An improved methodology for evaluating availability of multistate series–parallel systems using the polynomial distribution function

• Published in: The International journal of quality & reliability management Vol. 37; no. 6/7; pp. 1033 - 1047
• Main Authors: Hashemzadeh Ghalhari, Zahra, Zeinal Hamadani, Ali
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 23.11.2020; Emerald Group Publishing Limited
• Subjects: Algorithms; Availability; Computing time; Distribution functions; Markov analysis; Methods; Optimization; Polynomials; Power plants; Probability; Production capacity; Recursive functions; Redundancy; Subsystems; Availability; Multistate system; Universal generating function; Polynomial distribution function; Recursive algorithm
• ISSN: 0265-671X; 1758-6682
• DOI: 10.1108/IJQRM-08-2019-0250

PurposeThis paper employs new methods to evaluate the availability of multistate series–parallel systems, in which a number of similar components are available in each subsystem.Design/methodology/approachIn this paper, polynomial distribution function (PDF) is combined with universal generating function (UGF) and recursive algorithm (RA) methods to evaluate the availability of multistate series–parallel systems. To achieve this goal, the PDF is initially used to determine the performance rates and the probabilities corresponding to the performance states of the similar components in a subsystem. The obtained results are used to evaluate the system availability via the UGF and RA methods.FindingsIt is shown that the combined UGF and PDF (UGF-PDF) and also the combined RA and PDF (RA-PDF) methods require less computational time than did the UGF and RA methods, respectively.Originality/valueIn the UGF and RA methods, there is no difference in system availability evaluation time when considering similar or different components in each subsystem. But the proposed methods in this article do not have this restrictions; therefore, these methods can be used to evaluate system availability in optimal redundancy allocation problems. As a result, using these methods reduces the optimization time of those problems.