Failure occurrence in industrial systems can be a result of a sequence of failures leading to a total system failure. Up to now, several methods to determine failure sequences and to calculate probability of such failures have been proposed. These methods primarily focus on modeling aspects of the problem and do not present a certain framework to determine potential failure sequences. In this paper, a novel approach based on Petri net modeling of the systems is proposed and several heuristic algorithms are developed. Determination of potential failures in sample industrial problems and comparing the results with other existing methods demonstrates that the presented algorithms are much more efficient in dealing with complex Petri net models while existing methods are not capable of handling such complicated models. 1. Introduction Risk analysis of complicated systems, such as flexible manufacturing cells, is a challenging task. There are diverse approaches aiming in describing different risky behaviors of the systems. One of the most applicable tools in this field is the Fault Tree Analysis (FTA) method. This method, presented in early 1960s, is only a static graphical technique to find correlations among principal reasons of a system failure [1] which makes it difficult in dealing with complicated systems. Other methods, including Failure Mode and Effect Analysis (FMEA), suffer from a similar deficiency [2, 3]. Failures occurring in systems are not confined to failures of each independent sub-system. Sequential failures of sub-systems may also lead to the failure of the entire system. Sequential Failure Logic (SFL) was presented by Fussell et al. [4]. In this research, the focus is on analyzing non-repairable electric supply systems with main and standby power units and switch controls. Exact and approximate methods are used to calculate the probability of occurrence of the output event from priority-AND SFL. It is assumed that elementary events are independent and stochastic [4]. The approach proposed in [4] is then adopted by some researchers, for example, in risk analysis of a human-robot system [5], in the field of product liability prevention [6], and quantitative analysis of dynamic systems like space satellites [7]. The concept of sequential failure analysis [1] has been further developed by introducing counters of transitions in stochastic Petri nets (SPNs) located in various network connections [8]. The probabilities of sequential failures are calculated based on the obtained counters of failure transitions in the net. A fuzzy approach to the
References
[1]
A. Adamyan and D. He, “Sequential failure analysis using counters of Petri net models,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 33, no. 1, pp. 1–11, 2003.
[2]
M. Braglia, M. Frosolini, and R. Montanari, “Fuzzy critically assessment for failure modes and effect analysis,” International Journal of Quality & Reliability Management, vol. 20, no. 4, pp. 503–524, 2003.
[3]
K. Xu, L. C. Tang, M. Xie, and M. L. Zhu, “Fuzzy assessment of FMEA for engine systems,” Reliability Engineering & System Safety, vol. 75, no. 1, pp. 19–27, 2002.
[4]
J. B. Fussell, E. F. Aber, and R. G. Rahl, “On the quantity analysis of priority-AND failure logic,” IEEE Transactions on Reliability, vol. R-25, no. 5, pp. 324–326, 1976.
[5]
Y. Sato, E. J. Henley, and K. Inoue, “Action-chain model for the design of hazard-control systems for robots,” IEEE Transactions on Reliability, vol. 39, no. 2, pp. 151–157, 1990.
[6]
Y. Shibata and Y. Sato, “Case study of risk assessment for product liability prevention,” in Proceedings of the PSAM-4, vol. 2, pp. 1215–1220, 1998.
[7]
L. Ngom, A. Cabarbaye, and C. Barpm, “Taking into account of dependency relations in the Monte Carlo simulation of non-coherent fault trees,” in Proceedings of the PSAM-4, vol. 3, pp. 2067–2072, 1998.
[8]
F. Baccelli, G. Cohen, G. J. Olsder, and J. P. Quadrat, Synchronization and Linearity, John Wiley, New York, NY, USA, 1992.
[9]
D. Torshizi A and S. R. Hejazi, “A fuzzy approach to sequential failure analysis using Petri nets,” International Journal of Industrial Engineering and Production Research, vol. 21, no. 2, pp. 53–60, 2010.
[10]
Q. Wang, J. Gao, K. Chen, and P. Yang, “Reliability assessment of Manufacturing system based on HSPN models and non-homogeneous isomorphism Markov,” in Proceedings of the International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, pp. 182–186, 2011.
[11]
M. Zhao, Y. Zhou, Y. Yang, W. Song, and Y. Du, “A new method to detect useless service failure model in SPN,” Journal of Convergence Information Technology, vol. 5, no. 3, pp. 129–134, 2010.
[12]
H. Garg and S. P. Sharma, “Stochastic behavior analysis of complex repairable industrial systems utilizing uncertain data,” ISA Transactions, vol. 51, no. 6, pp. 752–762, 2012.
[13]
Z. Beirong, X. Xiaowen, and X. Wei, “Availability modeling and analysis of equipment based on generalized stochastic petri nets,,” Research Journal of Applied Sciences, Engineering and Technology, vol. 4, no. 21, pp. 4362–4366, 2012.
[14]
C. Su and S. Wang, “Dynamic reliability simulation for manufacturing system based on stochastic failure sequence analysis,” Journal of Mechanical Engineering, vol. 47, no. 24, pp. 165–170, 2011.
[15]
A. Adamyan and D. He, “Analysis of sequential failures for assessment of reliability and safety of manufacturing systems,” Reliability Engineering and System Safety, vol. 76, no. 3, pp. 227–236, 2002.
[16]
C. A. Petri, “Kommunikation mit automaten,” Schriften Des IIM no. 3, Institut für Instrumentelle Mathematik, Bonn, Germany, 1962.
[17]
T. Murata, “Petri nets: properties, analysis and applications,” Proceedings of the IEEE, vol. 77, no. 4, pp. 541–580, 1989.
[18]
N. G. Leveson and J. L. Stolzy, “Safety analysis using Petri nets,” IEEE Transactions on Software Engineering, vol. SE-13, no. 3, pp. 386–397, 1987.
[19]
S. K. Yang and T. S. Liu, “Failure analysis for an airbag inflator by petri nets,” Quality and Reliability Engineering International, vol. 13, no. 3, pp. 139–151, 1997.
[20]
T. S. Liu and S. B. Chiou, “The application of Petri nets to failure analysis,” Reliability Engineering and System Safety, vol. 57, no. 2, pp. 129–142, 1997.
[21]
G. S. Hura and J. W. Atwood, “Use of Petri nets to analyze coherent fault trees,” IEEE Transactions on Reliability, vol. 37, no. 5, pp. 469–474, 1988.
[22]
V. Kumar and K. K. Aggarwal, “Petri Net modelling and reliability evaluation of distributed processing systems,” Reliability Engineering and System Safety, vol. 41, no. 2, pp. 167–176, 1993.
[23]
J. Changjun, D. Baiqing, and W. Feng, “Study on reliability of manufacturing system based on petri net,” High Technology Letters, vol. 1, no. 2, pp. 25–30, 1995.
[24]
H. Xiong and Y. He, “GSPN based reliability modeling and analysis of CIMS,” Mechanical Science Technology, vol. 16, pp. 1103–1106, 1997.
[25]
C. H. Kuo and H. P. Huang, “Failure modeling and process monitoring for flexible manufacturing systems using colored timed petri nets,” IEEE Transactions on Robotics and Automation, vol. 16, no. 3, pp. 301–312, 2000.
