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Open AccessArticle

A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

1
College of Automation, Foshan University, Foshan City 528000, Guangdong Province, China
2
College of Computer Science, Foshan University, Foshan City 528000, Guangdong Province, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1774; https://doi.org/10.3390/s20061774
Received: 2 February 2020 / Revised: 16 March 2020 / Accepted: 19 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)
Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%. View Full-Text
Keywords: stacked auto-encoder; weighting strategy; deep learning; bearing fault diagnosis stacked auto-encoder; weighting strategy; deep learning; bearing fault diagnosis
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He, J.; Ouyang, M.; Yong, C.; Chen, D.; Guo, J.; Zhou, Y. A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning. Sensors 2020, 20, 1774.

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