American Journal of Intelligent Systems 2012, 2(5): 82-92 DOI: 10.5923/j.ajis.20120205.02 Performance Evaluation and Optimisation of Industrial System in a Dynamic Maintenance Smaïl Adjerid * , Toufik Aggab, Djamel Benazzouz Solid Mechanics and Systems Laboratory (LMSS), M’Hamed Bougara University (UMBB), Boumerdes 35000, Algeria Abstract Despite the existence of the multitude of behavioral analysis tools for industrial systems, increasingly complex, managers to date find difficulties to define maintenance strategies able to significantly improve the overall performance of companies in terms of production, quality, safety and environment. A static maintenance and not adapted to the evolution of the state system does not meet the expectations of industrialists. However, the behavior of any degradable system is closely related to the state of its components. This random influence is not always sufficiently considered for various reasons, consequently any decision making remains subjective. Our approach based on dynamic Bayesian networks (DBN) consists has the modeling of the system and the functional dependencies of its components. The results obtained then, after the introduction in the model of the most appropriate actions of maintenance show all the importance of this technique and the possible applications. Keywords Performance Evaluation, Reliability, Dynamic Maintenance Strategy, Bayesian Network 1. Introduction The functioning reliability evaluation of an industrial system in operating mode consists of analyzing failures component to estimate their impact on the service provided by the system [1], [2] and [3]. A particularity in characterizing a number of industrial systems is that their behavior varies as function of time due to interactions between components of the system or as function of the environment. Thus, we are talking about dynamic reliability [4], [5], which render conventional methods of functioning reliability static and ineffective. According to [6] the dynamic reliability is the predictive evaluation reliability of a system, whose reliability structure expresses how the system failure depends on the failures of its components, evolves dynamically over time. Thus, an industrial system will evolve physically over time in nominal operation, in a degraded or failed mode. The maintenance and the process organization which allow the functioning of a system will have an impact on its performance. In general, the efficiency evaluation of the maintenance action can be done by estimated methods based on feedback experience knowledge or Bayesian methods based on expert opinions [7], [8]. Bayesian networks (BN) are based on graph theory and probability theory. They allow to represent intuitively * Corresponding author: smailadjerid@gmail.com (Smaïl Adjerid) Published online at http://journal.sapub.org/ajis Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved systems whose state evolves in a non- deterministic manner [9]. In addition, they have the ability to integrate in the same model various kinds of knowledge (Rex, expertise and observation). However, the BN formalism does not allow to represent systems evolving over time i.e that contain variables whose conditional probability table (CPT) in present time which depends on past information [10], [11]. This problematic type led us to use dynamic Bayesian networks (DBN). The DBN are an extension of BN which model a stochastic processes varying over time. In addition statistical nodes used in conventional BN, DBN introduce a new type of nodes called temporal nodes to model discrete random variables depending on time [12], [13] and [14]. The objective of this paper is to evaluate the system reliability without maintenance intervention, then show what will be the efficiency of the maintenance strategy of the same system. The performance evaluation model for a maintained system involves the following steps: - modeling the system fro m its topo-functional decomposition in components, - modeling the degradation modes of various components, - modeling functional dependencies between components and their effects on the process of the system by measuring the safety parameter, - identification and incorporation of maintenance strategies and system performance evaluation. 2. Bayesian Approach for a Degradable System