Maintenance Optimization for Complex System using Evolutionary Algorithms under Reliability Constraints within the Context of the Reliability-Centered-Maintenance

doi:10.23940/ijpe.21.01.p1.113

Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (1): 1-13.doi: 10.23940/ijpe.21.01.p1.113

• Orginal Article • Next Articles

Maintenance Optimization for Complex System using Evolutionary Algorithms under Reliability Constraints within the Context of the Reliability-Centered-Maintenance

Bouzouada Abdallah^a, Yssaad Benyssaad^b^*, Daoud Mohamed^c^d, Bekkouche Benaissa^c, and Yagoubi Benabdellah^c

^aResearch Laboratory Industrial Engineering and Sustainable Development (GIDD), University Abdelhamid Ibn Badis of Mostaganem Algeria
^bResearch Laboratory: Industrial Engineering and Sustainable Development (GIDD), Ahmed Zabana University of Relizane Algeria
^cResearch Laboratory of Signals and Automatic Systems (LSS), University Abdelhamid Ibn Badis of Mostaganem Algeria
^dLaboratory PRISME, EA 4229, F45072, University of Orléans France

Submitted on ; Revised on ; Accepted on
Contact: * Corresponding author. E-mail address: benyssaad.yssaad@cu-relizane.dz
About author:
Bouzouda Abdellah is a PhD student at the Department of Electrical Engineering at the faculty of science and technology of the University of Mostaganem Algeria- He is a member of the laboratory “Industrial Engineering and Sustainable Development (GIDD), University center AhmaeZabana of Relizane Algeria” His research focus is the optimization and modeling in the field of maintenance of electrical systems. E-mail: abdallah.bouzouada@univ-mosta.dz
Yssaad Benyssaad is a teacher at the university center of RELIZANE Algeria. He holds a PHD in electrical engineering. He is a member of the laboratory “Industrial Engineering and Sustainable Development (GIDD), University center AhmaeZabanaofRelizane Algeria” His research focus is the optimization and modeling in the field of maintenance of electrical systems, automation systems, smart technology and renewable energy. E-mail: benyssaad_y@yahoo.fr orbenyssaad.yssaad@cu-relizane.dz
Daoud Mohamed is a teacher researcher at AbdelhamidIbnBadis University of Mostaganem, Algeria. She received Magister in Electronics, signal processing specialty from University of U.S.T.Oran, Algeria in 1997, he obtained his Doctorat in Signal processing from department of electronics and electrical engineering at Mostaganem University, Algeria in 2014. His research interests are signal processing, image processing, solar energy, the electric vehicle, biomedical, automatism and several other topics related to signal processing, he had twenty years of teaching, research. He has supervised several engineers and masters and he is currently supervising a doctoral student. He was a member of several research projects. E-mail: mohamed.daoud@univ-mosta.dz
Bekkouche Benaissa is a Professor at the Department of Electrical Engineering at the faculty of science and technology of the University of Mostaganem Algeria-; He has a PHD in electrical engineering. He is member of the laboratory of Signals and Automatic Systems (LSS). His research interests are: the maintenance of electrical systems, control systems, and renewable energy. E-mail: becbenm@yahoo.fr
Yagoubi Benabdellah received the M. Sc degree in Electrical Engineering in 1985 from Bel-Abbes University, Algeria and the Ph. D degree (amorphous thin films) (1986-1989) in the Faculty of Sciences from Brunel University (UK). He was the head of the Signals and Systems Laboratory (1999-2003) and the head of the Department of Electrical Engineering (2005-2006). He is lecturing the theory of digital signal, systems modeling and identification, random processes and detection (1996-2016) at Mostaganem University, Algeria. He has been involved in some national projects such as forest fire detection, heart rate variability in the LF and HF bands to characterize the autonomous nervous system, and study and application of random processes. Currently, he is focusing on extracting useful information from mono-dimensional and multidimensional signals as well as on detection using the inverse of the belonging probability technique of a rare event to a Gaussian realization, as part of a national project. Further research interests are in real signals and models geometric representation based on Gram-Schmidt orthogonalization concept, as well as using a relative geometric space of observation. E-mail: benabdellah.yagoubi@univ-mosta.dz

