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A New Fault Diagnosis Method of Bearings Based on Structural Feature Selection
Open AccessArticle

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

by Wentao Mao 1,2,*,†, Di Zhang 1,†, Siyu Tian 1 and Jiamei Tang 1
1
School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
2
Engineering Lab of Intelligence Business & Internet of Things of Henan Province, Xinxiang 453007, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2020, 9(2), 323; https://doi.org/10.3390/electronics9020323
Received: 14 December 2019 / Revised: 2 February 2020 / Accepted: 6 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)
In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate. View Full-Text
Keywords: early fault detection; fault diagnosis; state assessment; transfer learning; deep learning early fault detection; fault diagnosis; state assessment; transfer learning; deep learning
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Mao, W.; Zhang, D.; Tian, S.; Tang, J. Robust Detection of Bearing Early Fault Based on Deep Transfer Learning. Electronics 2020, 9, 323.

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