Fault Diagnosis and Fault Tolerant Control for a Class of Re-Entrant Manufacturing Systems
In practical manufacturing scenarios, re-entrant manufacturing systems (RMSs) are vulnerable to uncertain workstation faults, which may cause sudden changes in the work-in-process (WIP) level and impair the overall production capacity. To ensure agile response to market demands and to maintain stable system behavior under fault scenarios, an active fault-tolerant control scheme via fault diagnosis for RMSs is developed in this paper. Firstly, a hybrid hyperbolic partial differential equation continuum model is developed to describe the evolution of WIP dynamics, where the workstation faults result in the discarding of defective products during processing. Subsequently, a fault diagnosis scheme is presented to detect and estimate the uncertain faults in real time, by integrating an observer-based fault detection method and an adaptive fault estimation algorithm. Utilizing the estimated fault information, a fault-tolerant control strategy is then proposed to compensate for fault-induced state jumps and achieve agile production control. Finally, a numerical simulation is conducted to demonstrate the effectiveness of the proposed fault-tolerant control approach. It is believed the proposed theory enriches the theory, analysis, design of control circuit systems and has potential of practical implementations to industrial manufacturing systems.
Authors
- Qing Gao (ORCID: https://orcid.org/0000-0001-8000-7736)
- Steven X. Ding (ORCID: https://orcid.org/0000-0002-5149-5918)
- Jianbin Qiu (ORCID: https://orcid.org/0000-0001-7707-6159)
Institutions
- Harbin Institute of Technology (CN)
- University of Duisburg-Essen (DE)
- Beihang University (CN)
Publication Details
- Journal
- IEEE Transactions on Circuits and Systems I Regular Papers
- Published
- 2025-08-28
- DOI
- https://doi.org/10.1109/tcsi.2025.3602398
- Citations
- 4
- Primary Topic
- Fault Detection and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 4.86
Funders
- National Natural Science Foundation of China