A Novel Transfer Dictionary Learning Strategy for Rolling Bearing Fault Identification with a Mixed Noise Model

2021 ◽  
pp. 1-1
Author(s):  
Jialing Zhang ◽  
Jimei Wu
Keyword(s):  
Dictionary Learning ◽  
Learning Strategy ◽  
Rolling Bearing ◽  
Noise Model ◽  
Fault Identification ◽  
Bearing Fault ◽  
Mixed Noise

scholarly journals Multi-source fault identification based on combined deep learning

2020 ◽  
Vol 309 ◽  
pp. 03037
Author(s):  
Dongqiu Xing ◽  
Rui Chen ◽  
Lihua Qi ◽  
Jing Zhao ◽  
Yi Wang
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Rolling Bearing ◽  
Fine Tuning ◽  
Fault Identification ◽  
Support Vector ◽  
Industrial Systems ◽  
Classifier Design ◽  
Time Frequency ◽  
Deep Model

This study establishes a multi-source fault identification method based on a combined deep learning strategy to identify a multi-source fault effectively in the fault diagnosis of complex industrial systems. This framework is composed of feature extraction and classifier design. In the first state, the signal is transformed to the time-frequency domain and the time-frequency feature is learned using stacked denoising autoencoders. A learning method that consists of unsupervised pre-learning and supervised fine-tuning is used to train this deep model. In the second state, a model for an ensemble multiple support vector machine classifier is created to recognize fault information. Ten types of rolling bearing signals were adopted in a simulation experiment to validate the effectiveness of the proposed framework. The results demonstrate that the joint model helps to obtain higher recognition accuracy.

Product function correntropy and its application in rolling bearing fault identification

Measurement ◽  
2017 ◽  
Vol 97 ◽  
pp. 88-99 ◽  
Author(s):  
Yunxiao Fu ◽  
Limin Jia ◽  
Yong Qin ◽  
Jie Yang
Keyword(s):  
Rolling Bearing ◽  
Fault Identification ◽  
Bearing Fault ◽  
Product Function

scholarly journals Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1560 ◽  
Author(s):  
Tomasz Ciszewski ◽  
Len Gelman ◽  
Andrew Ball
Keyword(s):  
Data Processing ◽  
Rolling Bearing ◽  
Higher Order ◽  
Spectral Triple ◽  
Fault Identification ◽  
Electromechanical Systems ◽  
Processing Technologies ◽  
Bearing Fault ◽  
Triple Correlation ◽  
Electrical Data

It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology.

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