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Journal of Computational and Applied Research in Mechanical Engineering - JCARME، جلد ۱۲، شماره ۱، صفحات ۱۲۱-۱۳۲
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عنوان انگلیسی Bearing fault prognostics using Takagi-Sugeno of extended fuzzy with recursive least square algorithms
چکیده انگلیسی مقاله This paper presents the detection of fault prognostics in bearings with the application of extended Takagi-Sugeno fuzzy recursive least square algorithms (exTSFRLSA). The nonlinear system is decomposed into a multi-model structure, consisting of linear models that are not inherently independent, due to the fuzzy regions defined in exTSFRLSA. The exTSFRLSA was developed to tune, adjust and adapt the parameters involved in the propagation model, as it tends to update itself with the availability of new data. This method is suitable for the online identification of systems because of its unsupervised learning pattern which dwells on a mechanism cantered on rule-based evolution. Scenarios considered for the rule-based modification and upgrade are quite diverse, thereby ensuring effective comparison of measured and predicted defect size. An estimation of the remaining useful life was determined successfully with the proposed method, showing that the system performance health indicator reflects bearing degradation, and it was concluded that exTSFRLSA can be used for fault prediction of bearing where rolling element are involved, especially while its operation is associated with fluctuating speed and load conditions
کلیدواژه‌های انگلیسی مقاله Prognostics, Rolling element bearing, Remaining useful life, Extended Takagi Sugeno, Fuzzy, Recursive least squares method
نویسندگان مقاله Henry Ogbemudia Omoregbee |
Department of Mechanical Engineering, University of Lagos. Akoka. Lagos. Nigeria

Modestus O. Okwu |
Department of Mechanical Engineering, Federal University of Petroleum Resources Effurun, Delta State, Nigeria.

Mabel U. Olanipekun |
Electrical Engineering Department, Tshwane University of Technology, eMalahleni Campus, South Africa

Bright A Edward |
Department of Mechanical Engineering, Federal University of Petroleum Resources Effurun

نشانی اینترنتی https://jcarme.sru.ac.ir/article_1623_eff1c8bda42fc7fc29268a5d90a97e0d.pdf
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