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  

An Intelligent Multi-Sensor Information Fusion Fault Diagnosis Method of Three-Phase Motors Based on Game Mapping Learning

作　　者：任翔宇 秦勇[1,2] 王彪 贾利民[1,2] 程晓卿[1,2] Ren Xiangyu;Qin Yong;Wang Biao;Jia Limin;Cheng Xiaoqing(State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University,Beijing 100044 China;School of Traffic and Transportation Beijing Jiaotong University,Beijing 100044 China)

机构地区：[1]轨道交通控制与安全国家重点实验室(北京交通大学),北京100044 [2]北京交通大学交通运输学院,北京100044

出　　处：《电工技术学报》2023年第17期4633-4645,共13页Transactions of China Electrotechnical Society

基　　金：北京交通大学人才基金(2022RC030);轨道交通控制与安全国家重点实验室自主研究课题(RCS2022ZQ002);国家自然科学基金(61833002)资助项目。

摘　　要：利用多传感数据能够全面地诊断三相电机的各类机械或电气故障,然而现有的智能诊断方法缺乏有效的关键故障信息挖掘与跨传感源特征聚合学习机制,导致其诊断效果不佳。针对以上问题,该文提出一种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。首先,使用多个并行的自学习特征映射网络从各传感数据中自动地提取故障特征;然后,构建传感源鉴别器并使其与自学习特征映射网络形成博弈关系,精炼故障特征并引导其跨传感源分类聚合;之后,将样本差异度量损失函数引入到优化目标中,以确保各类故障特征之间的空间可分性;最后,利用故障模式识别器进行多传感故障特征融合诊断。实验结果表明,该方法能够基于振动、电流、声等多传感数据准确地诊断三相电机轴承故障、转子故障及电气故障,且诊断性能优于现有方法。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 propo

关 键 词：三相电机 智能故障诊断 多传感源信息融合 对抗学习 信息度量 

分 类 号：TM307.1[电气工程—电机]