Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation

By M. Marseguerra, Enrico Zio, L. Podofillini. Reliability Engineering & System Safety, 2002

366 citations recorded by the source corpus.

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Condition-based maintenance policies for systems with multiple dependent components: A review (2017, background): “Contribution System structure CBM policy structure Decision moments Marseguerra et al. (2002) Series-parallel-series Threshold for preventive imperfect repair Continuously Tan et al.”
A condition-based maintenance policy with non-periodic inspections for a two-unit series system (2005, background): “It can quickly end in complex models and one has to resort to simulation tools [10].”
Health-aware hierarchical control for smart manufacturing using reinforcement learning (2017, methodology): “Simulation environments have also been used to study maintenance policies [16], [18], [29], [31], [38], [47]; however, these existing approaches have several limitations which make them unusable for the proposed methodology.”
Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, background): “Based on the first two aspects, the optimal health management schedule is planned to minimise costs and prevent unexpected accidents [10]–[12].”
On combining machine learning with decision making (2011, methodology): “Marseguerra et al. (2002) introduces a model for degradation leading to failure for a continuous complex system, and use Monte Carlo simulations to determine the optimal degradation level to perform an inspection.”