Protection Mechanism in Reliability Evaluation Approach to Multistate System with Common Cause Failure

Except the two types of state, complete failure and perfect functioning, some intermediate states also existed between those two states. This characteristic enables multistate system (MSS) whose components are often of more than two states. Moreover, because of dependence between components, common cause failure (CCF) enhances the failure risk of components during the operating period of MSS. A protection mechanism needs to be adopted because of the importance of certain components. For a MSS with CCF and protection mechanism, its reliability can be evaluated by a proposed approach. Certain components not only can cause failure at one state, but also can cause failure to other components because of the CCF. Under the impact of one type of CCF, those components can constitute a specific group that can overlap with certain components. Universal generating function (UGF) is adopted to incorporate the CCF and protection mechanism of a component. Consequently, reliability indices of a MSS can be evaluated by the proposed approach based on UGF. Examples and comparisons illustrate and validate this method. This method can also help engineers prioritize elements that require protection mechanism under various resource limitations.

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

Complexity ◽

10.1155/2019/6535726 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 6

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.

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Reliability Modeling for Multistate System with Preventive Maintenance under Customer Demand

Complexity ◽

10.1155/2020/3165230 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Jinlei Qin ◽

Zheng Li

Keyword(s):

Preventive Maintenance ◽

Customer Demand ◽

Reliability Modeling ◽

Reliability Indices ◽

Optimal Decisions ◽

Multistate System ◽

Proposed Model ◽

Random Failure ◽

Complete Failure ◽

Markov Random Process

The performance level of a multistate system (MSS) can vary among different values rather than only two states (perfect functioning and complete failure). To improve the reliability of MSSs, a maintenance strategy has been adopted to satisfy customer demand, and reliability modeling of MSS with preventive maintenance and customer demand is proposed. According to the regular degradation and random failure at each state, based on the Markov random process, the proposed MSS with preventive maintenance can be modeled to satisfy the customer demand in a specific state. This model can also be adapted to compute other reliability indices. Based on this model, the effect of different preventive maintenance actions on the reliability indices can be analyzed and further compared. Two numerical examples have been illustrated to show the validity of the proposed model. The reliability model presented in this study can be used to assess the type of MSS and help reliability engineers to compare different maintenance actions quantitatively and make optimal decisions.

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Effective sensor placement based on a VIKOR method considering common cause failure in the presence of epistemic uncertainty

Eksploatacja i Niezawodnosc - Maintenance and Reliability ◽

10.17531/ein.2021.2.5 ◽

2021 ◽

Vol 23 (2) ◽

pp. 253-262

Author(s):

Rong-Xing Duan ◽

Jie-Jun He ◽

Tao Feng ◽

Shu-Juan Huang ◽

Li Chen

Keyword(s):

System Reliability ◽

Epistemic Uncertainty ◽

Sensor Placement ◽

Vikor Method ◽

Common Cause ◽

Reliability Indices ◽

Common Cause Failure ◽

Dynamic Fault Tree ◽

Sensor Model

Owing to expensive cost and restricted structure, limited sensors are allowed to install in modern systems to monitor the working state, which can improve their availability. Therefore, an effective sensor placement method is presented based on a VIKOR algorithm considering common cause failure (CCF) under epistemic uncertainty in this paper. Specifically, a dynamic fault tree (DFT) is developed to build a fault model to simulate dynamic fault behaviors and some reliability indices are calculated using a dynamic evidence network (DEN). Furthermore, a VIKOR method is proposed to choose the possible sensor locations based on these indices. Besides, a sensor model is introduced by using a priority AND gate (PAND) to describe the failure sequence between a sensor and a component. All placement schemes can be enumerated when the number of sensors is given, and the largest system reliability is the best alternative among the placement schemes. Finally, a case study shows that CCF has some influence on sensor placement and cannot be neglected in the reliabilitybased sensor placement.

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

Shock and Vibration ◽

10.1155/2021/6660928 ◽

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.

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Practical Procedures for Estimation of Plant Specific Common Cause Failure Quantification Parameters for Plants With Accumulated Operating Experiences

12th International Conference on Nuclear Engineering, Volume 3 ◽

10.1115/icone12-49215 ◽

2004 ◽

Author(s):

Young G. Jo

Keyword(s):

Target System ◽

Common Cause ◽

Common Cause Failure ◽

Consistent Manner ◽

The Impact ◽

Original Event

One of the major tasks in a plant specific common cause failure (CCF) analysis is to determine the overall event applicability factors of the original CCF events. The overall event applicability factor of a CCF event, which is used for specializing the impact of the original event for a target system, should be determined by considering the target system’s defenses against such CCF. However, lack of currently available detailed guidelines made it difficult to determine the overall event applicability factors in a systematic and consistent manner. In this paper, practical procedures for plant specific CCF analysis were developed. Especially, guidelines and a set of criteria were developed for more systematic and consistent determination of the overall event applicability factors. Additional set of criteria was also developed especially for crediting plants with many years of operations for their enhanced defenses against CCFs based on accumulated operating experiences. The developed procedures were successfully implemented for three plant specific CCF analyses. It is believed that the use of the developed procedures would significantly facilitate a plant CCF analysis and produce consistent results.

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Reliability Analysis of Common Cause Failure Multistate System Based on CUGF

Mathematical Problems in Engineering ◽

10.1155/2020/4608124 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 1

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.

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