Method and system for identifying air gap eccentricity fault of permanent magnet generator

Abstract

The invention discloses a permanent magnet generator air gap eccentricity fault identification method and system, relates to the technical field of permanent magnet generators, and uses wavelet transform to extract time-frequency features and generate multi-band feature vectors, so that the system can more sensitively identify subtle features of air gap thickness deviation. And the extraction and analysis precision of characteristic signals of different frequency bands is effectively improved. And secondly, analyzing the air gap thickness deviation condition based on the feature vector, constructing an eccentricity degree index, and quantifying the eccentricity degree, so that the severity of the air gap eccentricity fault of the permanent magnet generator can be intuitively judged. And when the eccentricity degree exceeds a set threshold value, the system automatically sends a fault type checking instruction, so that the fault detection process is more intelligent. Afterwards, abnormal changes of all positions are analyzed through infrared thermal data and temperature gradients, air gap foreign matter faults possibly causing air gap eccentricity are further recognized, and the limitation of single signal analysis in traditional detection is made up.

Description

Technical Field

[0001] The invention relates to the technical field of permanent magnet generators, and in particular to a method and system for identifying air gap eccentricity faults of permanent magnet generators. Background Art

[0002] As a key equipment of new energy technology, permanent magnet generators are widely used in wind power generation, electric vehicles and industrial automation due to their high efficiency and stability. However, during the operation of permanent magnet generators, with the aging of mechanical components, the influence of environmental factors and the change of load, they often face faults such as air gap eccentricity. The air gap eccentricity fault is mainly manifested as uneven air gap between the rotor and the stator, which causes asymmetric distribution of the motor magnetic field, causing abnormal phenomena such as vibration and heat, and thus affecting the overall performance and service life of the generator. Therefore, the identification of air gap eccentricity fault of permanent magnet generator is of great practical significance.

[0003] For the identification of air gap eccentricity fault of permanent magnet generator, the current traditional methods mostly rely on vibration analysis or single magnetic field signal analysis. However, these methods have certain limitations in detection precision and accuracy. Since the air gap eccentricity fault of permanent magnet generator has multi-band characteristics, the signal is often non-stationary. A single analysis will cause the system to lack support for comprehensive analysis of multi-source signals. In addition, traditional detection methods usually ignore the local thermal anomalies that may be caused by foreign matter in the air gap and lack the effective use of temperature characteristics, thereby reducing the comprehensive diagnostic capability of air gap eccentricity fault. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for identifying air gap eccentricity faults of a permanent magnet generator, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for identifying air gap eccentricity fault of a permanent magnet generator comprises the following steps: S1. deploying several groups of sensors in the permanent magnet generator in advance, and monitoring the vibration signal and magnetic field signal of the permanent magnet generator in real time when it is working based on the several groups of sensors to construct motor operation data information; S2, preprocessing the motor operation data information and using wavelet transform to obtain the time-frequency characteristics of the vibration signal and the magnetic field signal to generate a feature vector when the motor is operating; S3. Based on the characteristic vector formed after data preprocessing in S2, it is preliminarily analyzed whether there is a deviation in the air gap thickness of the permanent magnet generator, so as to construct and analyze the eccentricity index Pczb, and based on the value of the eccentricity index Pczb, it is determined whether it is necessary to issue a fault type troubleshooting instruction externally; S4. After receiving the fault type troubleshooting instruction, use thermal imaging technology to monitor the two-dimensional temperature distribution of the air gap area in the permanent magnet generator to obtain infrared thermal data, and analyze the risk of foreign matter in the air gap area in the permanent magnet generator based on the infrared thermal data, and construct the temperature gradient at each position. ; S5. Preset the safety threshold Q by adjusting the temperature gradient at each position. Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator.

[0006] Preferably, the specific steps of S1 include: S11, deploying several groups of sensors in the permanent magnet generator in advance to form a sensor group, wherein the sensor group includes a vibration sensor and a Hall sensor; S12. According to the sensor group formed in S11, real-time monitoring is performed to obtain the vibration signal and magnetic field signal of the permanent magnet generator when it is working, so as to construct motor operation data information.

[0007] Preferably, the specific steps of S2 include: S21, removing noise in the motor operation data information by wavelet denoising technology, and removing trend components in the motor operation data information by sliding average method, so as to perform data preprocessing on the motor operation data information, and after data preprocessing, extracting time-frequency characteristics of the motor operation data information by wavelet transform technology, so as to generate a characteristic vector when the motor is operating, wherein the characteristic vector when the motor is operating includes a magnetic flux density value in the air gap and vibration amplitude .

