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.

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.

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.

Fault-tolerant redundant repairable system with different failures and delays

2019 ◽  
Vol 37 (3) ◽  
pp. 1043-1071
Author(s):  
Chandra Shekhar ◽  
Amit Kumar ◽  
Shreekant Varshney ◽  
Sherif I. Ammar
Keyword(s):  
Reliability Analysis ◽  
Time Distribution ◽  
Service System ◽  
Fault Tolerant ◽  
Repair Time ◽  
Repairable System ◽  
Common Cause ◽  
Content Type ◽  
Common Cause Failure ◽  
N Stage

Purpose The internet of things and just-in-time are the embryonic model of innovation for the state-of-the-art design of the service system. This paper aims to develop a fault-tolerant machining system with active and standby redundancy. The availability of the fault-tolerant redundant repairable system is a key concern in the successful deployment of the service system. Design/methodology/approach In this paper, the authors cogitate a fault-tolerant redundant repairable system of finite working units along with warm standby unit provisioning. Working unit and standby unit are susceptible to random failures, which interrupt the quality-of-service. The system is also prone to common cause failure, which tends its catastrophe. The instantaneous repair of failed unit guarantees the increase in the availability of the unit/system. The time-to-repair by the single service facility for the failed unit follows the arbitrary distribution. For increasing the practicability of the studied model, the authors have also incorporated real-time machining practices such as imperfect coverage of the failure of units, switching failure of standby unit, common cause failure, reboot delay, switch over delay, etc. Findings For deriving the explicit expression for steady-state probabilities of the system, the authors use a supplementary variable technique for which the only required input is the Laplace–Stieltjes transform (LST) of the repair time distribution. Research limitations/implications For complex and multi-parameters distribution of repair time, derivation of performance measures is not possible. The authors prefer numerical simulation because of its importance in the application for real-time uses. Practical implications The stepwise recursive procedure, illustrative examples, and numerical results have been presented for the diverse category of repair time distribution: exponential (M), n-stage Erlang (Ern), deterministic (D), uniform (U(a,b)), n-stage generalized Erlang (GE[n]) and hyperexponential (HE[n]). Social implications Concluding remarks and future scopes have also been included. The studied fault-tolerant redundant repairable system is suitable for reliability analysis of a computer system, communication system, manufacturing system, software reliability, service system, etc. Originality/value As per the survey in literature, no previous published paper is presented with so wide range of repair time distribution in the machine repair problem. This paper is valuable for system design for reliability analysis of the fault-tolerant redundant repairable.

scholarly journals Reliability and Sensitivity Analysis Method for a Multistate System with Common Cause Failure

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Jinlei Qin ◽  
Zheng Li
Keyword(s):  
Sensitivity Analysis ◽  
System Reliability ◽  
Analysis Method ◽  
Common Cause ◽  
Common Cause Failure ◽  
Resource Limitations ◽  
Multistate System ◽  
Failure State ◽  
Sensitivity Analysis Method ◽  
Reliability Assessments

With the increasing complexity of industrial products and systems, some intermediate states, other than the traditional two states, are often encountered during reliability assessments. A system with more than two states is called a multistate system (MSS) which has already become a general phenomenon in the components and/or systems. Moreover, common cause failure (CCF) often plays a very important role in the assessment of system reliability. A method is proposed to assess the reliability and sensitivity of an MSS with CCF. Some components are not only in a failure state that can cause failure itself, but also in a state that can cause the failure of other components with a certain probability. The components that are affected by one type of CCF make up some sets which can overlap on some components. Using the technology of a universal generating function (UGF), the CCF of a component can be incorporated in the expression of its UGF. Consequently, indices of reliability can be calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers to judge which type of CCF should be eliminated first under various resource limitations. Examples illustrate and validate this method.

Research on Reliability Analysis Method of Industrial Control System Based on Markov Process

2014 ◽  
Vol 541-542 ◽  
pp. 1513-1517 ◽  
Author(s):  
Cheng Cheng Wang ◽  
Xiao Jing Liu ◽  
Chun Xi Wang
Keyword(s):  
Markov Process ◽  
Reliability Analysis ◽  
Reliability Prediction ◽  
Testing Time ◽  
Analysis Method ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Common Cause ◽  
Common Cause Failure ◽  
Equipment Production

With the development of industrial control systems manufacture level and reliability technology in recent years, the traditional reliability prediction methods have been unable to meet the needs of product quality. The complicated system have taken the place of traditional simple equipment, maintenance, testing and common-cause failure are becoming new problem before product designers, compared to traditional methods of reliability prediction, the method based on Markov process take maintenance and testing time, common-cause failure factors into consideration, not only make the reliability analysis results more accurate, but also proves that these factors can affect the reliability of product, which can be used as the reference for equipment production engineer to improve products.

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