基于自适应和弦变换旋转蒸发策略的电机轴承未知故障诊断

doi:10.19595/j.cnki.1000-6753.tces.250049

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (29618 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Permanent magnet synchronous motors (PMSMs) are widely used in critical fields, and the operational status of their rolling bearings directly affects the efficiency and reliability of electromechanical systems. However, complex working environments and variable conditions often result in undetected bearing faults. These faults violate the assumptions of traditional data-driven fault diagnosis methods, specifically the requirement for balanced sample sizes across different states and for consistent state categories between the source and target domains, leading to misdiagnosis or missed diagnosis. This study proposes an adaptive chord transformation rotation evaporation strategy (ACTRES) for diagnosing unknown faults in PMSM bearings.
ACTRES comprises two core modules designed to enhance diagnostic accuracy and efficiency. First, a rotational evaporation strategy is introduced to mitigate the catastrophic forgetting of old and new samples during lifelong learning. This strategy simulates the solvent-solution separation mechanism in chemical synthesis: it clusters features of old task data using K-means to obtain local prototypes, generates pseudo-samples by fusing Gaussian noise with learnable embedding vectors, and harmonizes the constraints between evaporation loss and state category loss. This process effectively reconstructs the global distribution of known fault categories, thereby improving diagnostic accuracy. Second, an adaptive chord transformation method is inspired by music theory. Chord transposition, such as shifting between bass, midrange, and treble tones, parallels the mapping of different fault sizes. This method enables affine-invariant transfer of fault features. By employing a chord constructor and a global optimal function, it maps fault patterns across varying sizes. The negative impacts of knowledge, memory, and model expansion on diagnostic speed are eliminated.
Experimental validation was conducted using the case western reserve university (CWRU) public dataset and a self-built PMSM test platform. The CWRU dataset features SKF6205 bearings with fault sizes of 0.007, 0.014, and 0.021 inches. ACTRES achieves an average diagnostic accuracy of over 95.7% for unknown faults. On the self-built platform, tests were carried out under three conditions: constant speed, variable speed, and simultaneous speed-load variation. The constant speed is 1 200 r/min; the variable speeds are 600 r/min, 1 200 r/min, and 1 800 r/min; the simultaneous speed-load variation involves 50% and 100% load changes. Compared with the modified auxiliary classifier generative adversarial network (MACGAN), the theory-guided progressive transfer learning network (TPTLN), and the multi-source information fusion deep self-attention reinforcement learning (MSIF-DSARL), ACTRES demonstrates superior performance.
In conclusion, ACTRES effectively addresses the key challenges in unknown fault diagnosis. Future work will integrate motor structure models to extend the method to unknown-fault diagnosis across different motor types, such as asynchronous and synchronous reluctance motors.

Key words： Unknown fault chord transformation rotary evaporation permanent magnet synchronous motor rolling bearing

Received: 09 January 2025

PACS:

TM307

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Luo Peien
	Yin Zhonggang
	Yuan Dongsheng
	Bai Cong

Cite this article:

Luo Peien,Yin Zhonggang,Yuan Dongsheng等. Unknown Fault Diagnosis of Motor Bearings Based on Adaptive Chord Transformation Rotation Evaporation Strategy[J]. Transactions of China Electrotechnical Society, 2026, 41(2): 499-511.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.250049 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I2/499

