Abstract
Multi-state weighted k-out-of-n systems are widely applied in various scenarios, such as multiple line (power/oil transmission line) transmission systems where the capability of fault tolerance is desirable. However, the complex operating environment and the dynamic features of load demands influence the evaluation of system reliability. In this paper, a stochastic multiple-valued (SMV) approach is proposed to efficiently predict the reliability of two models of systems with non-repairable components and dynamically repairable components. The weights/performances and reliabilities of multi-state components (MSCs) are represented by stochastic sequences consisting of a fixed number of multi-state values with the positions being randomly permutated. Using stochastic sequences with L multiple values, linear computational complexities with parameters n and L are required by the SMV approach to compute the reliability of different multi-state k-out-of-n systems at a reasonable accuracy, compared to the complexities of universal generating functions (UGF) and fuzzy universal generating functions (FUGF) that increase exponentially with the value of n. The analysis of two benchmarks shows that the proposed SMV approach is more efficient than the analysis using UGF or FUGF.
Highlights
The k-out-of-n system is widely used to model various industrial systems that require the adoption of redundancy for the purpose of fault tolerance, such as multiple lines transmission systems and production systems consuming multiple resources [1]
Computational overhead is a key challenge in the evaluation of the reliability of multi-state k-out-of-n systems with large numbers of components and states that can describe the behavior of multiple line transmission systems
The results indicate that the run time of the stochastic multiple-valued (SMV) approach increases with the length of the sequences and the number of components, while the run time required by universal generating function (UGF) and fuzzy universal generating function (FUGF) increases with the number of components and the number of components’ states
Summary
The k-out-of-n system is widely used to model various industrial systems that require the adoption of redundancy for the purpose of fault tolerance, such as multiple lines transmission systems and production systems consuming multiple resources [1]. To perform the reliability analysis of a k-out-of-n system, the method of universal generating function (UGF) is adopted. The required computational complexity analysis of the UGF technique to evaluate the reliability of a multi-state weighted k-out-of-n system is performed in Reference [11]. In order to reduce the computational complexity, a fuzzy universal generating function (FUGF) method is presented in References [20,21] It combines the fuzzy set theory [22,23], UGF, and a clustering technique [24] to obtain the reliability of a multi-state weighted k-out-of-n system. A stochastic multiple-valued (SMV) approach is presented that aims to predict the reliability of different multi-state k-out-of-n systems. Two types of multi-state weighted k-out-of-n systems [11], using universal generating function (UGF) and fuzzy universal generating function (FUGF), are presented
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
