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Open AccessArticle

A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds

by Linjie Li 1, Mian Zhang 2,3 and Kesheng Wang 1,*
1
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechanical System, Tianjin University of Technology, Tianjin 300384, China
3
National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Binshuixidao 391, Xiqing District, Tianjin 300384, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1841; https://doi.org/10.3390/s20071841
Received: 27 January 2020 / Revised: 20 March 2020 / Accepted: 23 March 2020 / Published: 26 March 2020
(This article belongs to the Section Physical Sensors)
Deep learning-based intelligent fault diagnosis methods have attracted increasing attention for their automatic feature extraction ability. However, existing works are usually under the assumption that the training and test dataset share similar distributions, which unfortunately always violates real practice due to the variety of working conditions. In this paper, an end-to-end scheme of joint use of two-direction signals and capsule network (CN) is proposed for fault diagnosis of rolling bearing. With the help of the superior ability of CN in capturing the spatial position information between features, more valuable information can be mined. Aiming to eliminate the influence of different rotational speeds, vertical and horizontal vibration signals are fused as the input to CN, so that invariant features can be extracted automatically from the raw signals. The effectiveness of the proposed method is verified by experimental data of rolling bearing under different rotational speeds and compared with a deep convolutional neural network (DCNN). The results demonstrate that the proposed scheme is able to recognize the fault types of rolling bearing under scenarios of different rotational speeds. View Full-Text
Keywords: fault diagnosis; capsule network; two-direction signals; end-to-end scheme; rolling bearing; different rotational speeds fault diagnosis; capsule network; two-direction signals; end-to-end scheme; rolling bearing; different rotational speeds
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Li, L.; Zhang, M.; Wang, K. A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds. Sensors 2020, 20, 1841.

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