[0008] Preferably, the specific steps of S3 include: S31. According to the characteristic vector of the motor during operation, after feature extraction, the change of the magnetic flux density in the air gap of the permanent magnet generator over time is analyzed to calculate the change rate of the magnetic field intensity in different frequency bands. , the rate of change of magnetic field intensity in different frequency bands Obtained by the following formula: ; In the formula, Expressed as the magnetic flux density value in the air gap, Expressed as a time interval, is the time derivative of magnetic flux density; S32, according to the characteristic vector when the motor is operating, after feature extraction, analyze the synchronization between the vibration signal and the magnetic field signal of the permanent magnet generator to calculate the phase difference between the vibration signal and the magnetic field signal in different frequency bands , which is obtained according to the following formula: ; In the formula, Expressed as the vibration amplitude, Expressed as vibration amplitude and the magnetic flux density in the air gap The dot product of is used to calculate the phase relationship between the two. The dot product is a scalar of the interaction between the two signals, reflecting the amplitude and relative direction of the two signals; Expressed as vibration amplitude The model, Expressed as the magnetic flux density value in the gap The model, is the inverse cosine function.

[0009] Preferably, the specific step S3 also includes: S33, based on the magnetic field intensity change rate obtained in S31 and S32 and the phase difference between the vibration signal and the magnetic field signal in different frequency bands , preliminarily analyze whether there is a deviation in the air gap thickness of the permanent magnet generator, and after linear normalization processing, construct and analyze the eccentricity index Pczb, which is obtained by the following formula: ; In the formula, Expressed as the number of frequency bands, , Expressed as The rate of change of magnetic field strength over the frequency band, Expressed as the rate of change of average magnetic field intensity, Expressed as The phase difference between the vibration signal and the magnetic field signal in the frequency band, Expressed as the average value of the phase difference between the vibration signal and the magnetic field signal, and are weight values, Expressed as the first correction constant, where and The specific value is set by the user according to the situation.

[0010] Preferably, the specific step S3 also includes: S34, pre-set a judgment threshold value P, and compare and analyze it with the eccentricity index Pczb to determine whether there is a deviation in the air gap thickness of the current permanent magnet generator. The specific analysis content is as follows: If the eccentricity index Pczb exceeds the judgment threshold value P, it is judged that there is a deviation in the air gap thickness of the current permanent magnet generator, and a fault type troubleshooting instruction will be issued externally at this time; If the eccentricity index Pczb does not exceed the judgment threshold value P, it is judged that there is no deviation in the air gap thickness of the current permanent magnet generator, and a green warning notification will be issued.

[0011] Preferably, the specific steps of S4 include: S41, after receiving the fault type troubleshooting instruction issued in S34, using a thermal imaging device to capture an infrared thermal image of the air gap area in the permanent magnet generator, and using Gaussian filtering technology to perform image denoising on the infrared thermal image, wherein the thermal imaging device refers to an infrared thermal imager; S42, acquiring infrared thermal data according to the infrared thermal image after image denoising, wherein the infrared thermal data includes the temperature value at each pixel position in the infrared thermal image ; S43, by calculating the temperature value at each pixel position in the infrared thermal image Compare with the preset threshold value, if the temperature value at each pixel position in the infrared thermal image When the value exceeds the preset threshold, the corresponding pixel position is marked as an outlier, and the outlier is counted to obtain the number of outliers YS.

[0012] Preferably, the specific step S4 also includes: S43, analyzing the risk of foreign matter in the air gap area of ​​the permanent magnet generator according to the infrared thermal data, so as to construct the temperature gradient at each pixel position , which can be obtained by: ; In the formula, It is expressed as the temperature gradient along the horizontal axis; It is expressed as the temperature gradient along the vertical axis; x and y represent the horizontal and vertical coordinates in the infrared thermal image, respectively.

[0013] Preferably, the specific steps of S5 include: S51, the temperature gradient at each position Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator. The specific judgment content is as follows: S511, if the temperature gradient at the corresponding position When the safety threshold Q is exceeded, the current location is marked as a local hotspot; S512, if the temperature gradient at the corresponding position When the safety threshold Q is not exceeded, the current location is not marked as a local hotspot; S52, counting the local hot spots obtained in S511 to obtain the number of local hot spots JS. When the number of local hot spots JS exceeds 80% of the number of abnormal points YS, it is determined that the current permanent magnet generator has an air gap foreign body fault.

[0014] A permanent magnet generator air gap eccentricity fault identification system, comprising an acquisition module, a processing module, an eccentricity analysis module, a type troubleshooting module and a type feedback module; The acquisition module is used to deploy several groups of sensors in the permanent magnet generator in advance, and monitor the vibration signal and magnetic field signal of the permanent magnet generator in real time when it is working based on the several groups of sensors to construct motor operation data information; The processing module is used to perform data preprocessing on the motor operation data information and obtain the time-frequency characteristics of the vibration signal and the magnetic field signal by using wavelet transform to generate a feature vector when the motor is operating; The eccentricity analysis module is used to preliminarily analyze whether there is a deviation in the air gap thickness of the permanent magnet generator based on the characteristic vector formed after data preprocessing, so as to construct and analyze the eccentricity index Pczb, and based on the value of the eccentricity index Pczb, determine whether it is necessary to issue a fault type troubleshooting instruction externally; The type troubleshooting module is used to monitor the two-dimensional temperature distribution of the air gap area in the permanent magnet generator by using thermal imaging technology after receiving the fault type troubleshooting instruction, so as to obtain infrared thermal data, and analyze the risk of foreign matter in the air gap area in the permanent magnet generator based on the infrared thermal data, and construct the temperature gradient at each position. ; The type feedback module is used to pre-set the safety threshold Q by Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator.

