Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

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...

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Bibliographic Details
Main Authors: Shangjun Ma, Wenkai Liu, Wei Cai, Zhaowei Shang, Geng Liu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Residual convolutional neural networks
depthwise separable convolutions
deep learning
fault diagnosis
wavelet packet transform
Online Access:https://ieeexplore.ieee.org/document/8693794/

Internet

https://ieeexplore.ieee.org/document/8693794/

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