Maintenance Decision Rule Research Articles

Optimization of preventive replacements dates and covariate inspections for repairable systems in varying environments

This study investigates the issue of optimal preventive replacement scheduling for a repairable system, considering its failure/repair history and its environment or degradation, as characterised by covariates. The proposed approach is developed within a recurrent event modelling framework, in which the failure behaviour of the system is defined by its failure intensity, and perfect preventive replacements and imperfect corrective repairs are integrated following a virtual age assumption. The observed heterogeneity between systems is integrated through covariates by following a proportional hazard assumption. These covariates are assumed to be either fixed and represent, for example, the manufacturer origin, or to be dynamic and represent the monitored degradation process.The contributions of this study are twofold. First, a dynamic condition-based preventive replacement policy is developed. The maintenance decision rule integrates the available information on both the corrective repair history and covariate behaviour to determine the optimal preventive replacement time. This policy extends the state-of-the-art model of Gilardoni et al. (2016), which considers only the corrective repair history without integrating covariates. Second, the results of this study demonstrate how different levels of knowledge and information regarding the covariates can be integrated into the maintenance decision and lead to different optimal replacement times that are associated with different cost performances of the preventive replacement policy. This performance evaluation enables the optimisation of the monitoring policy and inspection frequency of the covariates. The proposed approach, which follows steps of increasing complexity, is developed and investigated first on static and then on dynamic covariates, considering minimal and imperfect repairs.

Sensitivity of optimal replacement policies to lifetime parameter estimates

This paper analyzes three time-based replacement policies when the parameters of the time-to-failure distribution are unknown. Under the hypothesis of a large sample data, the unknown parameters are estimated via maximum likelihood method. The uncertainty around the point estimates impacts the maintenance decision rules. Based on the asymptotic normality of the parameter estimators and delta-method, the asymptotic distribution of the optimal decision parameter and the average cost is derived. This distribution permits to measure the uncertainty around the optimal maintenance decision rule induced by the point estimates. The sensitivity analysis of the replacement policies to the maintenance unit costs and lifetime distribution parameters is carried out. Application to two time-to-failure distributions is provided.

Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance

Maintenance and inventory optimizations are two interrelated processes but often investigated separately in the literature. Joint optimization of these processes can reduce the total maintenance and inventory cost, it may however lead to a complex problem with a large numbers of decision parameters to be optimized. To face this open issue, in this paper, we present a joint predictive maintenance and inventory strategy for systems with complex structure and multiple non-identical components. Both predictive maintenance and spare parts provisioning operations are studied and optimized jointly. The prognostic condition index and the structural importance measure of components are jointly used to build the thresholds for preventive maintenance and spare part ordering. Additionally, in order to take the advantages of economic dependence, opportunistic maintenance decision rules based on the criticality level of components and their spare parts availability are proposed. To evaluate the proposed joint strategy, a cost model taking into account the economic dependence of both maintenance and inventory activities is developed. A numerical study of a 7-component system is finally presented to illustrate the use and the advantages of the proposed strategy. Indeed, this strategy shows the flexibility and the efficiency in joint maintenance and inventory optimization.

Remaining useful lifetime estimation and noisy gamma deterioration process

In many industrial issues where safety, reliability, and availability are considered of first importance, the lifetime prediction is a basic requirement. In this paper, by developing a prognostic probabilistic approach, a remaining lifetime distribution is associated to the system or component under consideration. More particularly, the system׳s deterioration is modelled by a non-homogeneous gamma process. The model considers a noisy observed degradation data and by using the Gibbs sampling technique, the hidden degradation states are approximated and afterwards the system׳s remaining useful lifetime distribution is estimated. Our proposed prognosis method is applied to the Prognostic and Health Management (PHM) 2008 conference challenge data and the interest of our probabilistic model is highlighted. To point out the interest of the prognostic, a maintenance decision rule based on the remaining lifetime estimation results is proposed.

Controlled systems, failure prediction and maintenance

The paper treats the problem of the maintenance of controlled systems. The actuator of the system is subject to deterioration due to wear and to shocks caused by environmental changes. This latter is modeled by random variables called covariates. The deterioration of the actuator impact the control system and requires more effort to balance the deterioration impact and the controlled out put of the system. The main purpose of this paper is to propose a maintenance policy based on the residual lifetime of the system regulated by a Markovien command by taking into account the deterioration of the actuator. The originality of the paper is in one hand due to the consideration of a random deterioration of the actuator in the framework of a controlled system, and on the other hand the consideration of the remaining useful lifetime in the maintenance decision rule.

A condition-based maintenance policy for multi-component systems with Lévy copulas dependence

In this paper, we propose a new condition-based maintenance policy for multi-component systems taking into account stochastic and economic dependences. The stochastic dependence between components due to common environment is modelled by Lévy copulas. Its influence on the maintenance optimization is investigated with different dependence degrees. On the issue of economic dependence providing opportunities to group maintenance activities, a new maintenance decision rule is proposed which permits maintenance grouping. In order to evaluate the performance of the proposed maintenance policy, we compare it to the classical maintenance policies.

Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams

The paper deals with a maintenance grouping approach for multi-component systems whose components are connected in series. The considered systems are required to serve a sequence of missions with limited breaks/stoppage durations while maintenance teams (repairmen) are limited and may vary over time. The optimization of the maintenance grouping decision for such multi-component systems leads to a NP-complete problem. The aim of the paper is to propose and to optimize a dynamic maintenance decision rule on a rolling horizon. The heuristic optimization scheme for the maintenance decision is developed by implementing two optimization algorithms (genetic algorithm and MULTIFIT) to find an optimal maintenance planning under both availability and limited repairmen constraints. Thanks to the proposed maintenance approach, impacts of availability constraints or/and limited maintenance teams on the maintenance planning and grouping are highlighted. In addition, the proposed grouping approach allows also updating online the maintenance planning in dynamic contexts such as the change of required availability level and/or the change of repairmen over time. A numerical example of a 20-component system is introduced to illustrate the use and the advantages of the proposed approach in the maintenance optimization framework.

Condition-based maintenance for multi-component systems using importance measure and predictive information

This paper presents a predictive condition-based maintenance strategy for multi-component systems whose structure may impact components deterioration process. To select components for preventive maintenance actions, a decision rule relying on both structural importance measure of components and their predictive reliability that can be estimated at inspection times is proposed. For corrective maintenance actions, an adaptive opportunistic maintenance decision rule taking into account both the criticality level of components and logistic support constraints is introduced. Moreover, both economic and structure dependencies between components are studied and integrated in maintenance model. A 12-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance strategy. Indeed, the proposed strategy provides more flexibility in maintenance decision-making, hence leading to significant profits in terms of maintenance cost when compared to existing strategies.

Remaining Useful Life Estimation of Stochastically Deteriorating Feedback Control Systems with a Random Environment and Impact of Prognostic Result on the Maintenance Process

The objective and originality of this work are twofold. On one hand, it considers the degradation modeling and Remaining Useful Life (RUL) estimation for the closed-loop dynamic systems, which have not been addressed extensively in the literature. On the other hand, the paper examines how the prognosis result impacts the maintenance process. Indeed, due to their natural ageing and/or non desired effects of the operating condition, actuators deal with the loss of effectiveness which is a source of performance degradation of closedloop system. In this paper, we consider a control system with classical Proportional-Integral-Derivative controller and stochastically deteriorating actuator. It is assumed that the actuators are subject to shocks that occur randomly in time.An integrated model is proposed which jointly describes the states of the controlled process and the actuators degradation. The RUL can be estimated by a probabilistic approach which consists of two steps. First, the system state regarding the available information is estimated online by Particle Filtering method. Then, the RUL of the system is estimated by Monte Carlo simulation. To illustrate the approach and highlight the impact of the prognosis result on the maintenance process, a well-known simulated tank level control system is used. The maintenance decision rule is based on the quantiles of RUL histogram. In order to evaluate the performance of the maintenance policy, a cost model is developed.

Monitoring and condition-based maintenance with abrupt change in a system’s deterioration rate

In this paper, the maintenance of a system which undergoes a change in its deterioration rate is considered. The time of change of regime and the parameters after the change are unknown. To deal with unknown change time and unknown parameters, a detection procedure based on a suitable online change detection algorithm is used. The paper proposes a maintenance decision rule versus detection policy in order to minimise the long-run average maintenance cost, and the performances of this policy are studied through numerical implementations.

CBM Policy for an Online Continuously Monitored Deteriorating System with Random Change of Mode

The objective of online continuous monitoring is to improve the maintenance decision making of stochastically deteriorating systems which are subject to a sudden change in their degradation process. In this investigation, we considered different preventive replacement thresholds for different modes of degradation. An online continuous monitoring maintenance decision rule has been proposed, and since it takes into account only preventive maintenance, the decision variable is preventive replacement threshold. In order to assess the performance of the proposed maintenance policy and to minimize the long run maintenance cost per unit time, a mathematical model for the maintained system cost has been derived. Moreover, this has been compared with conventional single threshold for different modes for degradation. Numerical experiments illustrate the performance of the proposed policy and confirm that the maintenance cost rate can be minimized by optimizing preventive replacement threshold.

Pavement Network Maintenance Optimization Considering Multidimensional Condition Data

Pavement management systems inventory historical and current conditions of roadway networks, predict the future conditions of such networks, and suggest schedules for maintenance, repair, and rehabilitation activities. Such systems typically rely on a composite condition index, a one-dimensional and often discrete measure of the overall structural health and/or serviceability of pavement. The index is used during deterioration modeling, user and agency cost estimation, and selection and scheduling of maintenance activities. Pavement can suffer from a large number of related but distinct distresses. Difficulties associated with unobserved heterogeneity have hampered efforts to accurately model deterioration through composite condition indexes. At the same time, optimization techniques used to generate recommended maintenance plans have been shown both to be sensitive to deterioration model specification and to become computationally intractable as condition data increase. This research describes how a large network of related sections of pavement, each one of which may be plagued by a number of different distresses, can be managed without reducing condition data to a composite index. The use of approximate dynamic programming mitigates the curse of dimensionality that has haunted distinct Markov decision problem formulations of the maintenance optimization problem and limited their complexity. A computational study illustrates how the proposed approach leads to more sophisticated maintenance decision rules, which can be used to ensure the suggestions of pavement management systems more closely match engineering best practices. The use of multidimensional condition data can also yield more accurate deterioration models and cost estimates. The techniques introduced in this paper in the context of pavement management could easily be applied within any infrastructure management system.

