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

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...

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Bibliographic Details
Published in:Chinese journal of mechanical engineering 2017-11, Vol.30 (6), p.1347-1356
Main Authors: Shao, Si-Yu, Sun, Wen-Jun, Yan, Ru-Qiang, Wang, Peng, Gao, Robert X
Format: Article
Language:English
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
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Summary: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.
ISSN:1000-9345
2192-8258
DOI:10.1007/s10033-017-0189-y