Optimization of an aperiodic sequential inspection and condition-based maintenance policy driven by value of information

Abstract

The issue of the optimal planning of inspection and maintenance actions for a randomly deteriorating system constitutes a difficult sequential decision-making problem in which the objective is generally to achieve minimal life-cycle cost. For mathematical tractability, most approaches rely either on the consideration of specific maintenance strategies, e.g. Periodic Inspection and Replacement (PIR), whose defining parameters are optimized, or on time-and-space-state discretization using Markov Decision Process (MDP) models and resolution through policy iteration. In both cases, optimality may be hard to guarantee. In this paper, the decision-theoretic concept of Value of Information (VoI) is used as a metric to guide resource prioritization in time, that is, to schedule inspections in a piecewise optimal manner. An aperiodic sequential inspection policy is proposed, where the determination of the next best time for inspection, or replacement, is based on the current condition and on the computed expected gain from possible inspections, i.e. on a VoI metric. This policy can be implemented when the current condition is known from imperfect inspection or processing of condition-monitoring data. Also, more generally, a discussion is proposed on the use of VoI as a guide for information collection in life-cycle management.

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

“…Recent progress in computational speed, and the application of Machine Learning on building a reasonably accurate digital twin for faster evaluation of degradation curves [17,18], has catalyzed research in numerous areas of optimal maintenance like condition-based maintenance policy [19], the impact of imperfect maintenance [20,21], impact of uncertain inspection data and condition rating protocol [4], maintenance planning multicomponents system [22,23], inspection and maintenance for multi-state systems [24,25], maintenance for 𝑘-out-of-𝑛 systems [26,27], to name a few. The works by Fauriat et al [28] and Lin et al [29] utilize the Value of Information as a metric to guide the inspection policies such that the cost acquired over the life of the structure is minimal. Vega et al [30] discuss the application of data analytics and machine learning to maintenance decision-making for civil infrastructure.…”

On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

“…Thanks to the advancement of technology, especially in sensing and monitoring devices, CBM has indeed gained much attention over the last years [8]. While time-based PM schedules maintenance actions at fixed time-intervals, CBM reacts based on available information regarding the condition of a given component [9]. Due to its advantages many researchers have recently focused on CBM application to several fields [10][11][12][13][14].…”

Optimization of equal-cycle maintenance strategy considering imperfect preventive maintenance

“…[6][7][8] Preventive maintenance can be further divided into two categories according to the basis of formulating maintenance strategies, namely condition-based preventive maintenance and time-based preventive maintenance. 9 According to the current information of the equipment monitored by the sensor, condition-based preventive maintenance [10][11][12][13][14][15][16] judges whether the equipment is running abnormally, and repairs it before the equipment failure occurs, thereby reducing the probability of equipment failure. Theoretically, condition-based preventive maintenance uses more advanced technical means to get a more ideal maintenance effect.…”