C
Christophe Bérenguer
Researcher at University of Grenoble
Publications - 229
Citations - 5695
Christophe Bérenguer is an academic researcher from University of Grenoble. The author has contributed to research in topics: Condition-based maintenance & Predictive maintenance. The author has an hindex of 35, co-authored 216 publications receiving 4943 citations. Previous affiliations of Christophe Bérenguer include University of Trinidad and Tobago & Centre national de la recherche scientifique.
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A condition-based maintenance policy for stochastically deteriorating systems
TL;DR: A mathematical model for the maintained system cost is derived and it is confirmed that the maintenance cost rate on an infinite horizon can be minimized by a joint optimization of the maintenance structure thresholds, or equivalently by a Joint optimization of a system replacement threshold and the aperiodic inspection schedule.
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Continuous-time predictive-maintenance scheduling for a deteriorating system
TL;DR: A predictive-maintenance structure for a gradually deteriorating single- unit system (continuous time/continuous state) and shows its adaptability to different possible characteristics of the maintained single-unit system.
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A condition-based maintenance policy with non-periodic inspections for a two-unit series system
TL;DR: A stochastic model is developed on the basis of the semi-regenerative properties of the maintained system state and the associated cost model is used to assess and optimize the performance of the maintenance model.
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Sequential condition-based maintenance scheduling for a deteriorating system
TL;DR: A new probabilistic method based on the semi-regenerative property of the evolution process is developed in order to calculate the long-time expected cost per unit of time using a recent result generalizing the well-known theorem usually used.
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A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events
TL;DR: A condition-based periodic inspection/replacement policy is developed and compared with a benchmark time-based block replacement policy, showing that it is indeed useful to follow closely the actual evolution of the system to adapt the maintenance decisions to the true system state to improve the performance of maintenance policies.