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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Abstract	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.
AbstractList	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.
Author	Song, Xin Cao, Jiawei Yao, Jian Gu, Yi
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Snippet	The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on...
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SubjectTerms	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
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Title	A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals
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