Projects per year
Abstract
Admitting its potential in flexible manufacturing, the reconfigurable multi-stage system (RMS) is widely used in modern industries while its reliability is of great importance since the failure of any composing stage will lead to the system failure and abortion of the whole mission. In this paper, we present a survival signature-based framework for the reliability of an RMS. The idea of our approach is to convert a conventional probability estimation problem to a graph-based path-searching problem, thus the tedious Monte Carlo sampling is simplified. To this end, an unconnected path graph method is developed to calculate the number of working paths of the equivalent graph model of RMS. Instead of directly enumerating all possible working paths, those paths of interest are identified by searching unconnected nodes via backtracking while the computation cost is reduced. To further address the case of an RMS with shared components, a sequential unconnected path graph (SUPG) method is developed. The proposed method is validated through two numerical cases and an application example. The results show our method can identify the “bottleneck” stage once the system is reconfigured with saved computational cost. © 2025 Elsevier Ltd.
Original language | English |
---|---|
Article number | 111093 |
Journal | Reliability Engineering and System Safety |
Volume | 261 |
Online published | 4 Apr 2025 |
DOIs | |
Publication status | Online published - 4 Apr 2025 |
Funding
The authors gratefully acknowledge the support of the National Natural Science Foundation of China under Grant 72271025 and 72371215, the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515011532 and 2024A1515010132, and the Research Grant Council of Hong Kong, China under Grant 11201023 and 11202224.
Research Keywords
- Reconfigurable multi-stage system
- Reliability evaluation
- Survival signature
- System reliability
- Unconnected path graph
Fingerprint
Dive into the research topics of 'Graph-based reliability evaluation of a reconfigurable multi-stage system using sequential unconnected path sets'. Together they form a unique fingerprint.Projects
- 2 Active
-
GRF: Towards Intelligent Operations and Maintenance: A Novel Failure Knowledge Graph Learning Framework
XIE, M. (Principal Investigator / Project Coordinator)
1/01/25 → …
Project: Research
-
GRF: Intelligent Prognostics and Health Management of Modular Systems
XIE, M. (Principal Investigator / Project Coordinator)
1/01/24 → …
Project: Research