چکیده:
Reliability is one of the most important factors of complex systems which play a crucial role in the performance of modern systems. In this study, a novel algorithm for estimating reliability of ready-to-use systems in designing phase for designed lifetime is proposed. At the first stage, the related studies are checked, and then fundamental theories of each section are presented. With reference to the particular structure of ready-to-use systems and Markov Chain conditions, a new model based on Markov method and Fuzzy approach is suggested. The performance of proposed model is validated by testing on a real system. Therefore, the reliability and mean time to failure of the industrial system is estimated by the algorithm. Finally, practical suggestions are recommended for optimizing the system reliability.
خلاصه ماشینی:
com A Novel Algorithm for Estimating Reliability of Ready-to-use Systems in Designing Phase for Designed Lifetime Based on Markov Method and Fuzzy Approach Youness Javid *,a, Mostafa Abouei Ardakan a Mohammad Yaghtin a and Mohammad N.
With reference to the particular structure of ready-to-use systems and Markov Chain conditions, a new model based on Markov method and Fuzzy approach is suggested.
Reliability; Ready-to-use systems; Markov chain; Fuzzy theory; Designed lifetime; Designing phase 1.
Lu & Wu (2014) considered Markov chain model for predicting the failure and repair time of each component, in order to evaluate the reliability of - Corresponding author email address: Youness.
Then for calculating the marginal probability values of FTA, similar failure data of the system is used by referring to reference documents in intended industry Then, the exact obtained results are converted into triangular fuzzy numbers as follows (Verma, A.
Series Bayesian Network: OR node In the following, since failures are considered independent of one another (assumption number 4), a joint distribution function can be obtained to calculate the probability of failure.
(View the image of this page) After plotting the Fault Tree for each subsystem and mapping it to the Bayesian network and collecting data from similar system’s failure data and referring to reference documents in intended industry to determine the marginal probabilities and converting the data into fuzzy numbers, we calculate the failure rate for each mission in accordance to (8).