1. Bearing Fault Online Identification Based on ANFIS.
- Author
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Truong, Nang Toan, Seo, Tae-Il, and Nguyen, Sy Dzung
- Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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