دانلود مقاله ISI انگلیسی شماره 106655
ترجمه فارسی عنوان مقاله

یک فرایند تخریب وابسته به دولت و مشکلات مربوط به شناسایی اشتباهات مربوط به آن

عنوان انگلیسی
A new state-dependent degradation process and related model misidentification problems
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
106655 2018 28 صفحه PDF
منبع

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

Journal : European Journal of Operational Research, Volume 267, Issue 3, 16 June 2018, Pages 1027-1038

ترجمه کلمات کلیدی
فرآیندهای تصادفی، شناسایی نادرست مدل، فرآیند تبدیل بتا، فرایند گاما، نگهداری نامناسب،
کلمات کلیدی انگلیسی
Stochastic processes; Model misidentification; Transformed Beta process; Gamma process; Imperfect maintenance;
پیش نمایش مقاله
پیش نمایش مقاله  یک فرایند تخریب وابسته به دولت و مشکلات مربوط به شناسایی اشتباهات مربوط به آن

چکیده انگلیسی

The Gamma process with random scale parameter is often used to describe independent increments degradation phenomena in the presence of random effects. In this paper, a new Markovian degradation process, the Transformed Beta process, is proposed, where the degradation growth depends on both the current age and degradation level. It is also shown that the Transformed Beta process shares the same stochastic properties of the Gamma process whose scale parameter is Gamma-distributed, and that the likelihood functions relative to a given set of degradation data under these two models coincide. Due to this circumstance, it is not possible to choose between these two models on the basis of degradation data alone, even though they rely on quite different assumptions. On the other hand, although these models are stochastically equivalent, there are situations in which selecting the wrong model can produce real practical consequences. In fact, it is shown that, if the considered degrading unit is subjected to a so-called “virtual age imperfect maintenance”, where the current degradation level is assumed to be lowered through an age reduction maintenance model, then these two alternative models provide different condition-based distributions of the degradation growth. Hence, in this latter case, a model misidentification can produce wrong predictions of the degradation growth and wrong estimates of the residual reliability. The discussed issue is illustrated via a numerical application based on a real set of degradation data.