NDLI: Gas turbine preventive maintenance optimization using genetic algorithm

Content Provider	Springer Nature Link
Author	Moinian, Fatemeh Sabouhi, Hamed Hushmand, Jafar Hallaj, Ahmad Khaledi, Hiwa Mohammadpour, Mojtaba
Copyright Year	2017
Abstract	The tremendous impact of an optimized maintenance program on system overall cost and reliability leads various industrial managers and owners to seek an intelligent tool for maintenance decision making. Gas turbine industry is no exception, since it is of the most expensive and critical components in both power plant and oil and gas industries. In this paper an intelligent maintenance optimization tool is developed based on genetic algorithm. Genetic algorithm is a heuristic optimization method in which genetic evolution patterns are employed. The algorithm has been used for solving several optimization problems and its ability to find optimized solutions makes it one of the most used algorithms. The main purpose of proposed algorithm is to make the balance between maintenance costs (i.e. direct and indirect) and down time cost while maintaining system availability on predefined level. Moreover, maintenance constraints such as task interval, maintenance duration are considered. To handle these constraints, new repair operators are defined and applied in the proposed genetic algorithm, besides other crossover and mutation operators. In order to verify and validate the novel developed algorithm, results of its implementation on a gas turbine case study are discussed. The case study is a maintenance optimization problem of Siemens SGT600 gas turbine, comprised of seventeen components and their maintenance activities, two life wear patterns and four production loss scenarios. Results of the optimized solution are compared with gas turbine conventional maintenance plan which is proved to have considerable improvements. It is shown that an optimized maintenance plan would reduce outage time and also increase the availability, which is mainly due to grouping maintenance activities. Besides, reduction in total cost including maintenance costs and production loss cost are of economic consequences of using proposed algorithm. Total cost is reduced more than 80% while availability is improved roughly 2%.
Starting Page	594
Ending Page	601
Page Count	8
File Format	PDF
ISSN	09756809
Journal	International Journal of System Assurance Engineering and Management
Volume Number	8
Issue Number	3
e-ISSN	09764348
Language	English
Publisher	Springer India
Publisher Date	2017-05-22
Publisher Place	New Delhi
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Maintenance optimization Gas turbine Availability Genetic algorithm Quality Control, Reliability, Safety and Risk Engineering Economics, Organization, Logistics, Marketing
Content Type	Text
Resource Type	Article
Subject	Strategy and Management Safety, Risk, Reliability and Quality

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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