An Intelligent Multi-Sensor Information Fusion Fault Diagnosis Method of Three-Phase Motors Based on Game Mapping Learning
Ren Xiangyu1,2, Qin Yong1,2, Wang Biao1,2, Jia Limin1,2, Cheng Xiaoqing1,2
1. State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China; 2. School of Traffic and Transportation Beijing Jiaotong University Beijing 100044 China
Abstract：Mechanical and electrical faults of three-phase motors can be comprehensively recognized by using multi-sensor data. Existing intelligent fault diagnosis methods, however, are short of an explicit learning mechanism to effectively mine key fault information and fuse multi-sensor features, thereby limiting their diagnosis performance. To overcome these problems, this paper proposes an intelligent multi-sensor information fusion fault diagnosis method based on game mapping learning for three-phase motors. By automatically extracting fault features from different sensor data and adaptively fusing them, the proposed method can accurately recognize various faults of three-phase motors. First, multiple parallel self-learning feature mapping networks are used to automatically extract fault features from different input data from multi-sensor sources. Then, a sensor source discriminator is constructed to form a game-playing relationship between it and self-learning feature mapping networks, aiming to refine fault features and make them aggregate by fault categories. Meanwhile, for ensuring the spatial separability of different types of fault features, a sample difference metric loss function is introduced to the optimization objective. Finally, a fault pattern recognizer is employed to fuse multi-sensor features and classify motor faults. Three-phase motor fault simulation experiments are designed and carried out in this paper. The multi-sensor data, including vibration, current and sound signals, are obtained to verify the proposed method. First, the selection of some hyper-parameters is discussed, and some implementation details of the network are determined. Then, the ablation experiments are performed and the experimental results are as follows. (1) The additions of game-playing learning strategy and sample difference metric loss function improve the diagnostic accuracy of the network. (2) The combined effect of game-playing learning strategy and sample difference metric loss function makes the average accuracy of the proposed method over 99%. Next, the comparison between the proposed method and the state-of-the-art methods shows that, the proposed method has the highest accuracy and the best stability in mechanical and electrical fault diagnosis of three-phase motors. Meanwhile, the above results also explain that the lack of an explicit learning mechanism in fusing multi-sensor features will lead to poor diagnosis accuracy. Finally, the aggregation process of the fault features from different sensor data is visualized. The results show that, the game-playing learning strategy and the sample difference metric loss function make the fault features aggregate by fault categories, thus realizing the effective fusion of multi-sensor source information. The following conclusions can be drawn from the experimental analyses: (1) Game-playing learning can guide the network to automatically extract fault features from multi-sensor data and make them aggregate within the class, thus avoiding the influence of irrelevant measurement noise or redundant sensor information, and improving the accuracy and robustness of three-phase motor fault diagnosis results. (2) The sample difference metric loss function makes different types of multi-sensor fault features distributed in clusters and clear boundaries, which is conducive to accurately distinguish various mechanical or electrical faults of three-phase motors. (3) Compared with the existing feature-level fusion diagnosis methods, the proposed method has an explicit learning mechanism in the process of feature extraction and fusion, and accordingly its diagnosis performance is better.
任翔宇, 秦勇, 王彪, 贾利民, 程晓卿. 基于博弈映射学习的多传感源信息融合三相电机智能故障诊断方法[J]. 电工技术学报, 2023, 38(17): 4633-4645.
Ren Xiangyu, Qin Yong, Wang Biao, Jia Limin, Cheng Xiaoqing. An Intelligent Multi-Sensor Information Fusion Fault Diagnosis Method of Three-Phase Motors Based on Game Mapping Learning. Transactions of China Electrotechnical Society, 2023, 38(17): 4633-4645.
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2023, Vol. 38 