Abstract

Abstract:

In this paper, we present maintenance cost optimization techniques under reliability constraints based on evolutionary algorithms for complex systems in the context of the Reliability-Centered-Maintenance (RCM). Our main goal is to find the best maintenance policy for this system by minimizing the maintenance cost function of the system, under the constraint of the required reliability for a given period. This policy identifies the optimum times in which the components must perform preventive maintenance (PM). The maintenance cost can be considered as a PM, an unscheduled maintenance (UM) and a replacement maintenance (RM) cost. The PM action is, therefore, considered to have an imperfect effect on the component in this work. The imperfect PM is executed whenever the component reliability reaches a certain threshold. The proposed method allows us to find the reliability threshold $R th j$ for each system component j related to the number n of PM actions performing the RM action on the components in order to minimize the expected total maintenance cost of the system over the mission time $(T mis$ ). Therefore, the optimal PM intervals durations obtained for each component $T i j$ (i = 1, 2, $?$ , n) correspond to the optimal reliability threshold. In this study, we use the evolutionary algorithm to find the optimal reliability threshold $R th j$ applied on each component j. A comparative study is performed to evaluate the performance of the Lévy-flight firefly (LFA) algorithm and particle swarm optimization (PSO) algorithm in finding the global optimum.

Key words: complex system, imperfect preventive maintenance, reliability centered maintenance, maintenance cost, optimization, levy-flight firefly, particle swarm optimization

Bouzouada Abdallah, Yssaad Benyssaad, Daoud Mohamed, Bekkouche Benaissa, and Yagoubi Benabdellah. Maintenance Optimization for Complex System using Evolutionary Algorithms under Reliability Constraints within the Context of the Reliability-Centered-Maintenance [J]. Int J Performability Eng, 2021, 17(1): 1-13.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 25.

