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Bearing Fault Online Identification Based on ANFIS


Effectiveness of online bearing status monitoring (OBSM) depends deeply on the online data processing ability and the sensitivity of data features used to recognize the mechanical-system dynamic response change. Focusing on these, we present a novel method of OBSM based on singular spectrum analysis (SSA) and adaptive neuro-fuzzy inference system (ANFIS) with the highlights as follows. A sensitive and stable multi-feature is discovered to better the ability to distill the valuable information in noisy and massive databases (NMDs) and process impulse-noise in them. The SSA-based high-frequency noise removal solution, the ANFIS’ interpolating and identifying capability, and the dual function of the proposed multi-feature are combined in a new algorithm named AfOBSM for building a system of OBSM through two phases, offline and online. The offline is to identify the mechanical-system in the presence of the typical kinds of bearing faults. The ANFIS is trained in this phase using a training dataset. Meanwhile, the online is to estimate online the real status of the bearing(s) based on the trained ANFIS and a monitoring dataset. Surveys from an experimental-system were performed. The obtained results showed the positive effects of the AfOBSM.

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Correspondence to Sy Dzung Nguyen.

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Recommended by Associate Editor Xiao-Heng Chang under the direction of Editor Guang-Hong Yang.

The authors are very grateful to the reviewers for their useful comments and suggestions. This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.01-2019.328.

Nang Toan Truong received his master’s degree in automation engineering from Ho Chi Minh City University of Transport (UT-HCMC) in 2011. Currently, he is a Ph.D. student at Institute for Computational Science (INCOS), Ton Duc Thang University, TDTU. Also, he is a lecturer at Industrial University of Ho Chi Minh City (IUH), Vietnam. His research interests include artificial intelligence and its applications to nonlinear control, data mining, system identification, and structures’ health managing.

Tae-Il Seo received his Ph.D. Degree in mechanical engineering from Ecole Centrale de Nantes, France, in 1998. From 1998 to 1999, he was a postdoctoral research fellow in the Department of Mechanical Engineering in Inha University, Incheon, Korea. From 1999 to 2001, he was a research fellow in the Department of mechanical corporation Laboratory, Inha University of Korea. From 2001 to 2003, he was a researcher in the Department of Precision Mold Lab, KITECH (Korea Institute of Industrial Technology), Korea. Currently, he is a Professor in the Department of Mechanical Engineering at Incheon National University, Korea. Dr. Seo’s current research interests include micro end-milling, intelligent manufacturing system, CAD/CAD systems, etc.

Sy Dzung Nguyen received his M.E. degree in manufacturing engineering from Ho Chi Minh City University of Technology (HCMUT) — VNU in 2001 and a Ph.D. degree in applied mechanics in 2011 from HCMUT. He is an Assoc. Professor at Institute for Computational Science (INCOS), Ton Duc Thang University, Ho Chi Minh City, Vietnam. He is currently the Head of Division of Computational Mechatronics (DCME), INCOS. He was a postdoctoral fellow at Inha University, Korea in 2011–2013, at Incheon National University, Korea in 2015–2016. His research interests include artificial intelligence and its applications to nonlinear adaptive control, system identification and managing structure damage. Dr. Nguyen has been the main author of plenty of ISI papers in these fields.

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Truong, N.T., Seo, TI. & Nguyen, S.D. Bearing Fault Online Identification Based on ANFIS. Int. J. Control Autom. Syst. 19, 1703–1714 (2021).

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  • ANFIS-based fault diagnosis
  • bearing fault diagnosis
  • machine health monitoring
  • online damge identification