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Bearing Fault Online Identification Based on ANFIS


Nang Toan Truong, Tae-Il Seo, Sy Dzung Nguyen

Source title: 
International Journal of Control, Automation and Systems, 19: 1703-1714, 2021 (ISI)
Academic year of acceptance: 

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.