A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing  

作　　者：Si-Yu Shao Wen-Jun Sun Ru-Qiang Yan Peng Wang Robert X Gao 

机构地区：[1]School of Instrument Science and Engineering, Southeast University [2]Department of Mechanical and Aerospace Engineering, Case Western Reserve University

出　　处：《Chinese Journal of Mechanical Engineering》2017年第6期1347-1356,共10页中国机械工程学报（英文版）

基　　金：Supported by National Natural Science Foundation of China(Grant No.51575102);Fundamental Research Funds for the Central Universities of China;Jiangsu Provincial Research Innovation Program for College Graduates of China(Grant No.KYLX16_0191)

摘　　要：Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks （DBN） is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing work- ing status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine （RBM）, and is trained using layer-by- layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierar- chical representations, which are suitable for fault classi- fication, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks （DBN） is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing work- ing status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine （RBM）, and is trained using layer-by- layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierar- chical representations, which are suitable for fault classi- fication, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

关 键 词：Fault diagnosis Deep learning Deep beliefnetwork. RBM CLASSIFICATION 

分 类 号：TM346[电气工程—电机]