[1] 史涔溦, 彭琳, 张振, 等. 电压源激励下表贴式永磁同步电机退磁故障建模与分析[J]. 电工技术学报, 2025, 40(8): 2430-2440.
Shi Cenwei, Peng Lin, Zhang Zhen, et al.Modeling and analysis of demagnetization fault in surface mounted permanent magnet synchronous motors with voltage source excitation[J]. Transactions of China Electrotechnical Society, 2025, 40(8): 2430-2440.
[2] Luo Peien, Yin Zhonggang, Yuan Dongsheng, et al.A novel generative adversarial networks via music theory knowledge for early fault intelligent diagnosis of motor bearings[J]. IEEE Transactions on Industrial Electronics, 2024, 71(8): 9777-9788.
[3] 刘蔚, 李万铨, 王明峤, 等. 复杂工况下的永磁同步电机典型绕组故障在线诊断[J]. 电工技术学报, 2024, 39(6): 1764-1776.
Liu Wei, Li Wanquan, Wang Mingqiao, et al.Online diagnosis of typical winding faults in permanent magnet synchronous motors under complex working conditions[J]. Transactions of China Electrotechnical Society, 2024, 39(6): 1764-1776.
[4] 张辉, 戈宝军, 韩斌, 等. 基于GAF-CapsNet的电机轴承故障诊断方法[J]. 电工技术学报, 2023, 38(10): 2675-2685.
Zhang Hui, Ge Baojun, Han Bin, et al.Fault diagnosis method of motor bearing based on GAF-CapsNet[J]. Transactions of China Electrotechnical Society, 2023, 38(10): 2675-2685.
[5] 李兵, 梁舒奇, 单万宁, 等. 基于改进正余弦算法优化堆叠降噪自动编码器的电机轴承故障诊断[J]. 电工技术学报, 2022, 37(16): 4084-4093.
Li Bing, Liang Shuqi, Shan Wanning, et al.Motor bearing fault diagnosis based on improved sine and cosine algorithm for stacked denoising auto- encoders[J]. Transactions of China Electrotechnical Society, 2022, 37(16): 4084-4093.
[6] Cao Zheng, Dai Jisheng, Xu Weichao, et al.Sparse bayesian learning approach for compound bearing fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2024, 20(2): 1562-1574.
[7] Luo Peien, Yin Zhonggang, Zhang Yanqing, et al.A novel whale optimization algorithm based on music theory knowledge for RUL prediction of motor bearing[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3534211.
[8] 刘航, 张德春, 刘志坚, 等. 改进infoGAN和QPSO-VGG16的小样本条件下电机轴承故障诊断方法[J]. 电机与控制学报, 2025, 29(5): 167-178.
Liu Hang, Zhang Dechun, Liu Zhijian, et al.Fault diagnosis method for motor bearings under small sample conditions using improved infoGAN and QPSO-VGG16[J]. Electric Machines and Control, 2025, 29(5): 167-178.
[9] 李俊卿, 胡晓东, 耿继亚, 等. 基于ACGAN和模型融合的电机轴承故障诊断方法[J]. 电机与控制应用, 2023, 50(2): 91-96.
Li Junqing, Hu Xiaodong, Geng Jiya, et al.Fault diagnosis method of motor bearing based on ACGAN and model fusion[J]. Electric Machines & Control Application, 2023, 50(2): 91-96.
[10] 王攀攀, 李兴宇, 张成, 等. 基于角域重采样和特征强化的电机滚动轴承故障迁移诊断方法[J]. 电工技术学报, 2025, 40(12): 3905-3916.
Wang Panpan, Li Xingyu, Zhang Cheng, et al.Fault transfer diagnosis method for motor rolling bearings based on angular domain resampling and feature enhancement[J]. Transactions of China Electro- technical Society, 2025, 40(12): 3905-3916.
[11] 宋向金, 孙文举, 刘国海, 等. 深度子领域自适应网络电机滚动轴承跨工况故障诊断[J]. 电工技术学报, 2024, 39(1): 182-193.
Song Xiangjin, Sun Wenju, Liu Guohai, et al.Across working conditions fault diagnosis for motor rolling bearing based on deep subdomain adaption network[J]. Transactions of China Electrotechnical Society, 2024, 39(1): 182-193.
[12] Zhang Wei, Li Xiang, Ma Hui, et al.Universal domain adaptation in fault diagnostics with hybrid weighted deep adversarial learning[J]. IEEE Transa- ctions on Industrial Informatics, 2021, 17(12): 7957-7967.
[13] Wang Bin, Wen Long, Li Xinyu, et al.Adaptive class center generalization network: a sparse domain- regressive framework for bearing fault diagnosis under unknown working conditions[J]. IEEE Transa- ctions on Instrumentation and Measurement, 2023, 72: 3516511.
[14] Liu Chang, Zhang Yi, Ding Zhongjun, et al.Active incremental learning for health state assessment of dynamic systems with unknown scenarios[J]. IEEE Transactions on Industrial Informatics, 2023, 19(2): 1863-1873.
[15] Wang Yinjun, Zeng Liling, Wang Liming, et al.An efficient incremental learning of bearing fault imbalanced data set via filter StyleGAN[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3522610.
[16] Yang Aoxue, Lu Chengda, Yu Wanke, et al.Data augmentation considering distribution discrepancy for fault diagnosis of drilling process with limited samples[J]. IEEE Transactions on Industrial Elec- tronics, 2023, 70(11): 11774-11783.
[17] Ren Yifu, Liu Jinhai, Wang Qiannan, et al.HSELL- net: a heterogeneous sample enhancement network with lifelong learning under industrial small samples[J]. IEEE Transactions on Cybernetics, 2023, 53(2): 793-805.
[18] Chen Bojian, Shen Changqing, Wang Dong, et al.A lifelong learning method for gearbox diagnosis with incremental fault types[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3514010.
[19] Zhang Shiqi, Wang Rongjie, Wang Libao, et al.Fault diagnosis for power converters based on incremental learning[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3512813.
[20] Min Qilang, He Juanjuan, Yu Piaoyao, et al.Incremental fault diagnosis method based on metric feature distillation and improved sample memory[J]. IEEE Access, 2023, 11: 46015-46025.
[21] Smith W A, Randall R B.Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64: 100-131.
[22] Li Wei, Zhong Xiang, Shao Haidong, et al.Multi- mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework[J]. Advanced Engineering Infor- matics, 2022, 52: 101552.
[23] Wang Zisheng, Xuan Jianping, Shi Tielin.Multi- source information fusion deep self-attention reinfor- cement learning framework for multi-label compound fault recognition[J]. Mechanism and Machine Theory, 2023, 179: 105090.