Optimal Highway Maintenance Policies Under Uncertainty

International audienceWe develop an inspection and maintenance policy to minimize the cost of maintaining a given section of road or highway when there is a great deal of uncertainty in the degradation process. We propose to model the degradation of a section of road based on the proliferation and growth of cracks. We utilize a combination of a Poisson and gamma process to account for the tremendous amount of uncertainty and difficulty in predicting the proliferation of cracks. Our policy defines the optimal inspection interval as well as the minimum threshold at which to perform crack repairs. Furthermore, our policy contains a safety constraint to prevent the probability of a "catastrophic" failure from exceeding a pre-determined reliability value. Numerical calculations have shown that our model will extend the lifecycle of the road by performing preventive, conditioned-based maintenance to slow down the growth of cracks. Classical preventive maintenance policies usually shorten the lifecycle by forcing earlier renewals

Optimal Resurfacing Decisions for Road Maintenance : A POMDP Perspective

International audienceWe develop an optimal maintenance policy for a road section to minimize the total maintenance cost over the infinite horizon when some deterioration and decision parameters are not observable. Both perfect and imperfect maintenance actions are possible through the application of various thicknesses of resurfacing layers. We use a two-phase deterioration process based on two parameters: the longitudinal cracking percentage and the deterioration growth rate. Our deterioration model is a state-based model based on the state-dependent Gamma process for the longitudinal cracking percentage and the Bilateral Gamma process for the deterioration growth rate. Moreover the maintenance decision is constrained by a maximum road thickness that makes the maintenance decisions more complex as it becomes how much surface layer to add as well as to remove. Because only one of the two deterioration parameters is observable, we formulate the problem as a partially observed Markov decision process and solve it using a grid-based value iteration algorithm. Numerical examples have shown that our model provides a preventive maintenance policy that slows down the initiation as well as the propagation of longitudinal cracks and that may ameliorate the road state to a better than as-good-as-new one by altering its composition through additive resurfacing layers

How to take into account potential change of deterioration mode in Condition-Based Maintenance decision rule

International audienceClassical results in maintenance optimization are based on the characterization of an average system degradation behaviour. The main reasons of such approaches are the robustness of the decisions with respect to the system state and the time horizon. By cons, the ''smoothing'' effect in existing degradation models and the ''generic'' aspect of the decision affect the decision quality as these models do not take into account potential changes in deterioration modes. Such consideration can be particularly valuable in a safety context. We propose here a condition-based maintenance approach which adapts the maintenance decision according to the degradation behaviour updated using current state observations. First, we propose to extend the system state definition, usually limited to an indicator of observable degradation by adding information related to the speed of deterioration referred hereafter as potential of degradation or deterioration growth rate. Moreover, we propose to take benefit of current observations as well as performed actions to update the degradation laws. More than the model fitness improvement, another advantage of our approach is the proposition of a more realistic modelling of the maintenance impact onto the future behaviour of the system. However, the introduction of the potential of degradation as a new decision parameter does not prejudge an intuitive structure for the decision policy. Moreover, as the classical concept of the failure rate, the potential of degradation is non observable. which implies more efforts in both optimization modelling and solution procedure. We highlight the structural properties of the optimization problem which ensure an optimal control limit policy. We will conclude our communication by the illustration of the results of our maintenance model in a pavement management context

Optimisation de la politique de maintenance pour un système à dégradation graduelle stressé

International audienceThis paper investigates a maintenance policy allowing the maintenance cost optimization per unit of time combining statistical process control (SPC) and condition-based maintenance (CBM) policy. We consider a single-unit system with two failure modes which can be partially explained by several covariates. Failure modes are a continuous-state deterioration and a stress. A CBM policy is used for inspecting and replacing the system in order to balance the impacts of an excessive deterioration level whereas a control a classical control chart is used to monitor the stress covariate. Sensitivity analysis based on numerical results is proposed

Optimisation de la politique de maintenance pour un système à dégradation graduelle stressé

International audienceThis paper investigates a maintenance policy allowing the maintenance cost optimization per unit of time combining statistical process control (SPC) and condition-based maintenance (CBM) policy. We consider a single-unit system with two failure modes which can be partially explained by several covariates. Failure modes are a continuous-state deterioration and a stress. A CBM policy is used for inspecting and replacing the system in order to balance the impacts of an excessive deterioration level whereas a control a classical control chart is used to monitor the stress covariate. Sensitivity analysis based on numerical results is proposed

