A sequential condition‐based repair/replacement policy with non‐periodic inspections for a system subject to continuous wear

Abstract

SUMMARYThis paper studies a condition-based maintenance policy for a repairable system subject to a continuousstate gradual deterioration monitored by sequential non-periodic inspections. The system can be maintained using different maintenance operations (partial repair, as good as new replacement) with different effects (on the system state), costs and durations. A parametric decision framework (multithreshold policy) is proposed to choose sequentially the best maintenance actions and to schedule the future inspections, using the on-line monitoring information on the system deterioration level gained from the current inspection. Taking advantage of the semi-regenerative (or Markov renewal) properties of the maintained system state, we construct a stochastic model of the time behaviour of the maintained system at steady state. This stochastic model allows to evaluate several performance criteria for the maintenance policy such as the long-run system availability and the long-run expected maintenance cost. Numerical experiments illustrate the behaviour of the proposed condition-based maintenance policy.

Choosing the optimal intervention method to reduce human-related machine failures

“…But their scope is on production scheduling and not on failure risk analysis. Castanier, Berenguer, and Grall (2003), discuss a continuously deteriorating machine where each maintenance operation makes sense at various stages. The DM can choose to run-as-normal, perform preventive repairs, or preventive replacement.…”

Conventional approaches to the modelling of a dysfunctional process in the context of maintenance activity

“…C. Meier-Himer [22] proposes an approach to estimate the parameters of lifetime laws of catenary parts using a statistical method based on the Markov chains and Markov processes jumps. B. Castanier [23] proposes a condition-based maintenance policy for a repairable system subject to a continuous state gradual deterioration monitored by sequential non-periodic inspections. This approach makes it's possible to study the reliability of these parts.…”

Interacting multiple-models, state augmented Particle Filtering for fault diagnostics

“…However, notice that, this work, as well as that in [38] which investigates the potential of such algorithms, has addressed case studies with only two models, additive Gaussian noise, and abnormal conditions where a sharp and abrupt jump in the measured variables is observed. These conditions are rarely verified in practice, when the fault detection and diagnosis concerns a gradually degrading industrial component [11].…”