Paper Info
Title
Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions.
Abstract
This paper proposes an efficient and noise-insensitive end-to-end lightweight deep learning method. The method synthesizes the characteristics of a frequency domain transform and a deep convolutional neural network. The former can extract multiscale information in vibration signal processing and the latter has a good classification performance, data-driven, and high transfer-learning ability. A vibration signal is decomposed into a pyramidal wavelet packet, and each sub-band coefficient is used as an input of a channel in the deep network. A deep residual convolutional network based on a separable convolution and concatenated rectified linear unit (CReLU) lightweight convolution technology is used for fault diagnosis. The proposed algorithm is compared with related deep learning algorithms using two bearing datasets produced by Case Western Reserve University (CWRU) and the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. Compared with the existing algorithms, the experimental results show that the comprehensive performance of the algorithm proposed in this paper is "small, light, and fast," and satisfactory diagnostic results are obtained in the fault diagnosis of rotating machinery.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2912072
IEEE ACCESS
Keywords
Field
DocType
Residual convolutional neural networks,depthwise separable convolutions,deep learning,fault diagnosis,wavelet packet transform
Residual,Convolution,Computer science,Separable space,Computational science,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Shangjun Ma120.68
Wenkai Liu220.68
Wei Cai317539.84
Zhaowei Shang435821.18
Geng Liu563.16