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| Eccentricity Fault Diagnosis of Permanent Magnet Linear Motor Based on Spiral Curve Transformation of Stray Magnetic Field Signal and ResNeXt-18 |
| Qian Long1, Wu Xianhong1, Song Juncai2, Lu Siliang1, Wang Xiaoxian3 |
1. School of Electrical Engineering and Automation Anhui University Hefei 230601 China;
2. School of Internet Anhui University Hefei 230039 China;
3. School of Electronic and Information Engineering Anhui University Hefei 230601 China |
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Abstract Permanent magnet synchronous linear motor (PMSLM) has the advantages of high transmission efficiency, high thrust quality and high positioning accuracy. It is widely used in industrial scenarios with linear direct drive such as high-precision machine tools, parallel robots, etc. In actual industrial applications, PMSLM may produce eccentricity faults due to complex operating conditions, mechanical assembly errors and other factors. The traditional eccentricity fault diagnosis method based on electrical signal is very easy to be affected by environmental noise, operating conditions and other factors, and it is still not enough to directly reflect the state of the motor eccentricity fault. In recent years, some fault diagnosis methods for motor eccentricity based on magnetic density signal which can directly reflect the state of the motor eccentricity fault have been proposed, but most of them exist some drawbacks, such as cumbersome sensor installation steps, insufficient sensor sensitivity and incomplete fault information feature extraction.
To solve the above problems, this paper proposes eccentricity fault diagnosis of permanent magnet linear motor based on three-dimensional spiral curve transformation of stray magnetic field signal and ResNeXt-18. Firstly, finite element simulation models under healthy state and eccentricity fault state of PMSLM are established to obtain the external stray magnetic density signal of PMSLM. Secondly, the eccentricity fault feature enhancement signal processing method based on three-dimensional spiral curve transformation (TDSCT) is applied to transform the external stray magnetic density signal into two-dimensional eccentricity fault feature imagein order to realize fault feature enhancement display. Thirdly, the dataset is input into the deep learning ResNeXt-18 classification framework, through which various fault signal characteristics are obtained and accurate fault diagnosis is achieved. In simulation experiments, the validation accuracy of ResNeXt-18 improves 1.3%, 1.3%, 1.6% and 3.8% compared with Xception, GoogLeNet, ResNet-18 and CNN, respectively. Under Gaussian noise of 40 dB, the validation accuracy of comparison model is reduced by 1.4%, 2.3%, 5.3% and 2.9%, respectively, and that decreased by 0.6%, 1.4%, 2.0% and 1.4% respectively under Gaussian noise of 30 dB. The ResNeXt-18 decreases by 0.5% under Gaussian noise of both 30 dB and 40 dB, which show better robustness compared to other models.
This work establishes PMSLM prototype experimental platform with different eccentricity faults to conduct verification experiments, and the excellent performances of this proposed method is verified. The conclusions of this work are as follows: (1) Using TMR sensor and integrated design with rotor, the measurement of the external stray magnetic density of PMSLM effectively solves the shortcomings of traditional signals in the motor eccentricity fault diagnosis, and build a foundation for the non-invasive eccentricity fault diagnosis of PMSLM. (2) An innovative three-dimensional spiral curve transformation with multi-view projection fusion method is proposed for PMSLM eccentricity fault signal processing. One-dimensional external stray magnetic density signal is converted into 3D spiral curve, and the enhanced vision features of eccentricity fault is realized through multi-view projection fusion. (3) A new classification model of ResNeXt is introduced, which has the advantages of simple structure, high precision and easy model transplantation, and effectively solves the shortcomings of traditional ResNet deep learning network, which is weak in representation ability and easy to overfit. This method has higher classification accuracy and robustness.
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Received: 30 June 2023
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2024, Vol. 39 
