Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, C...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2024-10, Vol.19 (10), p.e0307672
Main Authors: Qiu, Shaoming, Liu, Liangyu, Wang, Yan, Huang, Xinchen, E, Bicong, Ye, Jingfeng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Background noise
Biology and Life Sciences
Casualties
Computer and Information Sciences
Deep learning
Diagnostic systems
Effectiveness
Engineering and Technology
Equipment Failure
Equipment Failure Analysis
Fault diagnosis
Graphs
Health aspects
Humans
Industrial production
Information processing
Load resistance
Machine learning
Mechanical equipment
Methods
Neural networks
Neural Networks, Computer
Noise reduction
Noise threshold
Physical Sciences
Property and casualty insurance
Research and Analysis Methods
Short-term memory
Signal processing
Vibration
Wavelet transforms
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0307672