Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells
Document Type
Article
Publication Date
3-2025
Department
Department of Mechanical and Aerospace Engineering
Abstract
Recently, the Generalized Cauchy (GC) process has been applied to capture a Long Memory (LM) phenomenon in product degradation modeling and life prediction. Compared with the traditional fractional Brownian motion that captures the LM using a single Hurst parameter, the GC process has two free parameters (Hurst and fractal dimension parameters) that flexibly capture both global LM and local irregularity. However, all existing GC-based degradation models are for a single Degradation Characteristic (DC). In this article, motivated by a real degradation problem of dye-sensitized solar cells that jointly exhibits multiple DCs, global LM, local irregularity and DC-wise cross-correlation, we propose a novel GC-based Multivariate Degradation Model (GC-MDM) to simultaneously capture the aforementioned effects. A maximum likelihood estimation approach is developed to estimate parameters of the GC-MDM. Subsequently, product life prediction based on the GC-MDM is developed. The proposed GC-MDM is validated through a simulation study and a physical experiment of dye-sensitized solar cells. Results show that the proposed GC-MDM fundamentally improves the life prediction accuracy in comparison with conventional degradation models which significantly misestimate the uncertainty of product life.
Publication Title
Reliability Engineering & System Safety
Recommended Citation
Asgari, A.,
Si, W.,
Wei, W.,
Krishnan, K.,
&
Liu, K.
(2025).
Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells.
Reliability Engineering & System Safety,
255.
http://doi.org/10.1016/j.ress.2024.110651
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/1345