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

Asgari, Ali; Si, Wujun; Wei, Wei; Krishnan, Krishna; Liu, Kunpeng

doi:10.1016/j.ress.2024.110651

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

Journal Article · Sun Nov 17 00:00:00 EST 2024 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2024.110651· OSTI ID:2506141

Asgari, Ali ^[1]; ^[1]; Wei, Wei ^[2]; Krishnan, Krishna ^[1]; Liu, Kunpeng ^[3]

Wichita State University, KS (United States)
Michigan Technological University, Houghton, MI (United States)
Portland State University, OR (United States)

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. Furthermore, 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.

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This content will become publicly available on Mon Nov 17 00:00:00 EST 2025

Research Organization:: Wichita State University, KS (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; National Science Foundation (NSF); National Aeronautics and Space Administration (NASA); USDOE

Grant/Contract Number:: EE0009525; OIA-2148878; 80NSSC22M0028

OSTI ID:: 2506141

Alternate ID(s):: OSTI ID: 2479005

Journal Information:: Reliability Engineering and System Safety, Vol. 255; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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