Condition-based maintenance for a system subject to a non-homogeneous wear process with a wear rate transition

The aim of this paper is to propose an adaptive maintenance model for a gradually deteriorating system. The system considered initially deteriorates with a nominal deterioration rate and at an unknown time the system's deterioration rate changes and the new deterioration rate is a time-dependent function. To deal with the transition of mode of deterioration in the framework of the maintenance decision rule an adequate online change detection algorithm is used. The maintenance decision rule is chosen in order to minimise the total maintenance cost including the cost of unavailability. The main result of this paper is to point out the interest of using a detection algorithm and hence the appreciation of a decision rule which takes into account transitions in the deterioration rate.

Maintenance decision rule with embedded online Bayesian change detection for gradually non-stationary deteriorating systems

The aim of a condition-based maintenance policy is to manage the available online information about a component or a (sub)system, usually its degradation level, in order to improve the maintenance decision-making. This paper tackles the problem of maintenance decision rules for stochastically deteriorating systems that are subject to changes of their degradation rate during a life cycle. A well-suited control-limit maintenance decision rule is considered with an embedded online change detection algorithm. The maintenance decision and change detection parameters are optimized with respect to the same global maintenance cost and according to the available information about the degradation process. The obtained policy is compared with more classical control limit condition-based maintenance policies without online change detection. The use of the embedded online detection algorithm shows its efficiency especially for systems subject to significantly different degradation rates.

Online maintenance policy for a deteriorating system with random change of mode

Most of maintenance policies proposed in the literature for gradually deteriorating systems, consider a stationary deterioration process. This paper is an attempt to take into account stochastically deteriorating systems which are subject to a sudden change in their degradation process. A technical device subject to gradual degradation is considered. It is assumed that the level of degradation can be resumed by a single scalar variable. An online maintenance decision rule is proposed, which makes it possible to take into account in real time the online information available on the operating mode of the system as well as its actual deterioration level. We show the efficiency of considering online decision rules for maintenance with respect to traditional maintenance policies based on a static alarm threshold. Numerical simulations are given, to assess and optimize the performance of the maintained system from its asymptotic unavailability point of view. It is compared to the results obtained with classical control-limit maintenance policies.

Oil pipeline spill cause analysis

PurposeThe purpose of this paper is to develop an easy and robust tool to develop a decision support system (DSS) for the inspection staff of oil pipelines. The aim is to predict “the class” of each spillage, with respect to some relevant variables such as, mechanical failure or system malfunction. The management will then be able to define which pipelines to monitor and to choose the most suitable monitoring policies, based on the decision tree analysis outcome.Design/methodology/approachA non‐parametric technique based on rule induction is proposed for the identification of the expected spill cause category of cross‐country oil pipelines. In particular, the classification and regression trees approach is used to automatically generate inspection or maintenance decision rules. The analysis that is described is based on an extended database concerning information about spill cause category in cross‐country oil pipelines in Western Europe.FindingsThe proposed technique represents an interesting added value tool for the management. The proposed methodology extrapolates rules for determining the expected spill cause category of cross‐country pipelines, depending on the boundary conditions.Practical implicationsThe methodology here presented will assist maintenance managers of oil pipeline to better plan maintenance activity. In particular, the procedure makes it possible to determine which parts of a pipeline have to be submitted to a monitoring action or particular protection, with the aim of improving the efficiency and reducing the risk of spillages.Originality/valueEffective planning, coordination, and scheduling of the maintenance function can be, and for many years was, accomplished without computer support. The proposed procedure may be included in an information systems tool (sound Computerized Maintenance Information Management System (CMMIS)), for more efficient and effective maintenance/inspection scheduling activities.

A SUITABLE INSPECTION POLICY DEFINITION FOR SYSTEM WITH TWO MODES OF DEGRADATION 1

In this paper, a condition-based maintenance policy is proposed for a gradually deteriorating system with several modes of deterioration. The gamma process is used to describe the system degradation. The system deteriorates from a good as new state to the break down state (when the aging variable reaches a failure level). The level of deterioration can only be known by inspections and the time of change of degradation mode is not detected on line. The aim of the policy is to propose a suitable inspection schedule in every mode of deterioration as well as a related maintenance decision rule. A preventive maintenance is planned when the predicted deterioration level at the next inspection overpasses the failure level with a probability greater than a limit value.