Optimizing Road Milling and Resurfacing Actions

International audienceA condition-based maintenance optimization approach is developed for the road-cracking problem in order to derive optimal maintenance policies that minimize a total discounted maintenance cost. The approach is based on a Markov decision process that takes into ac- count multiple actions with varying effects on future road performance. Maintaining the road consists of adding a new asphalt layer; however, as resurfacing actions are constrained by a maximum total road thickness, the maintenance decision is not only how thick a layer to apply, but also how much old road to remove. Each combination of these actions leads to different maintenance costs and different future degradation behaviours. The road state is modelled by a dependent bivariate deterioration variable (the longitudinal cracking percentage and the deterioration growth rate), for taking these different changes in the cracking patterns into account. Moreover, the sensitivity to cracking for existing roads can be reduced with the addition of new layers, and thus actions that can lead to states better than good-as-new have to be considered. A numerical analysis is provided to illustrate the benefits of the introduction of the deterioration speed in the decision framework, as well as the belief that initially building a road to its maximum thickness is not optimal. The trade-offs in the design decisions and the exploitation/maintenance costs are also explored

Predictive maintenance policy for a gradually deteriorating system subject to stress

International audienceThis paper deals with a predictive maintenance policy for a continuously deteriorating system subject to stress. We consider a system with two failure mechanisms which are, respectively, due to an excessive deterioration level and a shock. To optimize the maintenance policy of the system, an approach combining statistical process control (SPC) and condition-based maintenance (CBM) is proposed. CBM policy is used to inspect and replace the system according to the observed deterioration level. SPC is used to monitor the stress covariate. In order to assess the performance of the proposed maintenance policy and to minimize the long-run expected maintenance cost per unit of time, a mathematical model for the maintained system cost is derived. Analysis based on numerical results are conducted to highlight the properties of the proposed maintenance policy in respect to the different maintenance parameters

Impact of maintenance on replacement investment under technological improvement

International audienceAn unexplored important area in the equipment investment problem under technological improvement is the impact of maintenance policy. In fact, maintenance not only helps to maximize the profitability of the asset, but also prolong its economic life while waiting the apparition of better technology in the near future. Therefore, we propose a model that allows us to consider how replacement investment in a new or improved asset will be influenced by maintenance. The investment decisions are based on information about the profitability of the current asset and the technological environment. For the maintenance process, we also consider the dependency of its cost and efficiency on the deterioration state of asset that is represented by a profit parameter. We use a non-stationary Markov decision process to solve for the optimal investment/maintenance policy and illustrate the potential benefits of integrating maintenance policies in the investment strategy through different numerical analysis

Optimal Maintenance and Replacement Decisions under Technological Change

International audienceThe requirement of equipment improvement in order to satisfy the safety and reliability of system motivates the development of technology. The presence or expectation of technologically better equipment will influence managerial decisions on whether to invest in the maintenance of current equipment, invest in replacement with an equivalent model, replacement with a higher technology model currently available on the market, or wait for a potentially even better technology to appear in the near future. Hence, the consideration of technological change is a very important aspect for maintenance and replacement decisions. This paper aims to define a model that allows us to gain insight into how maintenance/replacement policies will be influenced by the expectation of future technology. We then use stochastic dynamic programming (i.e., Markov decision process) to solve for the optimal maintenance and replacement policy of the equipment as a function of performance and cost. Finally, we illustrate the problem through several numerical example

Application of a Bivariate Deterioration Model for a Pavement Management Optimization

AbstractThis work is a part of a research project for the development of new condition-based approaches for road maintenance optimization. The degradation pattern on interest is here the longitudinal cracking process due to cumulative fatigue (the traffic repetition leads to the occurrence of cracks in the road basement which grows up to the road surface). In this context, we have proposed a new theoretical deterioration model based on two dependent indicators -the observable deterioration measurement and the potential deterioration growth. Even if the construction of the model is based on practical considerations, its application in an operational context remains difficult. The objective of this communication is to study the applicability of such a model and propose some improvements. After analyzing the original model to highlight its strengths and limitations, we propose to revisit the definitions of the decision parameters while specifying the construction of the associated functions. A statistical inference procedure is discussed. A numerical example based on the original maintenance model is presented to illustrate the benefits of this approach that we will present as some “best practices” for future road pavement management