can effectively identify and The correlation between them is analyzed to enhance the signal.

[0093] Select different cycle frequencies α and calculate the mean spectral correlation at that frequency:

[0094]

[0095] This embodiment will use X ΔT Normalized by dividing by the root mean square value of the power spectrum, therefore SC mean It is a dimensionless numerical value. According to the above formula, it can be found by searching SC. mean Identify the extreme points of the function and the vibrational sideband components.

[0096] (5) Evaluate the winding structure status based on sideband characteristics

[0097] The winding sideband characteristic frequency is strongly linearly positively correlated with the structural natural frequency. Its numerical change directly reflects the change in winding structural stiffness. The sideband characteristic frequency corresponding to the first natural frequency of the winding under normal clamping force is used as the benchmark judgment value. If the extracted sideband characteristic frequency is significantly reduced compared with the benchmark value, it is determined that the winding has clamping force decay and local loosening defects.

[0098] like Figure 3 As shown, the SC frequency within the 0-300Hz band is calculated as the core analysis frequency band. mean Under normal conditions, the winding only exhibits a cyclic frequency characteristic that is an integer multiple of 100Hz related to electromagnetic force harmonics; if SC mean If a significant peak appears at a cycle frequency that is not an integer multiple of 100Hz, it is determined that the winding has sideband vibration caused by parameter excitation, that is, the winding structure parameters have periodic time-varying characteristics, which is a structural anomaly; and the higher the peak amplitude, the more significant the sideband vibration, and the more prominent the characteristics of the winding structure anomaly.

[0099] Load current is also an important factor affecting the amplitude of sideband vibration. As the current increases, the amplitude of the natural frequency change δ increases accordingly, and the amplitude of sideband vibration also increases. Figure 4 The SC under 10% and 100% load conditions with a clamping force of 0.5 MPa was compared. mean value.

[0100] Figure 5 The SC of different winding coils under clamping forces of 0.5 MPa and 4.0 MPa were compared. Total Value. SC Total The distribution of the value is significantly affected by the clamping force. Under both typical clamping conditions, large SC values ​​were observed at both ends and in the middle region of the winding. Total The distribution pattern provides a theoretical basis for the selection of measuring points in practical engineering applications.

[0101] SC under normal clamping force and 100% load Total The value is the baseline threshold; if the extracted SC Total If the value exceeds the reference threshold and rises abnormally, it is determined that there is an abnormality in the winding structure.

[0102] Figure 6 shows the curve of the winding sideband characteristic frequency as a function of clamping force. As can be seen from the figure, the winding sideband characteristic frequency is significantly positively correlated with the clamping force: when the clamping force increases from 0% to 100%, the sideband characteristic frequency increases from approximately 15Hz to approximately 41Hz. This trend indicates that the higher the winding clamping force and the greater the structural stiffness, the higher the corresponding sideband characteristic frequency; conversely, when the winding loosens and the clamping force decreases, the sideband characteristic frequency decreases accordingly. These experimental results verify the high sensitivity of the sideband characteristics to the mechanical state of the winding, providing core mechanistic support for online diagnosis of transformer winding conditions based on vibration sidebands.

[0103] (6) On-site measurement verification and application

[0104] To verify the effectiveness of the sideband vibration theory and diagnostic method described in this application, vibration analysis and routine testing were performed on a 220 kV transformer with typical structural problems. The transformer, model SFPSZ7-120000 / 220, was manufactured in 1993. In the actual vibration monitoring, eight measuring points were arranged, with measuring points #1, #2, and #3, located near the A-phase, B-phase, and C-phase windings, selected as typical measuring points.

[0105] like Figure 7 As shown, during the vibration test, vibration signals were collected at a frequency of once per minute, with each collection lasting for 1 second. Figure 8 The vibration time-domain and frequency-domain characteristics at three typical measuring points at 13:19 in the afternoon are shown. Based on the vibration characteristics observed in the frequency domain, all measuring points exhibit slight non-integer harmonic components in addition to the 100 Hz integer harmonics. However, whether these harmonics belong to the sideband components generated by parametric vibration still needs further verification.

[0106] Spectral correlation analysis was performed at each measuring point. Figure 9 SC at different cycle frequencies mean Value distribution. Sideband vibration components with frequencies of 9.3 Hz, 90.7 Hz, 109.3 Hz, 190.7 Hz, and 209.3 Hz can be clearly observed at measuring point #1. Due to vibration transmission, these harmonic components are also weakly present at measuring points #2 and #3. Under normal clamping force, the first natural frequency of the 220 kV transformer winding is approximately 40 Hz. Based on the above analysis, The characteristic frequency is significantly lower than the normal value, indicating that the winding structure has become locally loose.

