Particle Swarm Optimization Fuzzy Systems for the Age Reduction Imperfect Maintenance Model

• Autorzy: Che-Hua Li Che-Hua
• Języki publikacji: EN

Abstrakty

EN

This research includes two topics: (1) the modeling of periodic preventive maintenance policies over an infi nite time span for repairable systems with the reduction of the degradation rate after performing an imperfect preventive maintenance (PM) activity; (2) the parameter estimation of failure distribution and the restoration effect of PM from the proposed PM policy for deteriorating systems. The concept of the improvement factor method is applied to measure the restoration effect on the degradation rate for a system after each PM. An improvement factor is presented as a function of the system's age and the cost of each PM. A periodic PM model is then developed. The optimal PM interval and the optimal replacement time for the proposed model can be obtained by minimizing the objective functions of the cost rate through the algorithms provided by this research. An example of using Weibull failure distribution is provided to investigate the proposed model. The method is proposed to estimate the parameters of the failure process and the improvement effect after each PM by analyzing maintenance and failure log data. In this method, a PSO-based method is proposed for automatically constructing a fuzzy system with an appropriate number of rules to approach the identifi ed system. In the PSO-based method, each individual in the population is constructed to determine the number of fuzzy rules and the premise part of the fuzzy system, and then the recursive least-squares method is used to determine the consequent part of the fuzzy system constructed by the corresponding individual. Consequently, an individual corresponds to a fuzzy system. Subsequently, a fi tness function is defi ned to guide the searching procedure to select an appropriate fuzzy system with the desired performance. Finally, two identifi cation problems of nonlinear systems are utilized to illustrate the effectiveness of the proposed method for fuzzy modeling.

Słowa kluczowe

EN

imperfect maintenance; preventive maintenance; reliability; fuzzy modeling; particle swarm optimization (PSO)

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2008
• Tom: nr 4
• Strony: 28--34
• Opis fizyczny: Bibliogr. 34

