Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells

Abstract

Click on the DOI link to access this article at the publishers website (may not be free).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. © 2024 Elsevier LtdWichita State University, WSU; Michigan Technological University, MTU; National Science Foundation, NSF: OIA-2148878; National Science Foundation, NSF; National Aeronautics and Space Administration, NASA: 80NSSC22M0028, 80NSSC23M0100; National Aeronautics and Space Administration, NASA; U.S. Department of Energy, USDOE: DE-EE0009525; U.S. Department of Energy, USDOEThis work was supported in part by the National Science Foundation under Award OIA-2148878, the Kansas NASA EPSCoR Research Infrastructure Development Program under Grant 80NSSC22M0028, and the NASA EPSCoR Program under Grant 80NSSC23M0100 to Wichita State University.This work was supported in part by the National Science Foundation under Award OIA-2148878, the Kansas NASA EPSCoR Research Infrastructure Development Program under Grant 80NSSC22M0028, and the NASA EPSCoR Program under Grant 80NSSC23M0100 to Wichita State University, and the U.S. Department of Energy under Award DE-EE0009525 to Michigan Technological University