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Condition-based maintenance policy optimization using genetic algorithms and Gaussian Markov improvement algorithm

Published

Author(s)

Michael Hoffman, Eunhye Song, Michael Brundage, Soundar R. Kumara

Abstract

Condition-based maintenance involves monitoring the degrading health of machines in a manufacturing system and scheduling maintenance to avoid costly unplanned failures. As compared with preventive maintenance, which maintains machines on a set schedule based on time or run time of a machine, condition-based maintenance attempts to minimize the number of times maintenance is performed on a machine while still attaining a prescribed level of availability to save on maintenance costs and reduce unwanted downtime over its lifetime. Finding an analytically-optimal condition-based maintenance policy is difficult when the target system has non-uniform machines, stochastic maintenance time, and capacity constraints on maintenance resources. In this work, we find an optimal condition-based maintenance policy for a serial manufacturing line using a genetic algorithm and Gaussian Markov Improvement Algorithm, an optimization via simulation method for a stochastic problem with a discrete solution space. The effectiveness of these two algorithms will be compared. When a maintenance job (i.e., machine) is scheduled, it is placed in a queue that is serviced with either a first-in-first-out discipline or based on a priority. In the latter, we apply the concept of opportunistic window to identify a machine that has the largest potential to disrupt the production of the system and assign a high priority to the machine. A test case is presented to demonstrate this method and its improvement over traditional maintenance methods.
Proceedings Title
Proceedings of the Annual Conference of the Prognostics and Health Management Society 2018
Conference Dates
September 24-27, 2018
Conference Location
Philadelphia, PA, US
Conference Title
Annual Conference of the Prognostics and Health Management Society 2018

Keywords

condition-based maintenance, smart manufacturing, maintenance scheduling

Citation

Hoffman, M. , Song, E. , Brundage, M. and Kumara, S. (2018), Condition-based maintenance policy optimization using genetic algorithms and Gaussian Markov improvement algorithm, Proceedings of the Annual Conference of the Prognostics and Health Management Society 2018, Philadelphia, PA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926343 (Accessed July 19, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created September 23, 2018, Updated October 12, 2021