1.	B. Yssaad, M. Khiat,A. Chaker, “Reliability Centered Maintenance Optimization for Power Distribution Systems,” International Journal of Electrical Power and Energy Systems, Vol. 55, pp. 108-115, February 2014
2.	B. Yssaad, “Optimization of the Maintenance based on the Approach of the Techniques of Modelling and Simulation: Application to the Networks of Transport and Interconnection,” Doctoral thesis, ENPO Oran Algeria, January 2014
3.	B. Yssaad, M. Khiat,A. Chaker.“Reliability Centered Asset Maintenance Optimization for Power Distribution Systems,” IREMOS International Review on Modelling and Simulations, Vol. 6, No. 1, Part A, February 2013
4.	M. A. da Rosa, A. M. Leite da Silva, and V. Miranda, “Multi-Agent Systems Applied to Reliability Assessment of Power Systems,” International Journal of Electrical Power and Energy Systems, Vol. 42, No. 1, November 2012
5.	D. E. Nordgård, “A Framework for Risk-Informed Decision Support in Electricity Distribution Companies Utilizing Input from Quantitative Risk Assessment,” International Journal of Electrical Power and Energy Systems, Vol. 43, No. 1, December 2012
6.	L. Chang and Z. G. Wu, “Performance and Reliability of Electrical Power Grids under Cascading Failures,” International Journal of Electrical Power and Energy Systems, Vol. 33, No. 8, October 2011
7.	M. Malik, “Reliable Preventive Maintenance Scheduling,” A I I E Transactions, Vol. 11, No. 3, pp. 221-228, 1979
8.	T. Nakagawa, “Sequential Imperfect Preventive Maintenance Policies,” IEEE Transactions on Reliability, Vol. 37, No. 3, pp. 295-298, 1988
9.	A. Khatab, D. Ait-Kadi,N. Rezg, “Availability Optimisation for Stochastic Degrading Systems under Imperfect Preventive Maintenance,” International Journal of Production Research, Vol. 52, No. 14, pp. 4132-4141, 2014
10.	A. Khatab, C. Diallo,I. B. Sidibe, “Optimizing Upgrade and Imperfect Preventive Maintenance in Failure-Prone Second-Hand Systems,” Journal of Manufacturing Systems, Vol. 43, Part 1, pp. 58-78, April 2017
11.	X. Zhou, L. Xi,J. Lee, “Reliability-Centered Predictive Maintenance Scheduling for a Continuously Monitored System Subject to Degradation,” Reliability Engineering and System Safety, Vol. 92, No. 4, pp. 530-534, 2007
12.	X. Zhou, Y. Li, L. Xi,J. Lee, “Multi-Phase Preventive Maintenance Policy for Leased Equipment,” International Journal of Production Research, Vol. 53, No. 15, pp. 4528-4537, 2014
13.	D. Lin, M. Zuo,R. Yam, “General Sequential Imperfect Preventive Maintenance Models,” International Journal of Reliability Quality and Safety Engineering, Vol. 7, No. 3, pp. 253-266, 2000
14.	C. H.Lie and Y. H. Chun, “An Algorithm for Preventive Maintenance Policy,” IEEE Transactions on Reliability, Vol. 35, No. 1, pp. 71-75, 1986
15.	X. Zhou, L. Xi,J. Lee, “Reliability-Centered Predictive Maintenance Scheduling for a Continuously Monitored System Subject to Degradation,” Reliability Engineering and System Safety, Vol. 92, No. 4, pp. 530-534, 2007
16.	I. Maatouka, N. Chebbo, E. Châtelet,I. Jarkass, “Preventive Maintenance Optimization for Multi States Series Parallel System,” Proceeding based on AIP Guide, Vol.2008, ISBN: 978-983-44483-63, 2013
17.	J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” inProceedings of IEEE International Conference on Neural Networks, pp. 1942-1948, Piscataway, NJ, 1995
18.	J. Kennedy and R. C. Eberhart, “A Discrete Binary Version of the Particle Swarm Algorithm,” in Proceedings of IEEE International Conference on Systems,Man and Cybernetics, pp. 4104-4108, Orlando, FL, 1997
19.	M. K.Loganathan and O. P. Gandhi, “Maintenance Cost Minimization of Manufacturing Systems using PSO under Reliability Constraint,”International Journal of Systems Assurance Engineering and Management, DOI: 10.1007/s13198-015-0374-2, Springer, July 2015
20.	G. Sharma and P. C. Tewari, “Performability Analysis for Feeding Unit of a Sugar Plant using Particle Swarm Optimization Technique,” International Journal of Performability Engineering, Vol. 15, No. 11, pp. 2835-2842, 2019
21.	M. Alrifaey, T. S. Hon, A. As'arry, E. E. Supeni, and C. K. Ang, “Optimization and Selection of Maintenance Policies in an Electrical Gas Turbine Generator based on the Hybrid Reliability-Centered Maintenance (RCM) Model,”MDPI/Processes (2227 9717), 2020
22.	X. S. Yang, “Nature-Inspired Metaheuristic Algorithms,” Luniver Press, Frome, UK, 2008
23.	X. -S. Yang, “Firefly Algorithm, Lévy Flights and Global Optimization,” RDIS Research and Development in Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, M. Petridis), Springer, London, pp. 209-218, 2010
24.	V. G. Mejlej, P. Falkenberg, E.Türck,T.Vietor, “Optimization of Variable Stiffness Composites in Automated Fiber Placement Process using Evolutionary Algorithms,” Procedia CIRP, Vol. 66, pp. 79-84, 2017
25.	N. F. Johari, A. M. Zain, N. H. Mustaffa,A. Udin, “Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization,” inProceedings of the 6th International Conference on Computer Science and Computational Mathematics, 2017

