A Convolutional Neural Network for Rotating Machinery Fault Diagnosis Based on Fast Independent Component Analysis

Guangxin Li, Yong Chen, Wenqing Wang, Rui Liu, Fengshou Gu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Efficient and accurate fault diagnosis is essential to ensure the safe operation of rotating machinery. An intelligent fault diagnosis based convolutional neural networks (CNN) and fast independent component analysis (FICA), is proposed to improve the classification and recognition ability of rolling bearings and. Firstly, the intrinsic mode function (IMF) components of the raw vibration signals are obtained by empirical mode decomposition (EMD) preprocessing method. Secondly, FICA method is used to extract additional feature components of IMFs and ICA components are obtained. Finally, a shallow CNN model is constructed to learn feature and diagnosis from different signal-to-noise ratio (SNR) and different working load of rolling bearings. The proposed method can conduct high accuracy of fault recognition and classification, which is more efficient than the IMFs feature. To verify this method, Back propagation neural networks (BPNN), stacked autoencoder (SAE), multilayer perceptron (MLP) are used as comparative models. The results demonstrate that the proposed method can achieve higher accuracy than other comparative methods.

Original languageEnglish
Title of host publicationProceedings of IncoME-V & CEPE Net-2020
Subtitle of host publicationCondition Monitoring, Plant Maintenance and Reliability
EditorsDong Zhen, Dong Wang, Tianyang Wang, Hongjun Wang, Baoshan Huang, Jyoti K. Sinha, Andrew David Ball
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Pages700-708
Number of pages9
Volume105
Edition1st
ISBN (Electronic)9783030757939
ISBN (Print)9783030757922
DOIs
Publication statusPublished - 16 May 2021
Event5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network - Zhuhai, China
Duration: 23 Oct 202025 Oct 2020
Conference number: 5
https://link.springer.com/book/10.1007/978-3-030-75793-9#about

Publication series

NameMechanisms and Machine Science
Volume105
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network
Abbreviated titleIncoME-V and CEPE Net-2020
CountryChina
CityZhuhai
Period23/10/2025/10/20
Internet address

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