Reliability, availability, and maintainability (RAM) analysis has helped to identify the critical and sensitive subsystems in the production systems that have a major effect on system performance. But the collected or available data, reflecting the system failure and repair patterns, are vague, uncertain, and imprecise due to various practical constraints. Under these circumstances it is difficult, if not possible, to analyze the system performance up to desired degree of accuracy. For this, Artificial Bee Colony based Lambda-Tau (ABCBLT) technique has been used for computing the RAM parameters by utilizing uncertain data up to a desired degree of accuracy. Results obtained are compared with the existing Fuzzy Lambda-Tau results and we conclude that proposed results have a less range of uncertainties. Also ranking the subcomponents for improving the performance of the system has been done using RAM-Index. The approach has been illustrated through analyzing the performance of the screening unit of a paper industry. 1. Introduction In any production plant, systems are expected to be operational and available for the maximum possible time so as to maximize the overall production and hence profit. That is each component/system of the entire production plant will run failure free for enhancing the production as well as productivity of the plant and furnish their excellent performance. However, failures are inevitable; a product will fail sooner or later. These failures may be the result of human error, poor maintenance, or inadequate testing and inspection. Therefore, the systems and components undergo several failure-repair cycles that include logistic delays while performing repair leads to the degradation of systems’ overall performance [1]. System performance depends on reliability and availability of the system/components, operating environment, maintenance efficiency, operation process and technical expertise of operators, and so forth. To improve the system reliability and availability, implementation of appropriate maintenance strategies play an important role. High performance of these units can be achieved with highly reliable subunits and perfect maintenance. To this effect the knowledge of behavior of system, their component(s) is customary in order to plan and adapt suitable maintenance strategies. Thus, maintainability is also to be a key index to enhance the performance of these systems [2, 3]. On the other hand availability of the system can be improved by improvement in its reliability and maintainability. To maintain the availability of
References
[1]
D. Kumar, Analysis and optimization of systems availability in sugar, paper and fertilizer industries [Ph.D. thesis], University of Roorkee (Presently IIT Roorkee), Roorkee, India, 1991.
[2]
C. Ebeling, An Introduction to Reliability and Maintainability Engineering, Tata McGraw-Hill, New York, NY, USA, 2001.
[3]
H. Garg and S. P. Sharma, “RAM analysis of a coal crushing unit of a thermal power plant using fuzzy Lambda-Tau Methodology,” in Proceedings of the 1st International Conference on Emerging Trends in Mechanical Engineering (ICETME '11), pp. 795–802, Thapar University, Patiala, India.
[4]
H. Garg and S. P. Sharma, “A two-phase approach for reliability and maintainability analysis of an industrial system,” International Journal of Reliability, Quality and Safety Engineering, vol. 19, no. 3, 2012.
[5]
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.
[6]
A. Adamyan and D. He, “System failure analysis through counters of Petri net models,” Quality and Reliability Engineering International, vol. 20, no. 4, pp. 317–335, 2004.
[7]
H. Garg, “Reliability analysis of repairable systems using Petri nets and Vague Lambda-Tau methodology,” ISA Transactions, http://dx.doi.org/10.1016/j.isatra.2012.06.009 . In press.
[8]
P. S. Rajpal, K. S. Shishodia, and G. S. Sekhon, “An artificial neural network for modeling reliability, availability and maintainability of a repairable system,” Reliability Engineering and System Safety, vol. 91, no. 7, pp. 809–819, 2006.
[9]
Komal, S. P. Sharma, and D. Kumar, “RAM analysis of repairable industrial systems utilizing uncertain data,” Applied Soft Computing Journal, vol. 10, no. 4, pp. 1208–1221, 2010.
[10]
M. Rani, S. P. Sharma, and H. Garg, “Reliability analysis of press unit in paper plant using weibull fuzzy distribution function,” in Proceedings of the 16th Online World Conference on Soft computing in Industrial Application (WSC '11), December 2011, http://wsc16.cs.lboro.ac.uk/conference/sites/default/files/Paper_0.pdf.
[11]
J. Knezevic and E. R. Odoom, “Reliability modelling of repairable systems using Petri nets and fuzzy Lambda-Tau methodology,” Reliability Engineering and System Safety, vol. 73, no. 1, pp. 1–17, 2001.
[12]
S. P. Sharma and H. Garg, “Behavioural analysis of urea decomposition system in a fertiliser plant,” International Journal of Industrial and Systems Engineering, vol. 8, no. 3, pp. 271–297, 2011.
[13]
H. Garg and S. P. Sharma, “Behavior analysis of synthesis unit in fertilizer plant,” International Journal of Quality and Reliability Management, vol. 29, no. 2, pp. 217–232, 2012.
[14]
D. L. Mon and C. H. Cheng, “Fuzzy system reliability analysis for components with different membership functions,” Fuzzy Sets and Systems, vol. 64, no. 2, pp. 145–157, 1994.
[15]
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.
[16]
S. M. Chen, “Fuzzy system reliability analysis using fuzzy number arithmetic operations,” Fuzzy Sets and Systems, vol. 64, no. 1, pp. 31–38, 1994.
[17]
H. Garg, S. P. Sharma, and M. Rani, “Behavior analysis of pulping unit in a paper mill with weibull fuzzy distribution function using ABCBLT technique,” International Journal of Applied Mathematics and Mechanics, vol. 8, no. 4, pp. 86–96, 2012.
[18]
D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Computer Engineering Department, Engineering Faculty, Erciyes University, 2005.
[19]
D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
[20]
D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 687–697, 2008.
[21]
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[22]
W. Pedrycz, “Why triangular membership functions?” Fuzzy Sets and Systems, vol. 64, no. 1, pp. 21–30, 1994.
[23]
X. Bai and S. Asgarpoor, “Fuzzy-based approaches to substation reliability evaluation,” Electric Power Systems Research, vol. 69, no. 2-3, pp. 197–204, 2004.
[24]
D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009.
[25]
T. J. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, New York, NY, USA, 2nd edition, 2004.
