Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process

Two-phase degradation is a prevalent degradation mechanism observed in modern systems, typically characterized by a change in the degradation rate or trend of a system’s performance at a specific time point. Ignoring this change in degradation models can lead to considerable biases in predicting the...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:European journal of operational research Ročník 319; číslo 3; s. 877 - 890
Hlavní autoři: Zhuang, Liangliang, Xu, Ancha, Wang, Yijun, Tang, Yincai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 16.12.2024
Témata:
Adaptive replacement
Inverse Gaussian process
Maintenance
Reliability
Remaining useful life
Maintenance
Reliability
Remaining useful life
Adaptive replacement
Inverse Gaussian process
ISSN:0377-2217
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Two-phase degradation is a prevalent degradation mechanism observed in modern systems, typically characterized by a change in the degradation rate or trend of a system’s performance at a specific time point. Ignoring this change in degradation models can lead to considerable biases in predicting the remaining useful life (RUL) of the system, and potentially leading to inappropriate condition-based maintenance decisions. To address this issue, we propose a novel two-phase degradation model based on a reparameterized inverse Gaussian process. The model considers variations in both change points and model parameters among different systems to account for subject-to-subject heterogeneity. The unknown parameters are estimated using both maximum likelihood and Bayesian approaches. Additionally, we propose an adaptive replacement policy based on the distribution of RUL. By sequentially obtaining new degradation data, we dynamically update the estimation of model parameters and of the RUL distribution, allowing for adaptive replacement policies. A simulation study is conducted to assess the performance of our methodologies. Finally, a Lithium-ion battery example is provided to validate the proposed model and adaptive replacement policy. Technical details and additional results of case study are available as online supplementary materials.
ISSN:0377-2217
DOI:10.1016/j.ejor.2024.06.032