The traditional assessment method of system reliability is based on unit reliability and probability theory, but the rationale behind this method of assessment does not consider resources and economy. Unit reliability tests tend to consume resources and economy, and consequently, reliability assessment of large systems tends to consume a large amount of resources and economy. Taking the series system and parallel system as examples, this study proves that the traditional assessment method of system reliability is unreasonable, especially from the perspective of resources and economy, as it does not meet green economy and sustainable development goals. The system life cycle diagram model is established on the unit life cycle. A green assessment method of system reliability is proposed from the resources and economy perspectives. This proposed method does not depend on the number of units and does not require the consumption of a large amount of resources and economy. This study promotes sustainable and cleaner production. The green assessment method of system reliability is applied to the series system and parallel system to assess system reliability.

Introduction

Green system reliability refers to the probability that a system completes its specified duty under specified conditions and for a specified time. For system reliability assessment: the unit reliability Ri is the basis, the reliability block diagram (RBD) is the logical architecture, the binomial distribution in probability theory is the calculation method, the calculated result Rs is taken as the assessment result of system reliability. The series system and parallel system are basic system configuration types, and their reliability assessments are the basis for complex systems. The traditional assessment methods of system reliability include Monte Carlo method, network theory, fuzzy theory and so on. In the literatures, using sequential Monte Carlo simulation, Xiao et al. (2018) incorporated network topology optimization into system reliability assessment. Based on the semi-Markov process, Li et al. (2018) proposed a method based on the Markov renewal equation for assessing the reliability of phased mission systems, and its accuracy was verified using the Monte Carlo method. Lee and Pan (2018) proposed a nonparametric Bayesian network method for assessing system reliability at early design stages. Based on the Bayesian network and evidence theory, Mi et al. (2018) analyzed the reliability of a complex multi-state system with common cause failure. For reliability assessment of the Bayesian system, Jackson and Mosleh (2016) proposed a methodology that allows the incorporation of exact or certain data sets within complex multi-state on-demand continuous life metric systems. The wolf pack algorithm was proposed as a modified strategy for optimizing system reliability during the restoration process; thus, sensitivity, effectiveness, and expandability are analyzed (Ren et al., 2019). Wu et al. (2019) presented the sharing mechanism in system design and applied the universal generating function to assess system reliability.