Rollout strategy-based probabilistic causal model approach for the multiple fault diagnosis

Mishra, Nishikant, Kumar Choudhary, Alok, Tiwari, M.K. and Shankar, Ravi (2010) Rollout strategy-based probabilistic causal model approach for the multiple fault diagnosis. Robotics and Computer-Integrated Manufacturing, 26 (4). pp. 325-332. ISSN 0736-5845

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Abstract

Multiple fault diagnosis (MFD) is used as an effective measure to tackle the problems of real-shop floor environment for reducing the total lifetime maintenance cost of the system. It is a well-known computationally complex problem, where computational complexity increases exponentially as the number of faults increases. Thus, warrants the application of heuristic techniques or AI-based optimization tools to diagnose the exact faults in real time. In this research, rollout strategy-based probabilistic causal model (RSPCM) has been proposed to solve graph-based multiple fault diagnosis problems. Rollout strategy is a single-step iterative process, implemented in this research to improve the efficiency and robustness of probabilistic causal model. In RSPCM instead of finding all possible combinations of faults, collect the faults corresponding to each observed manifestations that can give the best possible result in compared to other methods. Intensive computational experiments on well-known data sets witness the superiority of the proposed heuristic over earlier approaches existing in the literature. From experimental results it can easily inferred that proposed methodology can diagnosed the exact fault in the minimum fault isolation time as compared to other approaches.

Item Type: Article
Uncontrolled Keywords: multiple fault diagnosis,rollout strategy,probabilistic causal model
Faculty \ School: Faculty of Social Sciences > Norwich Business School
Related URLs:
Depositing User: Pure Connector
Date Deposited: 06 Nov 2015 12:00
Last Modified: 25 Jul 2018 11:17
URI: https://ueaeprints.uea.ac.uk/id/eprint/55030
DOI: 10.1016/j.rcim.2009.11.010

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