Degradation analysis on trend gamma process

Abstract

Manufactures always face a big challenge to obtain the sufficient reliability information for high-quality products when only a relatively short time period is available for an internal life testing. Fortunately, if QCs exists, whose degradation over time can be related to reliability, an approach is suggested to collect sufficient degradation data for estimating the product's lifetime distribution more accurately. In numerical applications, the gamma process (GP) is commonly used when the degradation path is strictly increasing. However, in some circumstances, the GP is not able to successfully capture the degradation path. One resolution to tackle this problem is to consider the random effect into the GP model.In this paper, we propose a trend gamma process (TGP), which integrated the merits of trend function into a GP model, as an alternative approach to overcome this obstacle. The proposed TGP model is a generalized formulation of GP model, and it is attempted to transform a monotonic stochastic process into a GP. The results based on simulation studies and real data examples illustrate that our proposed TGP model is suitable for widely use when the degradation path is monotonically increasing.

Performance reliability degradation analysis and lifetime prediction based on generalized normal grey cloud Bayesian Wiener process

“…Mercier and Castro 11 used the non-uniform Gamma process to establish a degradation model and compared two imperfect repair models for degraded systems. Wang et al 12 performed a degradation analysis based on the trend Gamma process. Oumouni et al 13 introduced the spatial stochastic scale of the lognormal distribution in the Gamma process and modeled the spatial and temporal degradation to assess reliability.…”

“…Mercier and Castro 11 used the non‐uniform Gamma process to establish a degradation model and compared two imperfect repair models for degraded systems. Wang et al 12 . performed a degradation analysis based on the trend Gamma process.…”

A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics

“…Research on modeling fatigue crack growth through the utilization of stochastic processes has been frequently seen in recent years. Specifically, the Wiener process (WP), Gamma process (GP), and inverse Gaussian process (IGP) with independent incremental properties [14] have been studied the most because of their excellent characteristics in degradation modeling.…”

An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network

“…Based on these distribution functions, the parameter can be estimated and the degradation trajectory model can be determined. Furthermore, the stochastic process can describe the randomness of the degradation path caused by internal and external influences in the product degradation process, and the popular stochastic process models include the Wiener process [14][15][16] and Gamma process [17][18][19][20]. In their studies on the durability evaluations of turbine impellers, Li et al [21] and Zhu et al [22] described the performance degradation processes of turbine impellers by developing a physical model of their failures.…”