Details
| Original language | English |
|---|---|
| Article number | 110893 |
| Number of pages | 8 |
| Journal | Computers and Industrial Engineering |
| Volume | 201 |
| Early online date | 21 Jan 2025 |
| Publication status | Published - Mar 2025 |
Abstract
The reliability evaluation of a complex mechanical system is imperative yet challenging because the experiment of a full-scale system is usually unavailable or prohibitively expensive leading to insufficient or incomplete data. Moreover, the collected data is essentially dependent since it is collected from the same system within the same time period, leading to the so-called “overlapped” failure data. To address the dependence between overlapped data in system reliability analysis, a novel concept called Evidence Likelihood Function (ELF) is developed to decompose the original joint likelihood function. This approach is capable of incorporating dependent evidence in the Bayesian framework and provides us with a better understanding of the nature of dependent evidence in system reliability analysis. It has the potential to optimize the system configuration using less full-scale test data in terms of reliability improvement with lower experiment cost.
Keywords
- Bayesian method, Dependent data, Likelihood function, Probability density function, System reliability
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Engineering(all)
- General Engineering
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In: Computers and Industrial Engineering, Vol. 201, 110893, 03.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - An evidence-based likelihood approach for the reliability of a complex system with overlapped failure data
AU - Yang, Lechang
N1 - Publisher Copyright: © 2025 Elsevier Ltd
PY - 2025/3
Y1 - 2025/3
N2 - The reliability evaluation of a complex mechanical system is imperative yet challenging because the experiment of a full-scale system is usually unavailable or prohibitively expensive leading to insufficient or incomplete data. Moreover, the collected data is essentially dependent since it is collected from the same system within the same time period, leading to the so-called “overlapped” failure data. To address the dependence between overlapped data in system reliability analysis, a novel concept called Evidence Likelihood Function (ELF) is developed to decompose the original joint likelihood function. This approach is capable of incorporating dependent evidence in the Bayesian framework and provides us with a better understanding of the nature of dependent evidence in system reliability analysis. It has the potential to optimize the system configuration using less full-scale test data in terms of reliability improvement with lower experiment cost.
AB - The reliability evaluation of a complex mechanical system is imperative yet challenging because the experiment of a full-scale system is usually unavailable or prohibitively expensive leading to insufficient or incomplete data. Moreover, the collected data is essentially dependent since it is collected from the same system within the same time period, leading to the so-called “overlapped” failure data. To address the dependence between overlapped data in system reliability analysis, a novel concept called Evidence Likelihood Function (ELF) is developed to decompose the original joint likelihood function. This approach is capable of incorporating dependent evidence in the Bayesian framework and provides us with a better understanding of the nature of dependent evidence in system reliability analysis. It has the potential to optimize the system configuration using less full-scale test data in terms of reliability improvement with lower experiment cost.
KW - Bayesian method
KW - Dependent data
KW - Likelihood function
KW - Probability density function
KW - System reliability
UR - http://www.scopus.com/inward/record.url?scp=85215831223&partnerID=8YFLogxK
U2 - 10.1016/j.cie.2025.110893
DO - 10.1016/j.cie.2025.110893
M3 - Article
AN - SCOPUS:85215831223
VL - 201
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
SN - 0360-8352
M1 - 110893
ER -