Reliability Assessment of a Multi-Redundant Repairable Mechatronic System

The reliability modelling of redundant systems is an important step to estimate the ability of a system to meet the required specifications. Markov chains have characteristics making it very simple the graphic representation of this type of model. They however have the disadvantage of being quickly unworkable because of the size of the matrices to be manipulated when systems become complex in terms of number of components or states. This issue, known as of combinatorial explosion is discussed in this chapter. Two methods are proposed. The first one uses the concept of decoupling between phenomena driven by different dynamics. The second is based on a principle of iteration after cutting the model into classes of membership. Both are based on the principles of approximating the exact result by reducing the scale of the problem to be solved. A case study is eventually carried out, dealing with the reliability modelling and assessing of a mechatronic subsystem used for an Unmanned Aerial Vehicle flight control with a triple modular redundancy. Results are discussed.

[1]  Matthieu Godichaud,et al.  Disassembly process planning using Bayesian network , 2010, WCE 2010.

[2]  Mirko Vujosevic,et al.  Reliability Evaluation and Optimization of Redundant Dynamic Systems , 1985, IEEE Transactions on Reliability.

[3]  A. Doucet,et al.  Particle Markov chain Monte Carlo methods , 2010 .

[4]  Suprasad V. Amari,et al.  Redundancy optimization problem with warm-standby redundancy , 2010, 2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS).

[5]  Benoît Iung,et al.  Sustainable management of end-of-life systems , 2012 .

[6]  Wen-Hua Chen,et al.  Fast mission reliability prediction for Unmanned Aerial Vehicles , 2013, Reliab. Eng. Syst. Saf..

[7]  Persi Diaconis,et al.  The Markov chain Monte Carlo revolution , 2008 .

[8]  Peter Buchholz Product Form Approximations for Communicating Markov Processes , 2008, 2008 Fifth International Conference on Quantitative Evaluation of Systems.

[9]  Andrew L. Reibman,et al.  Characterizing a lumping heuristic for a Markov network reliability model , 1993, FTCS-23 The Twenty-Third International Symposium on Fault-Tolerant Computing.

[10]  S. Albright,et al.  Steady-state approximations for a multi-echelon multi-indentured repairable-item inventory system , 1992 .

[11]  Mustapha Nourelfath,et al.  Joint redundancy and imperfect preventive maintenance optimization for series-parallel multi-state degraded systems , 2012, Reliab. Eng. Syst. Saf..

[12]  P. Taylor Algebraic criteria for extended product form in generalised semi-Markov processes , 1992 .

[13]  D. J. White,et al.  A Survey of Applications of Markov Decision Processes , 1993 .

[14]  Dimitris Kiritsis,et al.  Engineering Asset Lifecycle Management , 2010 .

[15]  F. Peres,et al.  Design methods applied to the selection of a rapid prototyping resource , 1999, 1999 7th IEEE International Conference on Emerging Technologies and Factory Automation. Proceedings ETFA '99 (Cat. No.99TH8467).

[16]  John G. Kemeny,et al.  Excessive functions of continuous time Markov chains , 1967 .

[17]  J. Nahman Iterative Method for Steady State Reliability Analysis of Complex Markov Systems , 1984, IEEE Transactions on Reliability.

[18]  Beichelt Frank,et al.  IEEE Trans. Reliab. , 1992 .

[19]  Ioannis A. Papazoglou,et al.  Markov Processes for Reliability Analyses of Large Systems , 1977, IEEE Transactions on Reliability.

[20]  Johann Christoph Strelen,et al.  Approximate Product Form Solutions for Markov Chains , 1997, Perform. Evaluation.