Bearing fault prognostics using Takagi-Sugeno of extended fuzzy with recursive least square algorithms
سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 142
فایل این مقاله در 12 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JCARME-12-1_010
تاریخ نمایه سازی: 8 شهریور 1401
چکیده 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
کلیدواژه ها:
Prognostics ، Rolling element bearing ، Remaining useful life ، Extended Takagi Sugeno ، Fuzzy ، Recursive least squares method
نویسندگان
Henry Omoregbee
Department of Mechanical Engineering, University of Lagos. Akoka. Lagos. Nigeria
Modestus Okwu
Department of Mechanical Engineering, Federal University of Petroleum Resources Effurun, Delta State, Nigeria.
Mabel Olanipekun
Electrical Engineering Department, Tshwane University of Technology, eMalahleni Campus, South Africa
Bright Edward
Department of Mechanical Engineering, Federal University of Petroleum Resources Effurun
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :