Multi-state reliability assessment for hydraulic lifting system based on the theory of dynamic Bayesian networks

2016 ◽  
Vol 230 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Chun Su ◽  
Ning Lin ◽  
Yequn Fu
Keyword(s):  
Bayesian Network ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Mechanical Systems ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Effect Analysis ◽  
Analysis And Design ◽  
Reliability Modelling ◽  
Lifting System

Mechanical systems and their components usually have multiple failure modes and different performance states. Most existing system reliability modelling theories are developed on the basis of binary logic, which lack sufficient ability to describe the above phenomena. In this article, dynamic Bayesian network theory is employed to evaluate the multi-state reliability of a hydraulic lifting system. First, failure mode and effect analysis and structural analysis and design technique are comprehensively applied to analyse the functionalities and failure modes of the components. Afterwards, the time factor is integrated into the model by considering the state transition of the components. In this way, the multi-state reliability model of the system is established by dynamic Bayesian network. The reliability assessment and diagnostic analysis are performed by taking advantage of the dynamic Bayesian network’s bi-directional reasoning ability, and the results are in good agreement with actual situation. It shows that the proposed approach is effective and convenient for multi-state reliability modelling and analysis for mechanical systems.

Time and Spatial Modeling of Wear Mechanisms in Nuclear Steam Turbine Blades Using Dynamic Bayesian Networks

2018 ◽  
Author(s):  
David A. Quintanar-Gago ◽  
Pamela F. Nelson ◽  
Ángeles Díaz-Sánchez
Keyword(s):  
Bayesian Network ◽  
Failure Mode ◽  
Wear Mechanisms ◽  
Failure Modes ◽  
Turbine Blades ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Time Dependent ◽  
Prototype Model ◽  
Quantitative Methodology

This paper describes a quantitative methodology to estimate the probability of blade failure modes resulting from typical wear mechanisms in nuclear turbines, which can be used to optimize maintenance. The approach used to model time and spatial dependence of wear mechanisms that affect blades involves the coupling of a Static Bayesian Network to a Dynamic Bayesian Network. This prototype model has been designed to use conditional and time dependent Weibull-like failure rates that can be computed from reliability data bases (failure times and modes, associated causes, row and blade part that failed) to quantify Markov matrixes contained within dynamic nodes. The model can be used to make inferences such as the most probable causes of failure in a row and blade part, and visualize the probability as a function of time. It can be also used to determine the riskier location given evidence such as failure mode or the wear mechanisms involved. Also, maintenance tasks acting over time dependent failure functions have been implemented to exemplify the effect of perfect and three kinds of imperfect actions and how they affect the mechanisms and failure mode evolution, given the conditional dependences among them.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network

2018 ◽  
Vol 233 (3) ◽  
pp. 455-469 ◽  
Author(s):  
Lei Jiang ◽  
Yiliu Liu ◽  
Xiaomin Wang ◽  
Mary Ann Lundteigen
Keyword(s):  
Control System ◽  
Bayesian Network ◽  
High Speed ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
High Speed Train ◽  
Train Control ◽  
Train Control System ◽  
Onboard System ◽  
Reliability And Availability

The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This article presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in the operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, that is, the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian networks. The forward and backward inferences are conducted not only to evaluate the reliability and availability but also to recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.

scholarly journals The engineering skills training process modeling using dynamic bayesian nets

2021 ◽  
pp. 87-96
Author(s):  
Andrey Chukhray ◽  
Olena Havrylenko
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Process ◽  
Probabilistic Inference ◽  
Dynamic Bayesian Network ◽  
Computer Training ◽  
Skills Training ◽  
Dynamic Bayesian Networks ◽  
Competence Level ◽  
Intelligent Computer

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

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.

scholarly journals Attack Impact Discovery and Recovery with Dynamic Bayesian Networks

