Preventive maintenance scheduling for a multi-component system with non-negligible replacement time

Laggoune, Radouane; Chateauneuf, Alaa; Aı̈ssani, Djamil

doi:10.1080/00207720903230765

Cited by 34 publications

(11 citation statements)

References 14 publications

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“…However, for economic, logistic and technical reasons, the periodic aspect of PR actions has to be properly managed. In fact, many manufacturers and researchers tried to group PM actions for economic reasons . As a result, the periodicity of the PR types should be a multiple of the PR periodicity T (Figure ).…”

Section: Implemented Maintenance Policymentioning

confidence: 99%

Imperfect Preventive Maintenance Policy for Complex Systems Based on Bayesian Networks

Mokhtar

Laggoune

Chateauneuf

2016

Quality & Reliability Eng

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The main challenge in maintenance planning lies in the realistic modeling of the maintenance policy. This paper is focused on the maintenance optimization of complex repairable systems using Bayesian networks. A new policy is developed for periodic imperfect preventive maintenance policy with minimal repair at failure; this policy allows us to take into consideration several types of preventive maintenance with different efficiency levels. The Bayesian networks are used for complex system modeling, allowing the evaluation of the model parameters. The Weibull parameters and the maintenance efficiency are evaluated thanks to the proposed methodology using Bayesian inference. The approach developed in this paper is applied on a real system, to determine the optimal maintenance plan for a turbo-pump in oil industry. in Wiley Online Library Model formulationWe note by τ the operating time (i.e. up-time) over the time interval T. Each PR type reduces the failure rate of the system by an amount, which is the same for each PR type, but different from one PR type to the other. Let the initial failure rate function of the system be noted λ 0 (τ), the PM policy is described as follows ( Figure 1):The proposed optimization approach, represented in Figure 4, is based on the main following steps:1. The studied system is modeled as a BN by using the corresponding FT, the expert judgment, or both. In this BN, all the system components are represented by root nodes and connected to the other nodes, in order to represent and quantify the dependencies between the system components. The other events, which can affect the system such as the environment or functioning conditions, can be added to the BN. 2. The prior probability tables of all the root nodes are generated using Equation (11) or expert judgment if the corresponding node does not represent a system component. The CPT are generated from the FT gate and the expert judgment. 3. The CDF of the whole system is obtained from Bayesian inference, and its corresponding parameters are assessed by using linear regression (Equation (12)). 7574. The PM efficiency factors are computed for each PR type, from Equation (16). 5. The optimal time interval between two successive PM actions (i.e. τ *) is obtained by minimizing the expected maintenance cost per unit time (Equation (9)). This step is performed for several values of K. For large systems, the use of combinatory optimization algorithms can be recommended. 6. The optimal PM plan corresponds to the couple (τ *, K), which leads to the lowest maintenance cost.One can see that the use of BNs in the optimization approach allows the combination of historical data with expert judgment for the estimation of reliability and maintenance efficiency parameters. In addition, the approach can be applied for complex systems with the consideration of several PM types with specific effect for each PM type on one hand, and the consideration of the system components on the other hand. Industrial applicationIn order to validate the proposed model, the...

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Section: Implemented Maintenance Policymentioning

confidence: 99%

Imperfect Preventive Maintenance Policy for Complex Systems Based on Bayesian Networks

Mokhtar

Laggoune

Chateauneuf

2016

Quality & Reliability Eng

Self Cite

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“…According to the well-known formulation in maintenance planning, the expected total cost per unit time is given by [15] …”

Section: Cost Modelmentioning

confidence: 99%

Reliability analysis of screwed connections in high-speed trains, considering fatigue, corrosion, and imperfect maintenance operations

Chateauneuf

Cocheteux

Deffarges³

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part O:

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The reliability of railway equipment is of central interest in the choice of design and maintenance strategies. In high-speed trains, known as TGV, the assembly of mechanical parts is performed by the use of prestressed screws. In order to ensure safe operation of the railway network, the French company SNCF has undertaken a reliability-based analysis of the life cycle of screwed connections. This study aims at establishing a consistent reliability-based methodology for the assessment of equipments subjected to fatigue, corrosion, and imperfect maintenance operations, in order to improve their inspection on the basis of cost and reliability balancing. This methodology incorporates the life cycle of the equipment, including the deterioration modes, the imperfect maintenance operations and the uncertainties in geometry, loading, and material properties. The results of the study show that despite the high resistance regarding the applied load, the connection is still very sensitive to parameter variations: friction, tightening and prestressing. Although the large safety margin in fatigue design, the robustness of screwed connections should be sought when operation and maintenance conditions are considered.

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“…In the past decades, maintenance policies for repairable systems have been extensively studied, e.g. Ben-Daya, Duffuaa, and Raouf (2000), Laggoune, Chateauneuf, and Aissani (2010), Sheu and Chang (2010), Fan, Hu, Chen, andZhou (2011), andLiao (2013). Several excellent reviews are provided by Valdez-Flores and Feldman (1989), Wang (2002), and Jardine, Lin, and Banjevic (2006).…”

Section: Introductionmentioning

confidence: 98%

Optimally maintaining a multi-state system with limited imperfect preventive repairs

Fan¹,

Xu²,

Chen³

2013

International Journal of Systems Science

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In this paper, the optimal maintenance policy for a multi-state system with no observation is considered. Different from most existing works, only a limited number of imperfect preventive maintenance actions can be performed between two successive replacements. Assume that the system's deterioration state cannot be observed during its operation expected after each replacement, and it evolves as a discrete-time Markov chain with a finite state space. After choosing the information state as state variable, the problem is then formulated as a Markov decision process over the infinite time horizon. In order to increase the computational efficiency, several key structural properties are developed by minimising the total expected cost per unit time. The existence of the optimal threshold-type maintenance policy is proved and the monotonicity of the threshold is obtained. Finally, a numerical example is given to illustrate the optimal policy.

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Preventive maintenance scheduling for a multi-component system with non-negligible replacement time

Cited by 34 publications

References 14 publications

Imperfect Preventive Maintenance Policy for Complex Systems Based on Bayesian Networks

Imperfect Preventive Maintenance Policy for Complex Systems Based on Bayesian Networks

Reliability analysis of screwed connections in high-speed trains, considering fatigue, corrosion, and imperfect maintenance operations

Optimally maintaining a multi-state system with limited imperfect preventive repairs

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