Size and Location Diagnosis of Rolling Bearing Faults: An Approach of Kernel Principal Component Analysis and Deep Belief Network

Authors

Heli Wang^{1, 2}, Haifeng Huang^{1, *,} , Sibo Yu¹, Weijie Gu¹

¹School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, P. R. China

²Patent Examination Cooperation Sichuan Center of the Patent Office, China National Intellectual Property Administration, Chengdu, 610213, P. R. China

^*Corresponding author. Email: hhfeng@swjtu.edu.cn

DOI

10.2991/ijcis.d.210518.002How to use a DOI?

Keywords

Rolling bearings; Fault diagnosis; Incipient faults; Kernel principal component analysis; Deep belief network

Abstract

Diagnosing incipient faults of rotating machines is very important for reducing economic losses and avoiding accidents caused by faults. However, diagnoses of locations and sizes of incipient faults are very difficult in a noisy background. In this paper, we propose a fault diagnosis method that combines kernel principal component analysis (KPCA) and deep belief network (DBN) to detect sizes and locations of incipient faults on rolling bearings. Effective information of raw vibration signals processed by KPCA method is used as input signals of the DBN of which weights of the first RBM are initialized by contribution rates of principal components. A DBN with complex structures can be cut into a briefer network by KPCA-DBN model. That model reduces network structure and increases convergence rate. As a result, an average test accuracy by KPCA-DBN can reach 99.1% for identification of 12 labels including incipient faults and the training time is 28s which is half of that by DBN model. The average accuracy of rolling bearing location detection nearly gets to 100% and the average accuracy of fault size detection is above 99%. Compared with SVM, BP, CNN, Deep EMD-PCA (Empirical Mode Decomposition-Principal Component Analysis), CNN-SVM and DBN, it is found that training time can be shortened and detection accuracy can be improved by KPCA-DBN model. The proposed method is beneficial to realize sizes and locations detection of incipient faults online.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Heli Wang
AU  - Haifeng Huang
AU  - Sibo Yu
AU  - Weijie Gu
PY  - 2021
DA  - 2021/05/28
TI  - Size and Location Diagnosis of Rolling Bearing Faults: An Approach of Kernel Principal Component Analysis and Deep Belief Network
JO  - International Journal of Computational Intelligence Systems
SP  - 1672
EP  - 1686
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210518.002
DO  - 10.2991/ijcis.d.210518.002
ID  - Wang2021
ER  -