Reliability modeling for competing failure processes considering degradation rate variation under cumulative shock

Abstract

Most systems experience both random shocks (hard failure) and performance degradation (soft failure) during service span, and the dependence of the two competing failure processes has become a key issue. In this study, a novel dependent competing failure processes (DCFPs) model with a varying degradation rate is proposed. The comprehensive impact of random shocks, especially the effect of cumulative shock, is reasonably considered. Specifically, a shock will cause an abrupt degradation damage, and when the cumulative shock reaches a predefined threshold, the degradation rate will change. An analytical reliability solution is derived under the concept of first hitting time (FHT). Besides, a one-step maximum likelihood estimation method is established by constructing a comprehensive likelihood function. Finally, the reasonability of the closed form reliability solution and the feasibility and effectiveness of the proposed DCFPs modeling methodology are demonstrated by a comparative simulation study.

Reliability analysis method of competitive failure processes considering the coupling effect of multiple shock processes and degradation process

“…The study proposes a degradation model consisting of three primary components: normal degradation $$X( t )$$, degradation resulting from internal shock damage $$S{{( t )}_{in}}$$, and degradation caused by external shock damage $$S{{( t )}_{ex}}$$. Normal degradation $$X( t )$$ represents a continuous, monotonically increasing process that can be effectively characterized using various degradation failure models such as Wiener processes, 35–37 gamma processes, 34 inverse Gaussian processes, 38 and others. Regardless of the specific model employed for normal degradation, it is essential to adhere to the principle of monotonic progression.…”

Wind turbine gear reliability analysis considering dependent competing failure

“…Some studies have pointed out that there is a linear relationship between $${Y}_i$$ and the shock amplitude $${W}_i$$ of the damage shocks, and the cumulative degradation caused by the shocks becomes 49 $$$$\begin{equation}S\left( t \right) = \sum_{i = 1}^{{N}_2\left( t \right)} {{Y}_i} = {\gamma }_\beta \cdot \sum_{i - 1}^{{N}_2\left( t \right)} {{W}_i} \end{equation}$$$$where $${\gamma }_\beta $$ is the proportional factor.…”

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

“…Dong et al 4 investigated reliability models for multi-component systems under stochastic deterioration process and generalized cumulative shocks with different damage effects. Wang et al 5 studied the reliability of a system with degradation rate variation under cumulative shocks. Li et al 6 modeled the reliability of a system with multi-stage degradation subjected to cumulative shocks.…”