Fault diagnosis For complex systems based on reliability analysisand sensors data considering epistemic uncertainty diagnozowanie błędów w systemach złożonych na podstawie analizy niezawodności oraz danych z czujników z uwzględnieniem niepewności epistemicznejThis paper presents an information fusion method to diagnose system fault based on dynamic fault tree (DFT) analysis and dynamic evidential network (DEN). In the proposed method, firstly, it uses a DFT to describe the dynamic fault characteristics and evaluates the failure rate of components using interval numbers to deal with the epistemic uncertainty. Secondly, qualitative analysis of a DFT is to generate the characteristic function via a traditional zero-suppressed binary decision diagram, while quantitative analysis is to calculate some importance measures by mapping a DFT into a DEN. Thirdly, these reliability results are updated according to sensors data and used to design a novel diagnostic algorithm to optimize system diagnosis. Furthermore, a diagnostic decision tree (DDT) is obtained to guide the maintenance workers to recover the system. Finally, the performance of the proposed method is evaluated by applying it to a train-ground wireless communication system. The results of simulation analysis show the feasibility and effectiveness of this methodology.

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.

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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.

Competing failures are time domain contention situations between the propagated failures (PFs) that originate from dependent components and the failure isolation caused by the trigger component. The methods based on combinatorial analysis commonly used in the analysis of competing failures require a complicated formula derivation and model reduction process. This paper proposes an integrated model based on generalized stochastic Petri nets (GSPNs) for analyzing the competing failures in the system, and further considers the effect of common cause failures (CCFs). The proposed modeling method inherits the advantages of GSPNs and provides a simplified method to compute the reliability of systems, which affect by competing failures and CCFs. Finally, the proposed method is applied in the flight control system (FCS) and demonstrated by the efficient decomposition and aggregation (EDA) method and combinatorial analysis method.

A new optimal sensor placement is developed to improve the efficiency of fault diagnosis based on multiattribute decision-making considering the common cause failure. The optimal placement scheme is selected based on the reliability of the top event on condition that the number of sensors is preset. Specifically, a β-factor model is introduced to deal with the common cause failure, and dynamic fault tree is used to describe the dynamic failure behaviors. Besides, a dynamic fault tree is converted into a dynamic Bayesian network to calculate the reliability parameters, which construct the decision matrix. Furthermore, an efficient TOPSIS algorithm is adopted to determine the potential locations of sensors. In addition, a diagnostic sensor model is developed to take into account the failure sequence between a sensor and a component using a priority AND gate, and the failure probability of the top event for all sensor placement scenarios is calculated to determine the optimal sensor placement. Finally, a case is provided to prove that the common cause failure has made a considerable impact on the 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.

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