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.

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.

Technological innovation in modern systems has significantly improved their performance. However, fault characteristics such as epistemic uncertainty and dynamic failure modes often occur when these systems break down, which greatly raises some new challenges in fault diagnosis. A new fault diagnosis strategy for complex systems is presented based on multi-source heterogeneous information considering epistemic uncertainty in this paper. Specifically, in view of the epistemic uncertainty, the failure distribution parameters of basic events are described with interval numbers and test cost of these events are evaluated using domain experts and intuitionistic fuzzy linguistic set; Aiming at the problem of dynamic failure modes, a dynamic fault tree (DFT) is used to establish the dynamic failure model and is converted into a dynamic evidential network to calculate some reliability parameters; Furthermore, a diagnostic decision table is constructed based on multi-attribute heterogeneous information such as test cost and some reliability results; Finally, a novel fault diagnosis strategy is designed based on distance-based VIKOR algorithm, which can provide some decision support for fault diagnosis and locate the fault as quickly as possible.

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.

Application of new technology in modern systems not only substantially improves the performance, but also presents a severe challenge to fault location of these systems. This paper presents a new fault location strategy for maintenance personnel to recover them based on information fusion and improved CODAS algorithm. Firstly, a fault tree is adopted to develop the failure model of a complex system, and failure probability of components is determined by expert evaluations to handle the uncertainty problem. Moreover, a fault tree is converted into an evidence network to obtain importance degrees, which are used to construct a diagnostic decision table together with the risk priority number. Additionally, these results are updated to optimize the maintenance process using sensor information. A novel dynamic location strategy is designed based on interval CODAS algorithm and optimal fault location strategy can be obtained. Finally, a real system is analyzed to demonstrate the feasibility of the proposed maintenance strategy.