[0015] The present invention provides a method and system for identifying air gap eccentricity fault of a permanent magnet generator, which has the following beneficial effects: (1) Through the deployment of the sensor group in step S1, the method monitors and obtains vibration signals and magnetic field signals in real time, so that the fault identification method can timely capture the operating data of the permanent magnet generator under different operating conditions, providing a variety of basic data for subsequent analysis; the wavelet transform is used to extract the time-frequency features and generate multi-band feature vectors, so that the system can more sensitively identify the subtle features of the air gap thickness deviation, effectively improving the extraction and analysis accuracy of the feature signals of different frequency bands. Secondly, based on the feature vector, the air gap thickness deviation is analyzed, the eccentricity index Pczb is constructed, and the eccentricity is quantified, which can intuitively judge the severity of the air gap eccentricity fault of the permanent magnet generator. When the eccentricity exceeds the set threshold, the system automatically issues a fault type troubleshooting instruction, making the fault detection process more intelligent. Subsequently, the temperature distribution of the air gap area is monitored by thermal imaging technology, and the abnormal changes of each position are analyzed by combining infrared thermal data and temperature gradient to further identify the air gap foreign body fault that may cause air gap eccentricity, making up for the limitations of single signal analysis in traditional detection. Finally, in step S5, the temperature gradient at each position is further compared with the safety threshold to count the local hot spot position and accurately judge the risk of foreign matter in the air gap. In short, this method combines multiple source data such as vibration signals, magnetic field signals, and temperature signals, which not only improves the recognition accuracy of air gap eccentricity faults, but also makes the troubleshooting of complex faults such as air gap foreign matter more comprehensive, providing effective guarantee for the safe and stable operation of permanent magnet generators.

[0016] (2) In step S2, wavelet denoising technology is used to remove noise from the collected motor operation data information, and the trend component in the data is removed by the sliding average method, making the data fluctuation more stable and the characteristics more significant. This data preprocessing method improves the representativeness of signal characteristics and lays a more stable foundation for fault analysis. On this basis, wavelet transform technology is further used to extract time-frequency characteristics and generate feature vectors containing the magnetic flux density value and vibration amplitude in the air gap, so that the system can more keenly identify the local characteristics and spectrum changes of the fault signal. These processing steps not only improve the accuracy of air gap eccentricity fault detection, but also effectively eliminate the interference components in the signal, providing high-quality feature data for subsequent analysis.

[0017] (3) First, in step S3, by analyzing the change of the magnetic flux density in the air gap in the characteristic vector over time, the change rate of the magnetic field intensity in different frequency bands can be obtained. The calculation of this indicator accurately reflects the change characteristics of the air gap thickness, so that the system can monitor the fluctuation of the magnetic field intensity of the permanent magnet generator in real time in different frequency ranges, so as to more sensitively detect the fault characteristics caused by the air gap eccentricity. This method uses Hall sensors or magnetic flux density sensors to measure the time change of magnetic field intensity, which effectively improves the timeliness of fault detection and the accuracy of data. Secondly, through the analysis of the synchronization between the vibration signal and the magnetic field signal in step S32, the phase difference between the two signals in different frequency bands is further calculated, revealing the relative changes of vibration and magnetic field, thereby helping to identify the type of air gap eccentricity fault. This multi-band phase difference analysis method not only increases the richness of the interaction between the detection signals, but also can identify the fault type, making the system more accurate and comprehensive in identifying fault characteristics under complex working conditions, and providing key data information support for subsequent fault judgment and maintenance decisions.

[0018] (4) Through the temperature gradient analysis in step S43, the local temperature change rate of each pixel is calculated. The system can accurately identify the location of the temperature mutation in the air gap area. The temperature gradient analysis can reveal the distribution of local hot spots and temperature anomaly points, and effectively identify the possibility of the existence of foreign matter in the air gap. This risk analysis method based on temperature gradient can not only accurately locate the fault location, but also provide an in-depth assessment of the local thermal anomaly caused by foreign matter in the air gap, providing reliable data support and diagnostic basis for further investigation of air gap eccentricity faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a method for identifying air gap eccentricity fault of a permanent magnet generator according to the present invention; Figure 2 The present invention is a block diagram of a permanent magnet generator air gap eccentricity fault identification system. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 See also Figure 1 The present invention provides a method for identifying air gap eccentricity fault of a permanent magnet generator, comprising the following steps: S1. deploying several groups of sensors in the permanent magnet generator in advance, and monitoring the vibration signal and magnetic field signal of the permanent magnet generator in real time when it is working based on the several groups of sensors to construct motor operation data information; S2, preprocessing the motor operation data information and using wavelet transform to obtain the time-frequency characteristics of the vibration signal and the magnetic field signal to generate a feature vector when the motor is operating; S3. Based on the characteristic vector formed after data preprocessing in S2, it is preliminarily analyzed whether there is a deviation in the air gap thickness of the permanent magnet generator, so as to construct and analyze the eccentricity index Pczb, and based on the value of the eccentricity index Pczb, it is determined whether it is necessary to issue a fault type troubleshooting instruction externally; S4. After receiving the fault type troubleshooting instruction, use thermal imaging technology to monitor the two-dimensional temperature distribution of the air gap area in the permanent magnet generator to obtain infrared thermal data, and analyze the risk of foreign matter in the air gap area in the permanent magnet generator based on the infrared thermal data, and construct the temperature gradient at each position. ; S5. Preset the safety threshold Q by adjusting the temperature gradient at each position. Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator.