Twórcy

autor

Che-Hua Li Che-Hua

• P.O Box No.3162, Taipei City, Taiwan, R.O.C,

Bibliografia

• [1] Canficld, R.V., (1986): Cost Optimization of Periodic Preventive Maintenance. IEEE Transactions on Reliability, R-35: 78-81.
• [2] Chan, J. K. and Shaw, L. (1993): Modeling Repairable Systems with Failure Rates that Depend on Age and Maintenance. IEEE Transactions on Reliability, 42: 566-571.
• [3] Cheng, C.-Y. and Chen, M. (2003): The Periodic Preventive Maintenance Policy for Deteriorating Systems by Using Improvement Factor Model. International Journal of Applied Science and Engineering. 1: 114-122.
• [4] Jayabalan, V. and Chaudhnri, D. (1992a): Cost Optimization of Maintenance Scheduling for a System with Assured Reliability. IEEE Transactions on Reliability, 41: 21-25.
• [5] Jayabalan, V. and Chaudhuri, D. (1992b): Optimal Maintenance - Replacement Policy under Imperfect Maintenance. Reliability Engineering and System Safety, 36: 165-169.
• [6] Lie,C. H. and Chun, Y. H. (1986): An Algorithm for Preventive Maintenance Policy. IEEE Transactions on Reliability, R-35: 71-75.
• [7] Malik, M. A. K. (1979): Reliable Preventive Maintenance Scheduling. AIIE Transaction, 11: 221-228.
• [8] Nakagawa, T. (1979): Imperfect Preventive Maintenance. IEEE Transactions on Reliability, R-28: 331-332.
• [9] Nakagawa, T. (1986): Periodic and Sequential Preventive Maintenance Policies. Journal of Applied Probability, 23: 536-542.
• [10] Park, D. H., Jung, G. M., and Yum, J. K. (2000): Cost Minimization for Periodic Maintenance Policy of a System Subject to Slow Degradation. Reliability Engineering and System Safety, 68: 105-112.
• [11] Pham, H. and Wang, H. (1996): Invited Review: Imperfect Maintenance. European Journal of Operational Research, 94: 425-438.
• [12] Shin, I., LimT.J., and Lie, C.H. (1996): Estimating Parameters of intensity function and maintenance effect for repairable unit. Reliability Engineering and System Safety, 54: 1-10.
• [13] Yang, C.-S., Lin, C.-C., and Cheng, C.-Y. (2003): Periodic Preventive Maintenance Model For Deteriorating Equipment Using Improvement Factor Method. Proceedings of the Fifth ROC Symposium on Reliability and Maintainability, pp.345-355.
• [14] Abe S. and Lan M.S.: Fuzzy rules extraction directly from numerical data for function approximation, IEEE Trans, on Systems, Manand Cybernetics, Vol. 25, pp. 119-129, 1995.
• [15] Eberhart R. and Kennedy J.: A new optimizer using particle swarm theory, Proc. Int. Sym. Micro Machine and Human Science, Nagoya Japan, Oct. 1995, pp. 39-43.
• [16] Roger Jang J.S.: ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans, on Systems, Man and Cybernetics, Vol. 23, No. 3, pp. 665-685, 1993.
• [17] Roger Jang J.S., Sun C.T. and Mizutani E.: Neuro-Fuzzy and Soft Computing (Prentice HalLNew Jersey, 1997)
• [18] JuangC.F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design, IEEE Trans, on Systems, Man and Cybernetics- Part B: Cybernetics, Vol. 34, No. 2, pp. 997-1006, 2004.
• [19] Kennedy J. and Eberhart R.: Particle swarm optimization, Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, 1995, pp. 1942-1948.
• [20] Kosko B.: Fuzzy Engineer, (Prentice Hall, New Jersey, 1997)
• [21] Lee S.J. and Ouyang C.S.: A neuro-Juzzy system modeling with self-constructing rule generation and hybrid SVD-based learning, IEEE Trans, on Fuzzy Systems, Vol. 11, No. 3,pp. 341 -353, 2003.
• [22] Lin C.J. and Lin C.T.: An ART-based fuzzy adaptive learning control network, IHEE Trans, on Fuzzy Systems, Vol. 5, No. 4, pp. 477-496, 1997.
• [23] Simpson P.K.: Fuzzy min-max neural networks- Par!!: Classification, IEEE Trans. Neural Networks, Vol. 3, pp. 776-786, Sept., 1992.
• [24] Sugeno M. and Kang G.T.: Structure identification of fuzzy model, Fuzzy Sets and Systems, Vol. 28, pp. 15-33, 1988.
• [25] Sugeno M. and Tanaka K.: Successive identification of a fuzzy model and its applications to rediction of a complex system, Fuzzy Sets and Systems, Vol. 42, pp. 315-334. 1991.
• [26] Sugeno M. and YasukawaT.: A fuzzy-logic-based approach to qualitative modeling, IEEE Trans, on Fuzzy Systems, Vol. l,pp. 7-31, 1993.
• [27] Takagi T. and Sugeno M.: Fuzzy identification of systems and its applications to mo deling and control, IEEE Trans, on Systems, Man and Cybernetics, Vol. 15, no. 1, pp.116-132, 1985.
• [28] Teng Y.W. and Wang W.J.: Constructing a user-friendly G A-based fuzzy system directly from numerical data, IEEE Trans, on Systems, Man and Cybernetics- Part B: Cybernetics, Vol. 34, No. 5, pp. 2060-2070, 2004.
• [29] Wang L.X. and Mendel J.M.: Generating fuzzy rules by learning from examples, IEEE Trans.on Systems, Man and Cybernetics, Vol. 22, No. 6, pp. 1414-1427, 1992.
• [30] Wang L.X.: A Course in Fuzzy Systems and Control (Prentice Hall, New Jersey, 1997)
• [31] Wong C.C. and Chen C.C.: A hybrid clustering and gradient descent approach for fuzzy modeling, IEEE Trans, on Systems, Man and Cybernetics- Part B: Cybernetics, Vol. 29, No. 6, pp. 686-693, 1999.
• [32] Wong C.C. and Chen C.C.: A GA-based method for constructing fuzzy systems directly from numerical data, IEEE Trans, on Systems Man and Cybernetics- Part B: Cybernetics, Vol. 30, No. 6, pp. 904-911, 2000.
• [33] Yager R.R. and Filev D.P.: Essentials of Fuzzy Modeling and Control (John Wiley, New York, 1994)
• [34] Yen J. and Wang L.: Simplifying fuzzy rule-based models using orthogonal transformation methods, IEEE Trans. On Systems, Man and Cybernetics- Part B: Cybernetics, Vol. 29, No. 1, pp. 13-24, 1999.