دانلود مقاله ISI انگلیسی شماره 47128
ترجمه فارسی عنوان مقاله

بهینه سازی اطمینان محور طرح تعمیر و نگهداری پیش بینانه برای سیستم ناوبری اینرسیایی

عنوان انگلیسی
Optimization of reliability centered predictive maintenance scheme for inertial navigation system
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
47128 2015 10 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Reliability Engineering & System Safety, Volume 140, August 2015, Pages 208–217

ترجمه کلمات کلیدی
تعمیر و نگهداری پیشگویانه - سیستم ناوبری ساکن - قابلیت اطمینان سیستم - باقی مانده عمر مفید
کلمات کلیدی انگلیسی
Predictive maintenance (PdM); Inertial Navigation System (INS); System reliability; GO Methodology; Remaining Useful Life (RUL)
پیش نمایش مقاله
پیش نمایش مقاله  بهینه سازی اطمینان محور طرح تعمیر و نگهداری پیش بینانه برای سیستم ناوبری اینرسیایی

چکیده انگلیسی

The goal of this study is to propose a reliability centered predictive maintenance scheme for a complex structure Inertial Navigation System (INS) with several redundant components. GO Methodology is applied to build the INS reliability analysis model—GO chart. Components Remaining Useful Life (RUL) and system reliability are updated dynamically based on the combination of components lifetime distribution function, stress samples, and the system GO chart. Considering the redundant design in INS, maintenance time is based not only on components RUL, but also (and mainly) on the timing of when system reliability fails to meet the set threshold. The definition of components maintenance priority balances three factors: components importance to system, risk degree, and detection difficulty. Maintenance Priority Number (MPN) is introduced, which may provide quantitative maintenance priority results for all components. A maintenance unit time cost model is built based on components MPN, components RUL predictive model and maintenance intervals for the optimization of maintenance scope. The proposed scheme can be applied to serve as the reference for INS maintenance. Finally, three numerical examples prove the proposed predictive maintenance scheme is feasible and effective.