[0107] The sideband energies at measurement point #1 are summed to obtain the total energy SC. Total , Figure 10 It shows its trend over 20 hours, and also provides the actual load rate changes. In the graph, as the load rate decreases, SC... TotalStatistically, it shows a decreasing trend. However, due to uncontrollable factors such as electromagnetic disturbances and oil circulation, the actual transformer SC... Total The numerical values ​​have a certain degree of uncertainty.

[0108] Finally, the suspected faulty transformer was disassembled and thoroughly inspected. As shown in Figure 11, a localized bulge deformation was observed at the top of the A-phase winding. Simultaneously, the clamping force of the A-phase winding showed significant decline; the clamping pins loosened when a single hydraulic jack applied 25 MPa (design value 45 MPa). Among all measuring points, measuring point #1 was closest to the winding fault location, revealing a correlation between side-frequency vibration components and local structural deformation.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for diagnosing transformer winding structural loosening based on sideband vibration characteristics, comprising acquiring vibration signals of the transformer winding, characterized in that, Includes the following steps: The cyclic autocorrelation function of the vibration signal is calculated and Fourier transform is performed to obtain the spectral correlation function; By analyzing the mean spectral correlation at different cycle frequencies, the sideband characteristics in the vibration signal are identified. Based on the identified sideband characteristics, the energy values ​​of the first-order sideband components within 300Hz are statistically analyzed, and the sum of the energies of all first-order sidebands is calculated to obtain the total sideband energy index. The extracted sideband characteristic frequencies, the mean value of the spectrum correlation, and the total index of the sideband energy are compared and analyzed with the reference sideband characteristic parameters of the transformer winding under normal conditions to comprehensively evaluate the winding structure status.

2. The diagnostic method according to claim 1, characterized in that, The vibration signal is collected by vibration sensors placed at the top, middle and bottom of the transformer winding to measure the axial vibration of the winding.

3. The diagnostic method according to claim 1, characterized in that, When calculating the spectral correlation function, short-time Fourier transform is used to perform time-frequency analysis on the vibration signal. The time window length is set to ensure that the frequency resolution meets the requirements for sideband interval identification, and the transform results are normalized to suppress noise interference.

4. The diagnostic method according to claim 1 or 3, characterized in that, Identifying the sideband characteristics includes: finding the extreme points of the spectral correlation mean function within a specified frequency range, and determining the sideband characteristic frequency based on the significant peak value that appears when the cyclic frequency matches the winding sideband modulation interval.

5. The diagnostic method according to claim 1, characterized in that, The first-order sideband components are sideband pairs with a center frequency of twice the power frequency and an interval of the winding's natural frequency, including vibration components at twice the power frequency plus the natural frequency and twice the power frequency minus the natural frequency.

6. The diagnostic method according to claim 1, characterized in that, The reference sideband characteristic parameters are obtained in the following way: under the condition that the transformer winding clamping force is normal and the structure is intact, the vibration signal under the short-circuit load test is collected, and the corresponding sideband characteristic frequency, spectral correlation mean and sideband energy index are extracted as reference values.

7. The diagnostic method according to claim 5 or 6, characterized in that, The evaluation of the winding structure includes: comparing the extracted sideband characteristic frequency with the reference sideband characteristic frequency. If the sideband characteristic frequency is significantly lower than the reference value, it is determined that the winding has a weakening of clamping force or a local loosening defect.

8. The diagnostic method according to claim 5 or 6, characterized in that, The assessment of the winding structure includes: analyzing the distribution of the spectrum correlation mean on the cycle frequency axis. If a significant peak appears at a cycle frequency that is not an integer multiple of the power frequency, it is determined that the winding has side-frequency vibration caused by parametric excitation. The higher the peak amplitude, the more severe the abnormality of the winding structure.

9. The diagnostic method according to claim 5 or 6, characterized in that, The evaluation of the winding structure status includes: comparing the calculated total sideband energy index with the reference threshold under normal conditions. If the total sideband energy index exceeds the reference threshold and rises abnormally, it is determined that there is an abnormality in the winding structure.

10. The diagnostic method according to claim 9, characterized in that, It also includes collecting vibration signals under different load current conditions, analyzing the trend of the total sideband energy index with the load current, and showing that the sideband vibration amplitude increases accordingly when the load current increases, thereby distinguishing between normal electromagnetic force response and abnormal sideband vibration caused by structural loosening.

Images