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.
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...
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.
Aiming at the problem in obtaining the precise failure rates of components, this paper presents a new reliability evaluation method for complex systems using interval triangular fuzzy subset and evidence network (EN). Specifically, it develops the fault tree model based on failure mode and effects analysis (FMEA) and uses the interval-valued triangular fuzzy weighted mean to express the interval failure rates of components. Furthermore, fuzzy fault tree is mapped into an EN to calculate some reliability parameters. In addition, a possibility-based NSG ranking approach is adopted to rank components and get the critical component, which can be used to provide the basis for system optimization and maintenance decision-making. Finally, a numerical example is given to validate the availability and efficiency of the proposed method.
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