[0022] In this embodiment, the method further improves the accuracy and comprehensiveness of identifying air gap eccentricity faults through comprehensive monitoring and analysis of multiple signals. Specifically, in the method, step S1 realizes real-time acquisition of vibration signals and magnetic field signals by deploying multiple sensors in the permanent magnet generator, ensuring the real-time and reliability of the data. Step S2 uses wavelet transform to perform data preprocessing and time-frequency feature extraction on vibration signals and magnetic field signals, which not only effectively removes noise interference, but also retains the key feature information of the signal, so that the generated feature vector more accurately reflects the operating state of the motor. In addition, in step S3, by constructing an eccentricity index, it is possible to quickly determine whether there is a deviation in the air gap thickness. If an abnormality is detected, the fault type troubleshooting instruction is immediately triggered, further improving the fault response speed. Step S4 uses thermal imaging technology to perform infrared thermal imaging monitoring on the air gap area, captures high-precision temperature distribution information, and calculates the temperature gradient at each position, further improving the detection sensitivity of local heating caused by foreign matter in the air gap. Finally, in step S5, the temperature gradient is compared with the safety threshold, which realizes accurate identification of different fault types, especially the identification of eccentric faults caused by foreign matter in the air gap has higher accuracy and reliability. Therefore, this method has the beneficial effects of high real-time performance, accurate detection, strong adaptability, etc., and provides a strong guarantee for the safe and efficient operation of permanent magnet generators.

[0023] Example 2 Please refer to Figure 1 , specifically: S1 specific steps include: S11, deploying several groups of sensors in the permanent magnet generator in advance to form a sensor group, wherein the sensor group includes a vibration sensor and a Hall sensor; S12. According to the sensor group formed in S11, real-time monitoring is performed to obtain the vibration signal and magnetic field signal of the permanent magnet generator when it is working, so as to construct motor operation data information.

[0024] The specific steps of S2 include: S21. Remove the noise in the motor operation data information by wavelet denoising technology, and remove the trend component in the motor operation data information by sliding average method to ensure data stability and further improve the representativeness of signal characteristics, so as to perform data preprocessing on the motor operation data information, and after data preprocessing, use wavelet transform technology to extract the time-frequency characteristics of the motor operation data information to generate a feature vector when the motor is operating, wherein the feature vector when the motor is operating includes the magnetic flux density value in the air gap and vibration amplitude .

[0025] In this embodiment, firstly, through the deployment of the sensor group in step S1, an efficient monitoring network can be formed inside the permanent magnet generator; the sensor group includes a variety of sensors for capturing vibration signals and magnetic field signals, and real-time acquisition of operating data during the operation of the motor ensures the comprehensiveness and accuracy of data acquisition. The layout of this sensor group further enhances the control of the motor operation state, and the real-time data information construction provides high-quality basic data support for subsequent data analysis. Secondly, the noise and trend components in the data are removed by the wavelet denoising and sliding average method in step S2, which can ensure the stability and representativeness of the signal characteristics and improve the reliability of the data. The data preprocessing process eliminates the interference factors in the signal, making the characteristics of the air gap eccentricity fault more prominent and clear. Based on the preprocessed data, the wavelet transform is used to extract the time-frequency characteristics to generate feature vectors, which accurately describe the changes in the magnetic flux density and vibration amplitude in the air gap, and provide multi-dimensional and refined feature information for the subsequent eccentricity fault identification, enhancing the fault identification ability of the system. Through these steps, the method further realizes the precise monitoring and multi-feature fusion of the motor's operating status, ensures the high accuracy of air gap eccentricity fault identification, thereby improving the comprehensiveness and real-time performance of permanent magnet generator fault diagnosis, and provides effective early warning and maintenance guarantees for the stable operation of the motor.

[0026] Example 3 Please refer to Figure 1 , specifically: S3 specific steps include: S31. According to the characteristic vector of the motor during operation, after feature extraction, the change of the magnetic flux density in the air gap of the permanent magnet generator over time is analyzed to calculate the change rate of the magnetic field intensity in different frequency bands. , the rate of change of magnetic field intensity in different frequency bands Obtained by the following formula: ; In the formula, Expressed as the magnetic flux density value in the air gap, Expressed as a time interval, It is the derivative of the magnetic flux density with time. This derivative value reflects its fluctuation and is measured in Tesla per second (T / s). The magnetic field strength can be measured by Hall sensors or magnetic flux density sensors, which can capture the change of magnetic field strength in the air gap with time.

