期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 14, 期 12, 页码 5245-5255出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2817201
关键词
Extreme learning machines (ELM); fault diagnosis; local mean decomposition (LMD); single and simultaneous faults; multistage gearbox
类别
资金
- Science and Technology Development Fund of Macao SAR (FDCT) under MoST-FDCT Joint Grant [015/2015/AMJ]
- FDCT Grant [121/2016/A3]
- University of Macau [MYRG2016-00160-FST]
- Ministry of Science and Technology of China [2016YFE0121700]
High precision fault diagnosis is vital for widely used multistage gearbox systems. Intelligent monitoring is difficult due to the fuzzy boundaries and a variety of unseen single or simultaneous faults of such complex machinery. To solve this problem, local mean decomposition is applied to extract features effectively from the original nonstationary and nonlinear vibration signals. By exploiting the diverse functionalities of extreme learning machines (ELM) in both regression and classification, a novel dual-ELM network is proposed, in which one ELM is employed to count the number of faults and the other is used to identify the specific single- or simultaneous-fault scenarios. The proposed dual-ELM-based multilabel classifier does not rely on an empirically specified threshold. Thus, it is more self-adaptive than the existing probabilistic-based classifiers. In addition, by inheriting the advantages of the original ELM, the dual-ELMs do not require iterative fine-tuning of parameters. Finally, the training speed of the dual-ELMs is much faster than other combinations of the existing classifiers. Experimental results under various loading conditions show that the proposed dual-ELM-based fault diagnostic framework is versatile at detecting single and simultaneous faults accurately and quickly.
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