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.

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

Purpose This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail. Design/methodology/approach First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency. Findings In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis. Originality/value The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

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

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.

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

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.