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| 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 |
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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.
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Received: 30 May 2022
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[1] Akay A, Lefley P.Research on torque ripple under healthy and open-circuit fault-tolerant conditions in a PM multiphase machine[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(4): 349-359.
[2] 卢锦玲, 张祥国, 张伟, 等. 基于改进辅助分类生成对抗网络的风机主轴承故障诊断[J]. 电力系统自动化, 2021, 45(7): 148-154.
Lu Jinling, Zhang Xiangguo, Zhang Wei, et al.Fault diagnosis of main bearing of wind turbine based on improved auxiliary classifier generative adversarial network[J]. Automation of Electric Power Systems, 2021, 45(7): 148-154.
[3] He Jiangbiao, Yang Qichen, Wang Zheng.On-line fault diagnosis and fault-tolerant operation of modular multilevel converters-a comprehensive review[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(4): 360-372.
[4] 雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104.
Lei Yaguo, Jia Feng, Kong Detong, et al.Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering, 2018, 54(5): 94-104.
[5] Wang Zhenya, Yao Ligang, Cai Yongwu.Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine[J]. Measurement, 2020, 156: 107574.
[6] Goyal D, Choudhary A, Pabla B S, et al.Support vector machines based non-contact fault diagnosis system for bearings[J]. Journal of Intelligent Manufacturing, 2020, 31(5): 1275-1289.
[7] 夏田, 詹瑶, 郭建斌. 基于小波包和梯度提升决策树的轴承故障诊断[J]. 陕西科技大学学报, 2020, 38(5): 144-149.
Xia Tian, Zhan Yao, Guo Jianbin.Bearing fault diagnosis based on Wavelet Packet and Gradient Boosting Decision Tree[J]. Journal of Shaanxi University of Science & Technology, 2020, 38(5): 144-149.
[8] Li Zefang, Fang Huajing, Huang Ming, et al.Data-driven bearing fault identification using improved hidden Markov model and self-organizing map[J]. Computers & Industrial Engineering, 2018, 116: 37-46.
[9] 史丽萍, 王攀攀, 胡泳军, 等. 基于骨干微粒群算法和支持向量机的电机转子断条故障诊断[J]. 电工技术学报, 2014, 29(1): 147-155.
Shi Liping, Wang Panpan, Hu Yongjun, et al.Broken rotor bar fault diagnosis of induction motors based on bare-bone particle swarm optimization and support vector machine[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 147-155.
[10] Sun Meidi, Wang Hui, Liu Ping, et al.Stack autoencoder transfer learning algorithm for bearing fault diagnosis based on class separation and domain fusion[J]. IEEE Transactions on Industrial Electronics, 2022, 69(3): 3047-3058.
[11] Niu Guangxing, Wang Xuan, Golda M, et al.An optimized adaptive PReLU-DBN for rolling element bearing fault diagnosis[J]. Neurocomputing, 2021, 445: 26-34.
[12] Shao Haidong, Xia Min, Han Guangjie, et al.Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3488-3496.
[13] 田代宗, 孙宇光, 王善铭, 等. 多相整流永磁同步发电机绕组内部相间短路的故障分析[J]. 电工技术学报, 2020, 35(6): 1262-1271.
Tian Daizong, Sun Yuguang, Wang Shanming, et al.Analysis of stator internal phase-to-phase short-circuit in the multiphase permanent magnet synchronous generator with rectifier load system[J]. Transactions of China Electrotechnical Society, 2020, 35(6): 1262-1271.
[14] 许爱德, 任萍, 陈加贵, 等. 基于电感特殊位置点的开关磁阻电机转子位置检测及误差补偿[J]. 电工技术学报, 2020, 35(8): 1613-1623.
Xu Aide, Ren Ping, Chen Jiagui, et al.Rotor position detection and error compensation of switched reluctance motor based on special inductance position[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1613-1623.
[15] 孙曙光, 李勤, 杜太行, 等. 基于一维卷积神经网络的低压万能式断路器附件故障诊断[J]. 电工技术学报, 2020, 35(12): 2562-2573.
Sun Shuguang, Li Qin, Du Taihang, et al.Fault diagnosis of accessories for the low voltage conventional circuit breaker based on one-dimensional convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(12): 2562-2573.
[16] 李睿彧, 刘飞, 梁霖, 等. 基于参数优化变分模态分解的交流变频电机转子断条故障识别方法[J]. 电工技术学报, 2021, 36(18): 3922-3933.
Li Ruiyu, Liu Fei, Liang Lin, et al.Fault identification of broken rotor bars for the variable frequency AC motor based on parameter optimized variational mode decomposition[J]. Transactions of China Electrotechnical Society, 2021, 36(18): 3922-3933.
[17] 杨明, 黄旭, 任博阳, 等. 基于转速信号的电机首次安装角度不对中故障检测[J]. 电工技术学报, 2021, 36(15): 3191-3199.
Yang Ming, Huang Xu, Ren Boyang, et al.Angle misalignment fault detection method of motor’s first installation based on speed signal[J]. Transactions of China Electrotechnical Society, 2021, 36(15): 3191-3199.
[18] 王栋悦, 谷怀广, 魏书荣, 等. 基于机电信号融合的DFIG定子绕组匝间短路故障诊断[J]. 电力系统自动化, 2020, 44(9): 171-178.
Wang Dongyue, Gu Huaiguang, Wei Shurong, et al.Diagnosis of inter-turn short-circuit fault in stator windings of DFIG based on mechanical and electrical signal fusion[J]. Automation of Electric Power Systems, 2020, 44(9): 171-178.
[19] Wang Jinjiang, Fu Peilun, Zhang Laibin, et al.Multilevel information fusion for induction motor fault diagnosis[J]. IEEE/ASME Transactions on Mechatronics, 2019, 24(5): 2139-2150.
[20] Zhang Tianfan, Li Zhe, Deng Zhenghong, et al.Hybrid data fusion DBN for intelligent fault diagnosis of vehicle reducers[J]. Sensors, 2019, 19(11): 2504.
[21] Hoang D T, Kang H J.A motor current signal-based bearing fault diagnosis using deep learning and information fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 3325-3333.
[22] Jian Xianzhong, Li Wenlong, Guo Xuguang, et al.Fault diagnosis of motor bearings based on a one-dimensional fusion neural network[J]. Sensors, 2019, 19(1): 122.
[23] Zhao Xiaoli, Jia Minping, Ding Peng, et al.Intelligent fault diagnosis of multichannel motor-rotor system based on multimanifold deep extreme learning machine[J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(5): 2177-2187.
[24] Xu Xingwei, Tao Zhengrui, Ming Weiwei, et al.Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion[J]. Measurement, 2020, 165: 108086.
[25] Azamfar M, Singh J, Bravo-Imaz I, et al.Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis[J]. Mechanical Systems and Signal Processing, 2020, 144: 106861. |
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2023, Vol. 38 
