Chinese Journal of Mechanical Engineering >

2017 , Vol. 30 >Issue 6: 1347 - 1356

DOI: https://doi.org/10.1007/s10033-017-0189-y

ORIGINAL ARTICLE

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

  • Si-Yu Shao ,
  • Wen-Jun Sun ,
  • Ru-Qiang Yan ,
  • Peng Wang ,
  • Robert X Gao
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  • 1 School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;
    2 Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland 44106, USA

Received date: 2016-12-30

  Revised date: 2017-09-29

  Online published: 2019-07-16

Supported by

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

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Abstract

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 working 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-bylayer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, 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.

Key words： Fault diagnosis; Deep learning; Deep belief network; RBM; Classification

Cite this article

Si-Yu Shao , Wen-Jun Sun , Ru-Qiang Yan , Peng Wang , Robert X Gao . A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing[J]. Chinese Journal of Mechanical Engineering, 2017 , 30(6) : 1347 -1356 . DOI: 10.1007/s10033-017-0189-y

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