NDLI: Method of state identification of rolling bearings based on deep domain adaptation under varying loads

Content Provider	IET Digital Library
Author	Kang, Shouqiang Chen, Weiwei Wang, Yujing Na, Xiaodong Wang, Qingyan Mikulovich, Vladimir Ivanovich
Abstract	Large amounts of labelled vibration data of rolling bearings are difficult to acquire in full during operating conditions under varying loads. Moreover, a large divergence in data distribution exists between source and target domains for the same state. A multiple-state identification method for rolling bearings under varying loads is proposed. The deep domain adaptation method integrates the convolutional and pooling theory with the deep belief network (DBN) that enables the construction of a convolutional Gaussian–Bernoulli DBN, which is used to extract the deep generalised features from the frequency-domain amplitudes of the rolling bearings. The weighted mixed kernel is then used instead of the single kernel to improve the joint distribution adaptation, which is used to process the features of both the labelled source domain and the unlabelled target domain for domain adaptation, and reduce the distribution divergence. Finally, the k-nearest neighbour algorithm is used for identification. Experimental results show that the proposed method can make full use of unlabelled data, mine the deep features of vibration signals, and reduce the divergence between data of the same state. In resolving the multiple-state identification of rolling bearings under varying loads, a higher accuracy is attained in the identification.
Starting Page	303
Ending Page	313
Page Count	11
ISSN	17518822
Volume Number	14
e-ISSN	17518830
Issue Number	Issue 3, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2019.0043
Journal	IET Science, Measurement & Technology
Publisher Date	2019-10-23
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Belief Network Combinatorial Mathematics Convolution Convolutional Gaussian–Bernoulli DBN Data Distribution Data Handling Technique Deep Belief Network Deep Domain Adaptation Method Deep Generalised Feature Extraction Fault Diagnosis Feature Extraction Frequency-domain Amplitudes Gaussian Processes Industrial Application of IT Joint Distribution Adaptation Knowledge Engineering Technique Labelled Source Domain Labelled Vibration Data Mechanical Component Mechanical Engineering Multiple-state Identification Method Production Engineering Computing Rolling Bearings Statistics Unlabelled Target Domain Vibrations Vibrations And Shock Wave
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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