Phuc Do

A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions

This paper deals with a proactive condition-based maintenance (CBM) considering both perfect and imperfect maintenance actions for a deteriorating system. Perfect maintenance actions restore completely the system to the ‘as good as new’ state. Their related cost are however often high. The first objective of the paper is to investigate the impacts of imperfect maintenance actions. In fact, both positive and negative impacts are considered. Positive impact means that the imperfect maintenance cost is usually low. Negative impact implies that (i) the imperfect maintenance restores a system to a state between good-as-new and bad-as-old and (ii) each imperfect preventive action may accelerate the speed of the system׳s deterioration process. The second objective of the paper is to propose an adaptive maintenance policy which can help to select optimally maintenance actions (perfect or imperfect actions), if needed, at each inspection time. Moreover, the time interval between two successive inspection points is determined according to a remaining useful life (RUL) based-inspection policy. To illustrate the use of the proposed maintenance policy, a numerical example finally is introduced.

Multi-level predictive maintenance for multi-component systems

In this paper, a novel predictive maintenance policy with multi-level decision-making is proposed for multi-component system with complex structure. The main idea is to propose a decision-making process considered on two levels: system level and component one. The goal of the decision rules at the system level is to address if preventive maintenance actions are needed regarding the predictive reliability of the system. At component level the decision rules aim at identifying optimally a group of several components to be preventively maintained when preventive maintenance is trigged due to the system level decision. Selecting optimal components is based on a cost-based group improvement factor taking into account the predictive reliability of the components, the economic dependencies as well as the location of the components in the system. Moreover, a cost model is developed to find the optimal maintenance decision variables. A 14-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance policy. Different sensitivity analysis are also investigated and discussed. Indeed, the proposed policy provides more flexibility in maintenance decision-making for complex structure systems, hence leading to significant profits in terms of maintenance cost when compared with existing 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.

A Stationary Grouping Maintenance Strategy Using Mean Residual Life and the Birnbaum Importance Measure for Complex Structures

This paper presents a stationary grouping maintenance strategy for multi-component systems whose structure could be a mixture of the basic structures such as series structures, parallel structures, k-out-of n, etc. The proposed preventive maintenance decision-making rules are based on the joint consideration of the mean remaining lifetime (MRL), which represents the mean lifetime left before the failure of components, and Birnbaums importance measure providing the important role of the components in the system structure. For corrective maintenance actions, adaptive corrective decision rules taking into account the criticality of components are introduced. Besides, a cost model considering economic and structural dependences among components are also developed. Moreover, to find the optimal maintenance plan, both analytical calculation and hybrid simulation-analysis are herein proposed. Finally, the use and the advantages of the proposed grouping strategy are illustrated through a case study of a substation automation system.

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.

Integrating energy efficiency-based prognostic approaches into energy management systems of base stations

Telecommunication industry is predicted to have an important role in total energy consumption of industry area. Thus, the increasing energy cost in operational costs demands effective tools to identify energy efficiency indicators (EEIs) and predict the evolution of energy efficiency performance (EEP). The energy efficiency (EE) improvement of any segment in the communication networks (CNs) can help lower energy cost and protect environment of network operators. The spread of users in overall areas results in the increasing number of base stations (BSs), which are main components of CN. As a supporting function of management tools, predicting EEP is a requested function of energy management system (EMS) by network operators. In this context, this paper presents the demand of EMS for integrating prediction of EEP deterioration. In addition, the energy efficiency function block to modelling the BSs with numerous of indicators is proposed. By prognostic approaches (PA) and suitable aggregation methods, network operators can handle their situations. An EEP degradation demonstration has been conducted by using PA and EE model of BS. An additional benefit can also be seen as increasing reliability of energy backup units in various scenarios of power source disturbance. Finally, the requirement of sensor networks in acquiring technical data of BS deterioration states is mentioned.

A Study on Health Diagnosis and Prognosis of an Industrial Diesel Motor: Hidden Markov Models and Particle Filter Approach

The paper presents a study on health diagnosis and prognosis of an industrial diesel motor. Two well-known approaches, Hidden Markov Model (HMM) and particle filter (PF), are applied from real recorded data with different measurements. The recorded data is firstly pre-processed and health indicator is then chosen before implementing each used approach. The obtained results are analyzed and discussed. The use and advantages of each approach are finally highlighted.

Prognostics on energy efficiency performance for maintenance decision-making: Application to industrial platform TELMA

Industrial plants are facing today with new challenges to well optimize performance of their operation and maintenance. For example, the sustainability paradigm is introducing new requirements to be taken into account in the decision-making process. In that way, energy consumption (EC) and energy efficiency (EE) are two critical performances impacting severely the plant effectiveness mainly with regards to its life cycle cost. Although there are models for following these two performances at the component level, there is a real need for modelling them at the function or system levels not only to support strategic decisions (and not only operational one) but also to forecast them to make decisions in advance for better optimization. Thus, the principles of a generic approach, which is focused on EE performance (EEP) and built on the modelling of this EEP at functional level, and its prognostics to calculate a Remaining Energy-Efficient Lifetime (REEL), are proposed in this paper. The REEL should integrate future mission profiles and operation conditions. The prognostics model is developed from a data-driven approach by using a nonlinear regression method. This generic approach is instantiated and validated on the TELMA platform (a motor-driven system) which is simulating a real industrial plant addressing unwinding metal bobbins. So, models are built from field data of two independent motors (the component level and electrical energy) in addition to data on the function supported by means of these two motors. It leads to prognostics models usable to predict the EE evolution - REEL (the input of the decision-making module) both at the component level from the relationships between speed performance (motor output), bearing deterioration (Gamma process) and EE, and at the functional level from the relationships between productivity performance (functional output on the product delivered), components deterioration level and EE.

Condition-based maintenance for a two-component system with stochastic and economic dependencies

This paper develops a model of a condition-based maintenance policy for a two-component system with both stochastic and economic dependencies. The stochastic dependency is such that the degradation rate of each component depends not only on its own state (degradation level) but also on the state of the other component. The economic dependency is such that combining multiple maintenance activities has lower cost than performing maintenance on components separately. To select a component or components to be preventively maintained, adaptive preventive maintenance and opportunistic maintenance rules are proposed. A cost model is developed to find the optimal values of decision variables. A case study of a gearbox system demonstrates the utility of the proposed model.