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Lookup NU author(s): Professor Gui Yun TianORCiD
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© 2007-2012 IEEE.Fault diagnosis of rolling bearing plays a vital role in identifying incipient failures and ensuring the reliable operation of the mechanical system. To improve the performance of the whole machine-fault-diagnosis system and meet the requirements of low cost, low consumption, high-reliability in industrial wireless sensor networks (IWSNs), a high-accuracy least-time-domain features fault diagnosis algorithm based on the BP neural network (BPNN) for IWSNs is proposed in this article. First, the hardware of wireless multifeatures extraction sensor node is designed, which performs local-processing features extraction of four-dimensional parameters and five dimensionless features of the vibration signal. Then, the bearing-fault classification based on mentioned characteristics is investigated in the proposed BPNN with different hidden layer nodes. Furthermore, we make the comparisons of bearing-fault classification accuracy in terms of varying number of dimensional features, dimensionless features, and the combination features, searching a least-time-domain mixture features selection strategy for ensuring high-fault classification accuracy and proving the effectiveness and feasibility of the proposed method by experiments on drivetrain diagnostics simulator system. This article is conducted to provide new insights into how to select the least time-domain features for high-accuracy fault diagnosis and further giving references to more IWSNs scenarios.
Author(s): Du C, Gao S, Jia N, Kong D, Jiang J, Tian G, Su Y, Wang Q, Li C
Publication type: Article
Publication status: Published
Journal: IEEE Systems Journal
Year: 2020
Volume: 14
Issue: 3
Pages: 4101-4109
Print publication date: 01/09/2020
Online publication date: 26/05/2020
Acceptance date: 02/04/2016
ISSN (print): 1932-8184
ISSN (electronic): 1937-9234
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/JSYST.2020.2993337
DOI: 10.1109/JSYST.2020.2993337
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