Improving Reliability of Complex Systems Using Analyses Obtained Through Design Structure Matrix and Interactive Failure Detection Procedures

The process of development and expansion of advanced industries reveals the need to implement more and more predictive methods and mechanisms in readiness to deal with possible failures. With complexities inherent in systems, having a proper and all-embracing model of the entirety of a system is not readily possible. Design structure matrices (DSMs) are regarded as great (a great) help in communicating, comparing, and integrating partial system models. Given that there are numerous relationships among subsystems in complex systems, it is expected that interactive failures occur giving rise to diverse problems as well as gradual or abrupt failures in the system. Correlational dependent (Correlational-dependent) failures, commonly known as interactive failures, most frequently occur in mechanical systems. In this study, we have exploited DSM for identifying interactive failures and the relationships existing among different components in complex systems. The latter matrix is generally used in industries for observing the strengths of existing relationships among interacting elements. From another perspective, by analyzing the relationships among elements and identifying coils and curls, it is possible to investigate the existing nodes in loops. Implementing this procedure leads to identifying critical components and interactive failures, eventually bringing about enhanced reliability in the system. The present paper, while considering prevailing methods adopted in previous studies for selecting critical parts and subsystems, proposes a new method for selecting critical parts so as (delete so as) to increase the reliability rates. The method set forth is derived from the Markov chain model in addition to employing mathematical methods in matrices. hypothetical system. The relationships in this system in graphs and matrices. The hypothetical system exhibited to substantiate the assumptions and the matrix methods and the Markov chain stochastic model employed.