[1]	Fatma Zohra Labadlia, Elias Hadjadj Aoul, Brahim Hamaidi, Mohammed Bougofa. Optimization of Safety Instrumented System Configuration based on Simplex Algorithm [J]. Int J Performability Eng, 2021, 17(2): 191-199.
[2]	Kun Li, Liwei Jia, and Xiaoming Shi. IPSOMC: An Improved Particle Swarm Optimization and Membrane Computing based Algorithm for Cloud Computing [J]. Int J Performability Eng, 2021, 17(1): 135-142.
[3]	Xiu Kan, Xiafeng Zhang, Le Cao, Dan Yang, and Yixuan Fan. EMG Pattern Recognition based on Particle Swarm Optimization and Recurrent Neural Network [J]. Int J Performability Eng, 2020, 16(9): 1404-1415.
[4]	Ran Zhang, Min Liu, Yifeng Yin, Qikun Zhang, and Zengyu Cai. Prediction Algorithm for Network Security Situation based on BP Neural Network Optimized by SA-SOA [J]. Int J Performability Eng, 2020, 16(8): 1171-1182.
[5]	Shujun Pei, Qinggen Zhang, and Xuehui Cheng. Workflow Scheduling using Graph Segmentation and Reinforcement Learning [J]. Int J Performability Eng, 2020, 16(8): 1262-1270.
[6]	Manish Rawat and Bhupesh Kumar Lad. Joint Optimization of Reliability Design and Level of Repair Analysis Considering Time Dependent Failure Rate of Fleet System [J]. Int J Performability Eng, 2020, 16(6): 821-833.
[7]	Shenyi Qian, Yongsheng Shi, Huaiguang Wu, and Songtao Shang. Prediction of Electricity Tariff Recovery Risk based on Hybrid Feature Selection Algorithm [J]. Int J Performability Eng, 2020, 16(6): 846-854.
[8]	Hongchan Li and Haodong Zhu. Modified Water Wave Optimization for Energy-Conscious Dual-Resource Constrained Flexible Job Shop Scheduling [J]. Int J Performability Eng, 2020, 16(6): 916-929.
[9]	Yong Cui, Haoran Chen, and Liang Zhu. Improved Post-Copy Live Migration with Memory Page Prefetching [J]. Int J Performability Eng, 2020, 16(5): 728-737.
[10]	Mohammed Bougofa, Abderraouf Bouafia, and Ahmed Bellaouar. Availability Assessment of Complex Systems under Parameter Uncertainty using Dynamic Evidential Networks [J]. Int J Performability Eng, 2020, 16(4): 510-519.
[11]	Xuan Chen, Zhengjiang Song, Hongfeng Zheng, and Zhiping Wan. Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing [J]. Int J Performability Eng, 2020, 16(4): 618-628.
[12]	Mohammed Bougofa, Abderraouf Bouafia, and Ahmed Bellaouar. An Integrated Quantitative Bayesian Network in Risk Management for Complex Systems [J]. Int J Performability Eng, 2020, 16(3): 354-366.
[13]	Guochao Ding, Haosong Duan, Xi Wang, and Zhen Zhou. Analysis of Extinction Spectrum Apparent Property and Extraction of Specific Wavelength of Milk Fat Particles in Liquid Absorbing Medium [J]. Int J Performability Eng, 2020, 16(3): 430-437.
[14]	Youfen Chen. Improved Particle Swarm Optimization Algorithm for Image Segmentation [J]. Int J Performability Eng, 2020, 16(3): 482-489.
[15]	Yong Cui, Liang Zhu, Zengyu Cai, and Ying Hu. An Adaptive Traffic-Aware Migration Algorithm Selection Framework in Live Migration of Multiple Virtual Machines [J]. Int J Performability Eng, 2020, 16(2): 314-324.

Maintenance Optimization for Complex System using Evolutionary Algorithms under Reliability Constraints within the Context of the Reliability-Centered-Maintenance

PDF

Knowledge

Abstract

Cite this article

share this article

References 25.

Related Articles 15

Recommended 10