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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...

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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Fault diagnosis For complex systems based on dynamic evidential network and multi-attribute decision making with interval numbers diagnostyka uszkodzeń systemu złożonego oparta na dynamicznych sieciach dowodowych oraz wieloatrybutowej metodzie podejmowania decyzji z wykorzystaniem liczb interwałowych The complexity of modern system structures and failure mechanisms makes it very difficult to locate the system fault. It has characteristics of dynamics of failure, diversity of distribution and epistemic uncertainties, which increase the challenges in the fault diagnosis significantly. This paper presents a fault diagnosis framework for complex systems within which the failure rates of components are expressed in interval numbers. Specifically, it uses a dynamic fault tree (DFT) to model the dynamic fault behaviors and deals with the epistemic uncertainties using Dempster-Shafer (D-S) theory and interval numbers. Furthermore, a solution is proposed to map a DFT into a dynamic evidential network (DEN) to calculate the reliability parameters. Additionally, diagnostic importance factor (DIF), Birnbaum importance measure (BIM) and heuristic information values (HIV) are taken into account comprehensively in order to obtain the best fault search scheme using an improved VIKOR algorithm. Finally, an illustrative example is given to demonstrate the efficiency of this method. Keywords: diagnosis strategy, D-S theory, interval numbers, dynamic evidential network, VIKOR.Złożoność However, these methods assume that all components obey to the same distribution and cannot handle the challenge (2). Furthermore, these methods, which are usually assumed that the failure rates of the components are considered as crisp values describing their reliability characteristics, have been found to be inadequate to deal with the challenge (3) mentioned above. Therefore, fuzzy sets theory has been introduced as a useful tool to handle the challenge (3). The fuzzy fault tree analysis model employs fuzzy sets and possibility theory, and deals with ambiguous, qualitatively incomplete and inaccurate information [8,16,18]. To deal with the challenge (1) and (3), fuzzy DFT analysis has been introduced [13][14] which employs a DFT to construct the fault model and calculates the reliability results based on the continuous-time BN under fuzzy numbers. However, these approaches cannot handle the challenge (2). For this purpose, Mi et al. proposed a new reliability assessment approach which used a DFT to model the dynamic characteristics within complex systems and estimated the parameters of different life distributions using the coefficient of variation (COV) method [19]. To a certain extent, this method can meet the above challenges. But it is confined to the reliability analysis and cannot be used for the fault diagnosis. Dugan introduced a diagnostic importance factor (DIF) to determine the diagnosis sequence using DFT analysis [1]. However, the solution for DFT is based on Markov Chain which has an apparent state space explosion problem. In the work...

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.