4.6 Article

A High-Accuracy Least-Time-Domain Mixture Features Machine-Fault Diagnosis Based on Wireless Sensor Network

期刊

IEEE SYSTEMS JOURNAL
卷 14, 期 3, 页码 4101-4109

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2020.2993337

关键词

Feature extraction; Vibrations; Wireless sensor networks; Fault diagnosis; Indexes; Wireless communication; Time-domain analysis; BP neural network (BPNN); dimensional and dimensionless features; fault diagnosis; rolling bearing; wireless sensor networks (WSNs); WMESN

资金

  1. Nanjing University of Science and Technology [AE89991/032]
  2. Fundamental Research Funds for the Central Universities [309181A8804, 30919011263]
  3. Natural Science Foundation of Jiangsu Province China [BK20190464]
  4. National Natural Science Foundation of China [61527803, 61960206010]
  5. EPSRC [EP/J012343/1] Funding Source: UKRI

向作者/读者索取更多资源

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.

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