Reliability assessment of systems subject to interval-valued probabilistic common cause failure by evidential networks

Zuo, Lin; Xiahou, Tangfan; Liu, Yu

doi:10.3233/jifs-18290

Cited by 25 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…respectively, whereR L la (t) andR U la (t) can be calculated by equations (27) and (28), respectively.…”

Section: Fuzzy Reliability Evaluation For a Multi-component Systemmentioning

confidence: 99%

“…Then, the a-cut level of the shape parameter of component l can be represented as h la (t) = ½h L la (t), h U la (t) with the lower bound In this example, all the fuzzy parameters are represented by TFNs and are tabulated in Table 2. Based on equations (27) and (28), the fuzzy survival probability of each component can be calculated under the internal degradation processes. As an illustration, the fuzzy survival probability of each component at time t = 10 months under the internal degradation processes is depicted in Figure 4.…”

Section: Case Studiesmentioning

confidence: 99%

“…The fuzzy reliability functions of components #1, #2, and #3 can be calculated by equations (27) and (28), and the results are shown in Figure 6, respectively. Based on the series-parallel configuration of the system, the fuzzy system reliability function is given bỹ…”

Section: Case Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Reliability assessment for systems suffering competing degradation and random shocks under fuzzy environment

Tang

2019

Science Progress

View full text Add to dashboard Cite

Reliability assessment of multi-component systems under competing degradation and random shocks has been intensively investigated in recent years. In most cases, the parameters associated with competing degradation and random shocks are represented by crisp values. However, due to insufficient data and vague judgments from experts, it may produce epistemic uncertainty with those parameters and they are befitting to be described as fuzzy numbers. In this article, the internal degradation is treated as a continuous monotonically increasing random process with respect to operating time, whereas the amount of cumulative damage produced by each external random shock is modeled by a geometric process. As components in a system suffer the same environmental condition, an external random shock will produce different amounts of cumulative damage to each component simultaneously. Each component fails when either the internal degradation or cumulative damage from the random shocks, whichever comes first, exceeds its corresponding random thresholds. Moreover, the parameters associated with the internal degradation and the random shocks are represented by triangular fuzzy numbers. The fuzzy reliability functions of components and the entire system are evaluated by a set of optimization models. A multi-component system, together with some comparative results, is presented to illustrate the implementation of the proposed method.

show abstract

“…respectively, whereR L la (t) andR U la (t) can be calculated by equations (27) and (28), respectively.…”

Section: Fuzzy Reliability Evaluation For a Multi-component Systemmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Reliability assessment for systems suffering competing degradation and random shocks under fuzzy environment

Tang

2019

Science Progress

View full text Add to dashboard Cite

show abstract

“…25 The explicit and implicit modeling methods are usually employed to solve the CCF modeling problem in reliability analysis. 26 In a previous study, 27 a method is proposed to process the CCF in the auxiliary power group based on the discrete-time BN and β factor model. The multiple error shock theory is introduced to solve the problem of three or more failures caused by the CCF.…”

Section: Introductionmentioning

confidence: 99%

A fault diagnostic system based on Petri nets and gray relational analysis for train–ground wireless communication systems

Zeng

Duan

Feng

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

The system structure of train–ground wireless communication systems (TWCSs) is extremely complicated due to the use of fault tolerant technology to improve their performance. This complex structure raises several challenges in fault diagnosis for TWCSs, such as epistemic uncertainty, dynamic fault behaviors, and common cause failure (CCF). A fault diagnostic system is proposed to deal with these challenges based on Petri nets and gray relational analysis in this paper. Specifically, the fuzzy analytic hierarchy process is used to evaluate the failure data of components to handle epistemic uncertainty. Furthermore, the dynamic fault tree of TWCSs is established and converted into a generalized stochastic Petri net to calculate several reliability parameters used for fault diagnosis. Besides, a β factor model is employed to resolve the problem of CCF in TWCSs. In addition, Birnbaum importance measure (BIM), risk achievement worth (RAW) and test cost are considered comprehensively to obtain the optimal diagnostic sequence using an improved gray relational analysis. Finally, a numerical example is presented to demonstrate the efficiency of the proposed fault diagnostic system.

show abstract

“…Many research attempts have also been developed to incorporate CCF into reliability assessment of SIS. For instant, Zuo et al [44] extended CCF into interval-valued probabilistic CCF and used the evidential network models to assess the system reliability of SIS. Liu and Rausand [45] used  -factor method to quantify the CCF event and assessed the reliability of SIS with three testing strategies under different demand modes by petri nets.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Reliability Assessment of Safety Instrumented Systems Accounting for Common Cause Failure

Zhao

2020

IEEE Access

View full text Add to dashboard Cite

Reliability of safety instrumented systems (SISs) is a critical measure to ensure production safety of many industries. This paper focus on low-demand SISs. The reliability of these SISs is quantified by evaluating their probability of failure on demand (PFD). However, due to lack of knowledge, and/or vague judgments from experts, epistemic uncertainty associated with the parameters of components' degradation models is inevitable. Meanwhile, common cause failure (CCF) of two or more components caused by shared environments, often exists in SISs, reliability assessment for a SIS, therefore, becomes a challenging task. In this paper, fuzzy reliability assessment for SISs is conducted by taking account of the CCF among components of a SIS. The fuzzy Markov model is utilized to characterize the degradation process of components under fuzzy environment. The fuzzy state probability distribution of components is, then, calculated by formulating a set of constrained optimization models. Based on the fault tree of a SIS, the fuzzy PFD of the entire SIS with CCFs is formulated by using the  -factor to quantify the CCFs. The fuzzy PFD at any  -cut level is, therefore, computed by a constrained optimization model. According to the optimization of parameters of SIS, the lower bound and upper bound of fuzzy PFD of SIS can be determined. Finally, we can quantify the effectiveness of CCF event for assessing the fuzzy PFD of SIS.

show abstract

Reliability assessment of systems subject to interval-valued probabilistic common cause failure by evidential networks

Cited by 25 publications

References 29 publications

Reliability assessment for systems suffering competing degradation and random shocks under fuzzy environment

Reliability assessment for systems suffering competing degradation and random shocks under fuzzy environment

A fault diagnostic system based on Petri nets and gray relational analysis for train–ground wireless communication systems

Fuzzy Reliability Assessment of Safety Instrumented Systems Accounting for Common Cause Failure

Contact Info

Product

Resources

About