کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5019364 | 1468210 | 2017 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
System dynamic reliability assessment and failure prognostics
ترجمه فارسی عنوان
ارزیابی قابلیت اطمینان پویا و پیش بینی شکست
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کلمات کلیدی
PDMPRULReliability assessment - ارزیابی قابلیت اطمینانRemaining useful life - باقی مانده زندگی مفیدProbability density function - تابع چگالی احتمالabsolute error - خطای مطلقMonte Carlo Simulation - روش مونت کارلوMC simulation - شبیه سازی MCPiecewise deterministic Markov process - فرایند مارکف قطعی و قطعیPdf - پی دی اف
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی مکانیک
چکیده انگلیسی
Traditionally, equipment reliability assessment is based on failure data from a population of similar equipment, somewhat giving an average description of the reliability performance of an equipment, not capturing the specificity of the individual equipment. Monitored degradation data of the equipment can be used to specify its behavior, rendering dynamic the reliability assessment and the failure prognostics of the equipment, as shown in some recent literature. In this paper, dynamic reliability assessment and failure prognostics with noisy monitored data are developed for a system composed of dependent components. Parallel Monte Carlo simulation and recursive Bayesian method are integrated in the proposed modelling framework to assess the reliability and to predict the Remaining Useful Life (RUL) of the system. The main contribution of the paper is to propose a framework to estimate the degradation state of a system composed of dependent degradation components whose conditions are monitored (even without knowing the initial system degradation state) and to dynamically assess the system risk and RUL. As case study, a subsystem of the residual heat removal system of a nuclear power plant is considered. The results shows the significance of the proposed method for tailored reliability assessment and failure prognostics.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Reliability Engineering & System Safety - Volume 160, April 2017, Pages 21-36
Journal: Reliability Engineering & System Safety - Volume 160, April 2017, Pages 21-36
نویسندگان
Jie Liu, Enrico Zio,