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.
Article citation info: DUAN R, ZHOU H, FAN J. Diagnosis strategy for complex systems based on reliability analysis and MADM under epistemic uncertainty. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2015; 17 (3): 345-354, http://dx.doi.org/10.17531/ein.2015.3.4. Rongxing DUAN Huilin ZHOU Jinghui FAN Diagnosis strategy for complex systems baseD on reliability analysis anD maDm unDer epistemic uncertainty strategia Diagnostyki Dla systemów złożonych oparta na analizie niezawoDności oraz metoDach wieloatrybutowego poDejmowania Decyzji maDm w warunkach niepewności epistemologicznej Fault tolerant technology has greatly improved the reliability of train-ground wireless communication system (TWCS). However, its high reliability caused the lack of sufficient fault data and epistemic uncertainty, which increased significantly challenges in system diagnosis. A novel diagnosis method for TWCS is proposed to deal with these challenges in this paper, which makes the best of reliability analysis, fuzzy sets theory and MADM. Specifically, it adopts dynamic fault tree to model their dynamic fault modes and evaluates the failure rates of the basic events using fuzzy sets theory and expert elicitation to hand epistemic uncertainty. Furthermore, it calculates some quantitative parameters information provided by reliability analysis using algebraic technique and Bayesian network to overcome some disadvantages of the traditional methods. Diagnostic importance factor, sensitivity index and heuristic information values are considered comprehensively to obtain the optimal diagnostic ranking order of TWCS using an improved TOPSIS. The proposed method takes full advantages of the dynamic fault tree for modelling, fuzzy sets theory for handling uncertainty and MADM for the best fault search scheme, which is especially suitable for fault diagnosis of the complex systems. Keywords: Train-ground wireless communication system, Reliability analysis, MADM, Epistemic uncertainty, TOPSIS. Technologia odporna na błędy przyczyniła się do dużej poprawy niezawodności systemów łączności bezprzewodowej pociąg-ziemia (TWCS). IntroductionTrain-ground wireless communication system (TWCS) is a safety-critical subsystem of urban rail transit and its reliability has a direct effect on the stability and safety of the train operation system. For fast technology innovation, the performance of TWCS has been greatly improved with the wide application of high dependability safeguard techniques on one hand, but on the other hand, its complexity of technology and structure increasing significantly raise challenges in system maintenance and diagnosis. These challenges are shown as follows.(1) Lack of sufficient fault samples. Fault samples integrity has a significant influence on the system diagnostic performance.However, it is extremely difficult to obtain mass fault samples which need many case studies in practice due to some reasons. One reason is imprecise knowledge in an early stage of the new product design. The other factor is the changes of th...
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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