Fault Feature of Gearbox Vibration Signals Based on Morphological Filter Dynamic Convolution Autoencoder

Vibration signals are widely used in fault diagnosis of rotating machinery. Although deep neural networks (DNNs) have been widely used in feature learning of vibration signals, they have not shown good performance in the extraction of impulsive features and noise filtering. In this study, a new DNN,...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 22; no. 23; pp. 22931 - 22942
Main Authors: Yu, Jianbo, Huang, Jiahuan, Liu, Changhui, Xia, Beixin
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Coders
Convolution
Convolution autoencoder
dynamic convolution
Fault diagnosis
Feature extraction
gearbox
Gearboxes
Kernel
Kurtosis
Machine learning
morphological filtering
Morphological filters
Morphology
Noise reduction
Representation learning
Rotating machinery
Sensors
Termites
Vibration
Vibrations
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:Vibration signals are widely used in fault diagnosis of rotating machinery. Although deep neural networks (DNNs) have been widely used in feature learning of vibration signals, they have not shown good performance in the extraction of impulsive features and noise filtering. In this study, a new DNN, morphological filter dynamic convolutional autoencoder (MF-DCAE), is proposed for fault feature extraction from vibration signals. First, a multiscale morphological filter layer embedded with a convolutional autoencoder (CAE) is proposed to extract fault features of vibration signals. Kurtosis is used to fuse the feature information extracted by morphological operators with different scales. Dynamic convolution is used to adjust the weight of each convolution kernel for adaptive feature extraction. Finally, residual learning is used to connect the encoder and decoder to obtain good feature learning of vibration signal. The effectiveness of MF-DCAE is verified on two gearbox testbeds. The results show that MF-DCAE can perform both noise reduction and feature learning on vibration signals in an unsupervised learning manner. The comparison results show that the feature extraction performance of MF-DCAE is better than that of the state-of-the-art DNNs.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3213783