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Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

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Bibliographic Details
Title: Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder
Authors: Juying Dai, Jian Tang, Faming Shao, Shuzhan Huang, Yangyang Wang
Source: Applied Sciences, Vol 9, Iss 13, p 2743 (2019)
Publisher Information: MDPI AG
Publication Year: 2019
Collection: Directory of Open Access Journals: DOAJ Articles
Subject Terms: IMFs, multiscale permutation entropy, stacked sparse denoising autoencoder, fault diagnosis, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Description: Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.
Document Type: article in journal/newspaper
Language: English
Relation: https://www.mdpi.com/2076-3417/9/13/2743; https://doaj.org/toc/2076-3417; https://doaj.org/article/513f8bf811eb491dab82fad9ce1ea4c2
DOI: 10.3390/app9132743
Availability: https://doi.org/10.3390/app9132743
https://doaj.org/article/513f8bf811eb491dab82fad9ce1ea4c2
Accession Number: edsbas.C8C3F3C
Database: BASE
Description
Abstract:Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.
DOI:10.3390/app9132743