[0027] The above-mentioned magnetic flux density value in the air gap is It reflects the strength of the magnetic field inside the permanent magnet generator. In order to measure the magnetic flux density, it can be monitored and obtained through a Hall sensor or a magnetic flux density sensor; S32, according to the characteristic vector when the motor is operating, after feature extraction, analyze the synchronization between the vibration signal and the magnetic field signal of the permanent magnet generator to calculate the phase difference between the vibration signal and the magnetic field signal in different frequency bands , which is obtained according to the following formula: ; In the formula, Expressed as the vibration amplitude, Expressed as vibration amplitude and the magnetic flux density in the air gap The dot product of is used to calculate the phase relationship between the two. The dot product is a scalar of the interaction between the two signals, reflecting the amplitude and relative direction of the two signals; Expressed as vibration amplitude The model, Expressed as the magnetic flux density value in the gap The model, is the inverse cosine function (also called the arccosine or arccosine value).

[0028] The above vibration amplitude It reflects the mechanical vibration or imbalance that may occur during the operation of the permanent magnet generator, which can be monitored and obtained through vibration sensors, accelerometers or piezoelectric sensors.

[0029] When wavelet transform is used to extract time-frequency features of vibration signals and magnetic field signals, the signals will be decomposed into different frequency bands (i.e., different scales or frequency bands). In each frequency band, there may be an independent phase relationship between the vibration signal and the magnetic field signal, thus generating multiple sets of phase differences, each corresponding to a specific frequency band. The phase differences of multiple sets of frequency bands extracted by wavelet transform can provide relative phase information of vibration signals and magnetic field signals within different frequency ranges, thus providing key data support for the identification of air gap eccentricity and other faults. The existence of multiple sets of phase differences enables the system to more comprehensively capture and analyze the complex characteristics of the motor under fault conditions.

[0030] In this embodiment, firstly, through the feature extraction and flux density change rate calculation of step S31, the magnetic field intensity change in the air gap of the permanent magnet generator can be analyzed in detail. The magnetic field intensity change rate in different frequency bands reflects the characteristics of the air gap eccentricity fault at each frequency, which can help identify the specific location and spectral characteristics of the fault. This method uses a Hall sensor or a flux density sensor to capture the magnetic field changes in real time and calculates the time derivative of the flux density, thereby improving the system's sensitivity to the uneven distribution of the magnetic field in the air gap and effectively enhancing the ability to identify early signs of faults. Secondly, through the phase difference calculation in step S32, the method further analyzes the synchronization between the vibration signal and the magnetic field signal in different frequency bands; the phase difference between the vibration amplitude and the magnetic flux density provides the relative position information of the signal in the frequency domain, which can not only further help identify the type of air gap eccentricity (such as static or dynamic eccentricity), but also accurately capture the interaction between the vibration signal and the magnetic field signal. By using these phase characteristics, the system can reflect the unique patterns of the fault signal in each frequency band in a more comprehensive way and effectively distinguish different types of eccentricity faults. This analysis method based on phase difference improves the reliability of fault diagnosis and provides an accurate positioning basis for subsequent fault detection and repair.

[0031] Example 4 Please refer to Figure 1 , specifically: S3 specific steps also include: S33, based on the magnetic field intensity change rate obtained in S31 and S32 and the phase difference between the vibration signal and the magnetic field signal in different frequency bands , preliminarily analyze whether there is a deviation in the air gap thickness of the permanent magnet generator, and after linear normalization processing, construct and analyze the eccentricity index Pczb, which is obtained by the following formula: ; In the formula, Expressed as the number of frequency bands, , Expressed as The rate of change of magnetic field strength over the frequency band, Expressed as the rate of change of average magnetic field intensity, Expressed as The phase difference between the vibration signal and the magnetic field signal in the frequency band, Expressed as the average value of the phase difference between the vibration signal and the magnetic field signal, and are weight values, Expressed as the first correction constant, where and The specific value is set by the user according to the situation.

[0032] The specific steps of S3 also include: S34, pre-set a judgment threshold value P, and compare and analyze it with the eccentricity index Pczb to determine whether there is a deviation in the air gap thickness of the current permanent magnet generator. The specific analysis content is as follows: If the eccentricity index Pczb exceeds the judgment threshold value P, it is judged that there is a deviation in the air gap thickness of the current permanent magnet generator (i.e., there is an air gap eccentricity fault problem), and a fault type troubleshooting instruction will be issued externally at this time; If the eccentricity index Pczb does not exceed the judgment threshold value P, it is judged that there is no deviation in the air gap thickness of the current permanent magnet generator, and a green warning notification will be issued.

[0033] In this embodiment, first, in step S33, based on the rate of change of magnetic field intensity and the phase difference between the vibration signal and the magnetic field signal in different frequency bands, the deviation of the air gap thickness of the permanent magnet generator can be accurately analyzed, and the eccentricity index Pczb is constructed through linear normalization processing. The calculation method of the eccentricity index adopts multi-band signal analysis, and integrates the mean value of the rate of change of magnetic field intensity and the phase difference in a weighted manner to ensure the effective use of each frequency band information in fault identification, thereby improving the sensitivity of fault detection. The multi-band analysis method of this method can more carefully reflect the complex characteristics of air gap eccentricity, making eccentricity detection more accurate. Secondly, in step S34, the eccentricity index can intelligently determine whether there is a deviation in the air gap thickness of the current permanent magnet generator by comparing it with a pre-set judgment threshold, thereby realizing an automated fault judgment and feedback mechanism. When the eccentricity index exceeds the threshold, the system automatically issues a fault type troubleshooting instruction, which helps to conduct detailed fault analysis in a timely manner; when it does not exceed the threshold, the system only issues a green warning notification to indicate that the equipment is in normal operation. This dual-mode notification mechanism of intelligent early warning and fault troubleshooting not only improves the real-time nature of fault identification, but also optimizes the response efficiency of the system, further avoiding false alarms or missed alarms, and providing reliable guarantee for the stable operation of the permanent magnet generator.

