Reliability analysis of complex system based on dynamic fault tree and dynamic Bayesian network

2017 ◽  
Author(s):  
Zhiqiang Li ◽  
Junyuan Gu ◽  
Tingxue Xu ◽  
Linyu Fu ◽  
Jin An ◽  
...  
Keyword(s):  
Complex System ◽  
Bayesian Network ◽  
Reliability Analysis ◽  
Fault Tree ◽  
Dynamic Bayesian Network ◽  
Dynamic Fault Tree

Developing an Efficient Approach for Unmanned Aerial Vehicle Reliability Analysis

2020 ◽  
Author(s):  
Ahmad Khayyati ◽  
Mohammad Pourgol-Mohammad
Keyword(s):  
Reliability Analysis ◽  
Block Diagram ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Dynamic Fault Tree ◽  
Safety And Reliability ◽  
Time Dependencies ◽  
Aerial Vehicle ◽  
Redundant Component ◽  
High Level

Abstract Unmanned Aerial Vehicles (UAV) are increasingly get popularity in many applications. Their operation requires high level of safety and reliability to accomplish successful missions. In this study, the reliability was comparatively analyzed by different available approaches to select the efficient method. First, failure model of the system is developed. Then, three different scenarios are considered to study the effect of redundancies on the system reliability results. In the first scenario, there is no redundancy where in the second scenario there is only one redundant component and in the third scenario, there are three redundant components. Static reliability analysis such as Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Markov Chain (MC), and Bayesian Networks (BN) are applied on proposed scenarios and results are obtained. Regarding to time dependencies between redundant components, a dynamic-based methodology is also developed in this study through applying Dynamic Fault Tree (DFT) analysis. Proposed static and dynamic approaches are applied on an UAV as a case study and results are discussed. Finally, characteristics of each methodology and related conditions are clarified for selecting the efficient reliability analysis approach.

scholarly journals Dynamic Reliability Analysis of Corroded Pipeline Using Bayesian Network

2018 ◽  
Vol 7 (4.35) ◽  
pp. 210
Author(s):  
Nurul Sa’aadah Sulaiman ◽  
Henry Tan
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Dynamic Bayesian Network ◽  
Operating Pressure ◽  
Dynamic Reliability ◽  
Time Dynamic ◽  
Remaining Service Life ◽  
Reliability Method ◽  
Deterioration Process

Maintenance and integrity management of hydrocarbons pipelines face the challenges from uncertainties in the data available. This paper demonstrates a way for pipeline remaining service life prediction that integrates structural reliability analysis, accumulated corrosion knowledge, and inspection data on a sound mathematical foundation. Pipeline defects depth grows with time according to an empirical corrosion power law, and this is checked for leakage and rupture probability. The pipeline operating pressure is checked with the degraded failure pressure given by ASME B31G code for rupture likelihood. As corrosion process evolves with time, Dynamic Bayesian Network (DBN) is employed to model the stochastic corrosion deterioration process. From the results obtained, the proposed DBN model for pipeline reliability is advanced compared with other traditional structural reliability method whereby the updating ability brings in more accurate prediction results of structural reliability. The comparisons show that the DBN model can achieve a realistic result similar to the conventional method, Monte Carlo Simulation with very minor discrepancy.

Fault Diagnosis of the Foundation Brake Rigging System Based on Fault Tree and Bayesian Network

2016 ◽  
Vol 693 ◽  
pp. 1734-1740 ◽  
Author(s):  
Dan Wang ◽  
Ying Tian ◽  
Tai Yong Wang ◽  
Shi Feng Ye ◽  
Qiong Liu
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Bayesian Network ◽  
Fault Tree ◽  
Fault Analysis ◽  
Inference Process ◽  
Tree Algorithm ◽  
Junction Tree ◽  
Bayesian Tree

Based on the analysis of the advantages and limits of the traditional fault tree and Bayesian network in fault diagnosis, the method that building the fault Bayesian network based on fault tree is proposed in this paper. The paper introduces the correspondences between elements of the fault tree and the fault Bayesian network, also describes the inference process of the junction tree algorithm in the fault Bayesian network. Then with the foundation brake rigging system of CRH380AL EMU as an example, we build up the fault tree, complete its transmission to the fault Bayesian network, proving the superiority of the fault Bayesian tree in fault analysis of the complex system at last.

scholarly journals Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

2018 ◽  
Vol 5 (4) ◽  
pp. 171438 ◽  
Author(s):  
Zhiqiang Li ◽  
Tingxue Xu ◽  
Junyuan Gu ◽  
Qi Dong ◽  
Linyu Fu
Keyword(s):  
Markov Process ◽  
Bayesian Network ◽  
Markov Models ◽  
Dynamic Bayesian Network ◽  
Control Unit ◽  
The State ◽  
Absorbing Set ◽  
Imperfect Repair ◽  
Dynamic Fault Tree ◽  
Reliability Modelling

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

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