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

• Published in: Chinese journal of mechanical engineering Vol. 30; no. 6; pp. 1347 - 1356
• Main Authors: Shao, Si-Yu, Sun, Wen-Jun, Yan, Ru-Qiang, Wang, Peng, Gao, Robert X
• Format: Journal Article
• Language: English
• Published: Beijing Chinese Mechanical Engineering Society 01.11.2017; Springer Nature B.V; Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland 44106, USA%Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland 44106, USA; School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China%School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
• Edition: English ed.
• Subjects: Algorithms; Belief networks; Classification; Computer simulation; Deep learning; Diagnostic systems; Electrical Machines and Networks; Electronics and Microelectronics; Engineering; Engineering Thermodynamics; Fault diagnosis; Feature extraction; Feature recognition; Frequency distribution; Heat and Mass Transfer; Induction motors; Instrumentation; Machine learning; Machines; Manufacturing; Mechanical Engineering; Original Article; Power Electronics; Processes; Theoretical and Applied Mechanics; Fault diagnosis; Deep learning; Deep belief network; RBM; Classification
• ISSN: 1000-9345; 2192-8258
• DOI: 10.1007/s10033-017-0189-y

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.