scholarly journals Reliability Analysis of a Complex Multistate System Based on a Cloud Bayesian Network

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Jin-Zhang Jia ◽  
Zhuang Li ◽  
Peng Jia ◽  
Zhi-Guo Yang
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
Cloud Model ◽  
The State ◽  
State Probability ◽  
Common Cause ◽  
Common Cause Failure ◽  
Failure Theory ◽  
Multistate System ◽  
Probability Information

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.

scholarly journals Reliability Analysis of Common Cause Failure Multistate System Based on CUGF

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jin-Zhang Jia ◽  
Zhuang Li ◽  
Peng Jia ◽  
Zhi-guo Yang
Keyword(s):  
Reliability Analysis ◽  
Cloud Model ◽  
State Probability ◽  
Information Uncertainty ◽  
Common Cause ◽  
Common Cause Failure ◽  
Generation Function ◽  
Multistate System ◽  
Multistate Systems ◽  
Failure Conditions

This paper addresses the problem of mixed uncertainty in the reliability analysis of multistate systems under common cause failure conditions. Combining the cloud model theory, universal generation function (UGF) method, and common cause failure theory, the universal generation function method is extended based on a probabilistic cloud model, i.e., the cloud universal generation function (CUGF) analysis method. The cloud model represents the random and cognitive uncertainty of the state probability, i.e., mixed uncertainty. Next, through CUGF, according to the calculation rules of cloud operators, we provide steps to obtain the reliability of a multistate system under independent failure and common cause failure conditions and obtain cloud digital features for reliability. The accuracy and feasibility of the method are verified by a numerical example. This paper solves the problem of reliability analysis of multistate systems with mixed uncertainty in unit state probability information under common cause failure conditions. We integrate system multistate, information uncertainty, and common cause failure for reliability analysis to avoid large errors, more in line with a project’s actual situation. We propose new ideas and methods to process randomness and fuzzy information or data in multistate system reliability analysis.

Reliability analysis for complex systems based on generalized stochastic petri nets and EDA approach considering common cause failure

2019 ◽  
Vol 37 (5) ◽  
pp. 1513-1530 ◽  
Author(s):  
Yining Zeng ◽  
Rongxing Duan ◽  
Shujuan Huang ◽  
Tao Feng
Keyword(s):  
Complex Systems ◽  
Reliability Analysis ◽  
Evaluation Method ◽  
Stochastic Petri Nets ◽  
Importance Measure ◽  
Dynamic Failure ◽  
Common Cause ◽  
Content Type ◽  
Common Cause Failure ◽  
Dynamic Fault Tree

Purpose This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems. Design/methodology/approach Firstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis. Findings In this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis. Originality/value The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

Reliability Analysis Based on Transverse Distribution of Random Loading

2010 ◽  
Vol 118-120 ◽  
pp. 532-535
Author(s):  
Peng Gao ◽  
Li Yang Xie
Keyword(s):  
Reliability Analysis ◽  
Exact Distribution ◽  
Engineering Practice ◽  
Reliability Model ◽  
Two Dimensional ◽  
Random Loading ◽  
Common Cause ◽  
Transverse Distribution ◽  
Common Cause Failure ◽  
Dimensional Distribution

The traditional loading-strength interference model is used to calculate the reliability of components and system when random loading act once. In fact, components always work under repeated random loading, so it is important to derive a reliability model considering the frequency of loading. The two-dimensional distribution of random loading is proposed in this paper. In engineering practice, only a few samples of time-loading process can be obtained because of all kinds of limitation, so the reliability model based on transverse distribution of random loading. In addition, when it is difficult to know the exact distribution of random loading and strength, a discrete reliablity model is developed through the method of universal generating function. Finally, the reliability of system is analyzed considering common cause failure.

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