A Hybrid Condition-Based Maintenance Model for Deteriorating Systems Subject to Nonmemoryless Imperfect Repairs and Perfect Replacements

• Published in: IEEE transactions on reliability Vol. 69; no. 2; pp. 781 - 815
• Main Author: Huynh, Khac Tuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Analytical models; Business competition; Computer Science; Deterioration; Gamma deterioration process; hybrid condition-based maintenance; imperfect repair; Inspection; long-run cost rate; Maintenance costs; Mathematical model; memory effect; Policies; Preventive maintenance; Probability density function; replacement; semiregenerative process; Switches; Systems and Control
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2019.2942019

Condition-based maintenance (CBM) is popularly recognized as a key lever that enables organizations to gain a competitive advance in the context of the fourth industrial revolution. Combining both the perfect and imperfect maintenance actions into CBM policies seems to be an efficient way to achieve high economic performances. Following this view, we propose and optimize in this article a hybrid deterioration-based maintenance model for continuously deteriorating systems subject to nonmemoryless imperfect repairs and perfect replacements. Compared to existing related works, the originality of our model is threefold. First, an innovative model representing both the positive and memory effects of imperfect repairs is developed. Second, a new decision rule for switching between perfect and imperfect maintenance actions is proposed. Finally, the mathematical cost model of the proposed hybrid maintenance policy is fully formulated on the basis of the sophisticated semiregenerative theory. Numerous sensitivity studies and comparisons with benchmark policies under various configurations of maintenance costs and system characteristics allow to better understand the behaviors of our hybrid maintenance policy and to confirm its effectiveness.