[0034] Example 5 Please refer to Figure 1 , specifically: S4 specific steps include: S41, after receiving the fault type troubleshooting instruction issued in S34, using a thermal imaging device to capture an infrared thermal image of the air gap area in the permanent magnet generator, and using Gaussian filtering technology to perform image denoising on the infrared thermal image to reduce background and noise interference, wherein the thermal imaging device refers to an infrared thermal imager; S42, acquiring infrared thermal data according to the infrared thermal image after image denoising, wherein the infrared thermal data includes the temperature value at each pixel position in the infrared thermal image ; S43, by calculating the temperature value at each pixel position in the infrared thermal image Compare with the preset threshold value, if the temperature value at each pixel position in the infrared thermal image When the value exceeds the preset threshold, the corresponding pixel position is marked as an outlier, and the outlier is counted to obtain the number of outliers YS.

[0035] The preset threshold value can be determined by the statistical characteristics of the temperature distribution of the image (such as mean value, standard deviation), specifically: preset threshold value = temperature mean value + k* temperature standard deviation; k is a constant, usually with a value of 1-3, corresponding to different confidence levels, and the specific value is set by the user (according to the actual situation); The specific steps of S4 also include: S43, analyzing the risk of foreign matter in the air gap area of ​​the permanent magnet generator according to the infrared thermal data, so as to construct the temperature gradient at each pixel position , which can be obtained by: ; In the formula, Indicates pixel position The temperature gradient at a location can also be understood as the local temperature change rate at that location. The larger the gradient value, the more drastic the temperature change around that location, which usually occurs in temperature mutation areas (such as hot spot edges). It is expressed as the temperature gradient along the horizontal axis, that is, the rate of change of temperature in the x direction. This is a partial derivative that measures the pixel position The temperature variation relative to its adjacent points in the x direction; It is expressed as the temperature gradient along the vertical axis, that is, the rate of change of temperature in the y direction. This is a partial derivative that measures the pixel position The temperature change in the y direction relative to its adjacent points; x and y represent the horizontal and vertical coordinates in the infrared thermal image, respectively.

[0036] In this embodiment, first, in step S41, the infrared thermal image of the air gap area in the permanent magnet generator is captured by a thermal imaging device, and the image denoising is performed using Gaussian filtering technology, which effectively reduces background and noise interference, ensures the clarity and accuracy of the infrared thermal image, and this denoising process makes the temperature data in the image more representative, ensures the accuracy of temperature analysis, and lays a high-quality data foundation for subsequent fault analysis. Secondly, in steps S42 and S43, by comparing the temperature value of each pixel position of the infrared thermal image with a preset threshold, the pixels exceeding the threshold are marked as abnormal points and the number of abnormal points is counted. This threshold setting method based on the temperature mean and standard deviation improves the recognition accuracy, can adapt to the temperature distribution characteristics under different working environments, and makes fault identification more robust. In addition, the temperature gradient calculated based on infrared thermal data in S43 can reflect the local temperature changes at each position in the air gap area. The calculation of the temperature gradient provides a detailed distribution of the temperature change in the air gap area. The larger the gradient, the more likely it is to indicate the presence of foreign matter in the air gap, thereby improving the positioning accuracy of hot spots and foreign matter in the air gap. In summary, this method can further effectively identify local hotspots caused by air gap foreign matter in the air gap area of ​​the permanent magnet generator, making fault identification more accurate and comprehensive, and providing a strong guarantee for the safety of air gap foreign matter detection and operating status of the permanent magnet generator. This method improves the accuracy and stability of air gap foreign matter detection while reducing the occurrence of false alarms, further optimizing the operation management of the permanent magnet generator.

[0037] Example 6 Please refer to Figure 1 Specifically: S5 includes the following steps: S51, the temperature gradient at each position Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator. The specific judgment content is as follows: S511, if the temperature gradient at the corresponding position When the safety threshold Q is exceeded, the current location is marked as a local hotspot; S512, if the temperature gradient at the corresponding position When the safety threshold Q is not exceeded, the current location is not marked as a local hotspot; S52, counting the local hot spots obtained in S511 to obtain the number of local hot spots JS. When the number of local hot spots JS exceeds 80% of the number of abnormal points YS, it is determined that the current permanent magnet generator has an air gap foreign body fault.

