Abstract
This study focused on mixed uncertainty of the state information in each unit caused by a lack of data, complex structures, and insufficient understanding in a complex multistate system as well as common-cause failure between units. This study combined a cloud model, Bayesian network, and common-cause failure theory to expand a Bayesian network by incorporating cloud model theory. The cloud model and Bayesian network were combined to form a reliable cloud Bayesian network analysis method. First, the qualitative language for each unit state performance level in the multistate system was converted into quantitative values through the cloud, and cloud theory was then used to express the uncertainty of the probability of each state of the root node. Then, the β-factor method was used to analyze reliability digital characteristic values when there was common-cause failure between the system units and when each unit failed independently. The accuracy and feasibility of the method are demonstrated using an example of the steering hydraulic system of a pipelayer. This study solves the reliability analysis problem of mixed uncertainty in the state probability information of each unit in a multistate system under the condition of common-cause failure. The multistate system, mixed uncertainty of the state probability information of each unit, and common-cause failure between the units were integrated to provide new ideas and methods for reliability analysis to avoid large errors in engineering and provide guidance for actual engineering projects.
Highlights
A Bayesian network (BN) is an inheritance and development of the fault tree
It is a powerful tool for two-way reasoning and probability analysis, and it is used to express and process uncertain knowledge [2]. e Bayesian network was first proposed by Pearl [3] in the 1980s. en, domestic and foreign scholars continued to improve it
For the reliability analysis of multistate systems, Wilson [22] and Graves et al [23] studied the method of applying BNs to establish a multistate system model
Summary
A Bayesian network (BN) is an inheritance and development of the fault tree. It can be used for system modeling through a directed acyclic graph. If one uses accurate values to describe the fuzzy possibility of node failure, the calculated results often have unacceptable errors, and the traditional Bayesian network method is not appropriate Methods such as fuzzy theory, interval theory, and Dempster-Shafer (DS) evidence theory can be integrated with a Bayesian network to analyze the reliability of the uncertainty problem of a multistate system with unit state probability information. Mi et al [53] used the ability of Bayesian networks to represent complex dependencies between random variables and proposed a reliability modeling and evaluation method for multistate systems with common-cause failures. Mi et al [57] used a complex multistate common-cause failure system based on the evidence network for reliability analysis and used the computer numerical control (CNC) heavy-duty horizontal lathe servo feed control system as an engineering example to verify the applicability of the method.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
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 call