A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of th...

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Veröffentlicht in:Wireless communications and mobile computing Jg. 2021; H. 1
Hauptverfasser: Gu, Yi, Cao, Jiawei, Song, Xin, Yao, Jian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Hindawi 2021
John Wiley & Sons, Inc
Schlagworte:
Artificial intelligence
Back propagation
Background noise
Bearings
Breakdowns
Classifiers
Datasets
Deep learning
Fault detection
Fault diagnosis
Fault location
Feature extraction
Machinery
Machinery condition monitoring
Methods
Neural networks
Noise
Noise reduction
Roller bearings
Rotating machinery
Rotating machines
Signal monitoring
Time domain analysis
Vibration monitoring
ISSN:1530-8669, 1530-8677
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Zusammenfassung:The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.
Bibliographie:ObjectType-Article-1
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ISSN:1530-8669
1530-8677
DOI:10.1155/2021/9790053