[0038] In this embodiment, first, in step S51, by comparing the temperature gradient of each position with the preset safety threshold, the system can effectively determine the local hot spots of abnormal temperature; the position where the temperature gradient exceeds the safety threshold is automatically marked as a local hot spot, which helps to identify the temperature concentration phenomenon in the air gap area in real time, and improves the response speed to air gap eccentricity and foreign body faults. Secondly, in step S52, the system counts the marked local hot spots, obtains the number of local hot spots, and compares and analyzes it with the total number of abnormal points. When the number of local hot spots exceeds 80% of the number of abnormal points, the system determines that there is an air gap foreign body fault. This analysis method based on statistical comparison can accurately identify the local temperature anomaly caused by foreign matter in the air gap, further avoiding misjudgment and missed judgment. This method achieves high-sensitivity detection of air gap foreign body faults by comparing the number of local hot spots and the number of abnormal points, and provides data support for accurate judgment of fault types.

[0039] Example 7 Please refer to Figure 2 ,Specifically: A permanent magnet generator air gap eccentricity fault identification system, including an acquisition module, a processing module, an eccentricity analysis module, a type troubleshooting module and a type feedback module; The acquisition module is used to deploy several groups of sensors in the permanent magnet generator in advance, and monitor the vibration signal and magnetic field signal of the permanent magnet generator in real time when it is working based on the several groups of sensors to construct motor operation data information; The processing module is used to perform data preprocessing on the motor operation data information and obtain the time-frequency characteristics of the vibration signal and the magnetic field signal by using wavelet transform to generate a feature vector when the motor is operating; The eccentricity analysis module is used to preliminarily analyze whether there is a deviation in the air gap thickness of the permanent magnet generator based on the characteristic vector formed after data preprocessing, so as to construct and analyze the eccentricity index Pczb, and based on the value of the eccentricity index Pczb, determine whether it is necessary to issue a fault type troubleshooting instruction externally; The type troubleshooting module is used to monitor the two-dimensional temperature distribution of the air gap area in the permanent magnet generator by using thermal imaging technology after receiving the fault type troubleshooting instruction, so as to obtain infrared thermal data, and analyze the risk of foreign matter in the air gap area in the permanent magnet generator based on the infrared thermal data, and construct the temperature gradient at each position. ; The type feedback module is used to pre-set the safety threshold Q by Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying air gap eccentricity fault of a permanent magnet generator, characterized in that: The following steps are included: S1. deploying several groups of sensors in the permanent magnet generator in advance, and monitoring the vibration signal and magnetic field signal of the permanent magnet generator in real time when it is working based on the several groups of sensors to construct motor operation data information; S2, preprocessing the motor operation data information and using wavelet transform to obtain the time-frequency characteristics of the vibration signal and the magnetic field signal to generate a feature vector when the motor is operating; S3. Based on the characteristic vector formed after data preprocessing in S2, it is preliminarily analyzed whether there is a deviation in the air gap thickness of the permanent magnet generator, so as to construct and analyze the eccentricity index Pczb, and based on the value of the eccentricity index Pczb, it is determined whether it is necessary to issue a fault type troubleshooting instruction externally; S4. After receiving the fault type troubleshooting instruction, use thermal imaging technology to monitor the two-dimensional temperature distribution of the air gap area in the permanent magnet generator to obtain infrared thermal data, and analyze the risk of foreign matter in the air gap area in the permanent magnet generator based on the infrared thermal data, and construct the temperature gradient at each position. ; S5. Preset the safety threshold Q by adjusting the temperature gradient at each position. Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator.

2. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 1, characterized in that: The specific steps of S1 include: S11, deploying several groups of sensors in the permanent magnet generator in advance to form a sensor group, wherein the sensor group includes a vibration sensor and a Hall sensor; S12. According to the sensor group formed in S11, real-time monitoring is performed to obtain the vibration signal and magnetic field signal of the permanent magnet generator when it is working, so as to construct motor operation data information.

3. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 2, characterized in that: The specific steps of S2 include: S21, removing noise in the motor operation data information by wavelet denoising technology, and removing trend components in the motor operation data information by sliding average method, so as to perform data preprocessing on the motor operation data information, and after data preprocessing, extracting time-frequency characteristics of the motor operation data information by wavelet transform technology, so as to generate a characteristic vector when the motor is operating, wherein the characteristic vector when the motor is operating includes a magnetic flux density value in the air gap and vibration amplitude .

4. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 3, characterized in that: The specific steps of S3 include: S31. According to the characteristic vector of the motor during operation, after feature extraction, the change of the magnetic flux density in the air gap of the permanent magnet generator over time is analyzed to calculate the change rate of the magnetic field intensity in different frequency bands. , the rate of change of magnetic field intensity in different frequency bands Obtained by the following formula: ； In the formula, Expressed as the magnetic flux density value in the air gap, Expressed as a time interval, is the time derivative of magnetic flux density; S32, according to the characteristic vector when the motor is operating, after feature extraction, analyze the synchronization between the vibration signal and the magnetic field signal of the permanent magnet generator to calculate the phase difference between the vibration signal and the magnetic field signal in different frequency bands , which is obtained according to the following formula: ； In the formula, Expressed as the vibration amplitude, Expressed as vibration amplitude and the magnetic flux density in the air gap The dot product of is used to calculate the phase relationship between the two. The dot product is a scalar of the interaction between the two signals, reflecting the amplitude and relative direction of the two signals; Expressed as vibration amplitude The model, Expressed as the magnetic flux density value in the gap The model, is the inverse cosine function.

5. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 4, characterized in that: The specific steps of S3 also include: S33, based on the magnetic field intensity change rate obtained in S31 and S32 and the phase difference between the vibration signal and the magnetic field signal in different frequency bands , preliminarily analyze whether there is a deviation in the air gap thickness of the permanent magnet generator, and after linear normalization processing, construct and analyze the eccentricity index Pczb, which is obtained by the following formula: ； In the formula, Expressed as the number of frequency bands, , Expressed as The rate of change of magnetic field strength over the frequency band, Expressed as the rate of change of average magnetic field intensity, Expressed as The phase difference between the vibration signal and the magnetic field signal in the frequency band, Expressed as the average value of the phase difference between the vibration signal and the magnetic field signal, and are weight values, Expressed as the first correction constant, where and The specific value is set by the user according to the situation.

6. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 5, characterized in that: The specific steps of S3 also include: S34, pre-set a judgment threshold value P, and compare and analyze it with the eccentricity index Pczb to determine whether there is a deviation in the air gap thickness of the current permanent magnet generator. The specific analysis content is as follows: If the eccentricity index Pczb exceeds the judgment threshold value P, it is judged that there is a deviation in the air gap thickness of the current permanent magnet generator, and a fault type troubleshooting instruction will be issued externally at this time; If the eccentricity index Pczb does not exceed the judgment threshold value P, it is judged that there is no deviation in the air gap thickness of the current permanent magnet generator, and a green warning notification will be issued.

7. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 6, characterized in that: The specific steps of S4 include: S41, after receiving the fault type troubleshooting instruction issued in S34, using a thermal imaging device to capture an infrared thermal image of the air gap area in the permanent magnet generator, and using Gaussian filtering technology to perform image denoising on the infrared thermal image, wherein the thermal imaging device refers to an infrared thermal imager; S42, acquiring infrared thermal data according to the infrared thermal image after image denoising, wherein the infrared thermal data includes the temperature value at each pixel position in the infrared thermal image ; S43, by calculating the temperature value at each pixel position in the infrared thermal image Compare with the preset threshold value, if the temperature value at each pixel position in the infrared thermal image When the value exceeds the preset threshold, the corresponding pixel position is marked as an outlier, and the outlier is counted to obtain the number of outliers YS.

8. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 7, characterized in that: The specific steps of S4 also include: S43, analyzing the risk of foreign matter in the air gap area of ​​the permanent magnet generator according to the infrared thermal data, so as to construct the temperature gradient at each pixel position , which can be obtained by: ； In the formula, It is expressed as the temperature gradient along the horizontal axis; It is expressed as the temperature gradient along the vertical axis; x and y represent the horizontal and vertical coordinates in the infrared thermal image, respectively.

9. A method for identifying air gap eccentricity fault of a permanent magnet generator according to claim 7, characterized in that: The specific steps of S5 include: S51, the temperature gradient at each position Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator. The specific judgment content is as follows: S511, if the temperature gradient at the corresponding position When the safety threshold Q is exceeded, the current location is marked as a local hotspot; S512, if the temperature gradient at the corresponding position When the safety threshold Q is not exceeded, the current location is not marked as a local hotspot; S52, counting the local hot spots obtained in S511 to obtain the number of local hot spots JS. When the number of local hot spots JS exceeds 80% of the number of abnormal points YS, it is determined that the current permanent magnet generator has an air gap foreign body fault.

10. A permanent magnet generator air gap eccentricity fault identification system, used to implement a permanent magnet generator air gap eccentricity fault identification method according to any one of claims 1 to 9, characterized in that: It includes an acquisition module, a processing module, an eccentricity analysis module, a type screening module and a type feedback module; The acquisition module is used to deploy several groups of sensors in the permanent magnet generator in advance, and monitor the vibration signal and magnetic field signal of the permanent magnet generator in real time when it is working based on the several groups of sensors to construct motor operation data information; The processing module is used to perform data preprocessing on the motor operation data information and obtain the time-frequency characteristics of the vibration signal and the magnetic field signal by using wavelet transform to generate a feature vector when the motor is operating; The eccentricity analysis module is used to preliminarily analyze whether there is a deviation in the air gap thickness of the permanent magnet generator based on the characteristic vector formed after data preprocessing, so as to construct and analyze the eccentricity index Pczb, and based on the value of the eccentricity index Pczb, determine whether it is necessary to issue a fault type troubleshooting instruction externally; The type troubleshooting module is used to monitor the two-dimensional temperature distribution of the air gap area in the permanent magnet generator by using thermal imaging technology after receiving the fault type troubleshooting instruction, so as to obtain infrared thermal data, and analyze the risk of foreign matter in the air gap area in the permanent magnet generator based on the infrared thermal data, and construct the temperature gradient at each position. ; The type feedback module is used to pre-set the safety threshold Q by Compare and analyze with the safety threshold Q to determine the fault type of the current permanent magnet generator.

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