2019 ◽  
Vol 8 (S1) ◽  
pp. 74-79
Author(s):  
T. Rajeshwari ◽  
C. Thangamani
Keyword(s):  
Bayesian Network ◽  
Impact Analysis ◽  
Probabilistic Reasoning ◽  
Belief Propagation ◽  
Dynamic Bayesian Network ◽  
Data Communication ◽  
Dynamic Bayesian Networks ◽  
Attack Detection ◽  
Object State ◽  
Dependency Network

The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the data communication over the objects. The attack ramification models are designed with intrusion root information. The attack ramifications are applied to identify the malicious objects and contaminated objects. The attack ramifications are discovered with the information flows from the attack sources. The Attack Ramification with Bayesian Network (ARBN) scheme discovers the attack impact without the knowledge of the intrusion root. The probabilistic reasoning approach is employed to analyze the object state for ramification process. The objects lifetime is divided into temporal slices to verify the object state changes. The system call traces and object slices are correlated to construct the Temporal Dependency Network (TDN). The Bayesian Network (BN) is constructed with the uncertain data communication activities extracted from the TDN. The attack impact is fetched with loopy belief propagation on the BN model. The network security system is built with attack impact analysis and recovery operations. Live traffic data analysis process is carried out with improved temporal slicing concepts. Attack Ramification and Recovery with Dynamic Bayesian Network (ARRDBN) is built to support attack impact analysis and recovery tasks. The unsupervised attack handling mechanism automatically discovers the feasible solution for the associated attacks.

Analysing Reliability Aspects of HVAC Systems - A Case

2012 ◽  
Vol 488-489 ◽  
pp. 1813-1817
Author(s):  
Rajiv Kumar Sharma ◽  
Chandan Parbhot ◽  
Sidhant Thakur ◽  
Vivek Thakur
Keyword(s):  
Qualitative Analysis ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Hvac Systems ◽  
Risk Priority Number ◽  
Effect Analysis ◽  
Cause Analysis ◽  
Root Cause ◽  
Reliability Characteristics ◽  
Assessment Techniques

The general objective of this work is to analyze the failure of HVAC components in metro trains by using reliability assessment techniques. Various failure causes with respect to the electronic, electrical, hydraulic, pneumatic, software and mechanical components were identified. To improve upon the reliability characteristics of the system, in depth qualitative analysis of all HVAC units is carried out using Root cause analysis (RCA) and failure modes and effect analysis (FMEA) by listing all possible failure modes and their possible causes. Based on FMEA of components, Risk Priority Number (RPN) is calculated.

scholarly journals Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 837 ◽  
Author(s):  
Hanbing Liu ◽  
Xin He ◽  
Yubo Jiao ◽  
Xirui Wang
Keyword(s):  
Bayesian Network ◽  
Mode Shape ◽  
Network Inference ◽  
Limit State ◽  
Reliability Assessment ◽  
Dynamic Bayesian Network ◽  
Structural Condition ◽  
Simply Supported ◽  
Vibration Data ◽  
Bee Colony

Structural health monitoring (SHM) has been widely used in all kinds of bridges. It is significant to accurately assess the serviceability and reliability of bridge subjected to severe conditions by SHM technique. Bridge deflection as an essential evaluation index can reflect structural condition perfectly. In this study, an approach for deflection calculation and reliability assessment of simply supported bridge is presented. Firstly, a bridge deflection calculation method is proposed based on modal flexibility and Kriging method improved by artificial bee colony algorithm. Secondly, a dynamic Bayesian network is employed to evaluate the deflection reliability combined with monitoring results which include modal frequency, mode shape, environmental temperature, and humidity. A linear regression model is established to analyze the relationship between modal parameters and environmental factors. Thirdly, a simply supported bridge is constructed and monitored to verify the effectiveness of the proposed method. The results reveal that the proposed method can precisely calculate the bridge deflection. Finally, the time-dependent reliabilities of two cases are computed and the effects of monitoring factors on bridge deflection reliability are analyzed by sensitivity parameter. It indicates that the reliability is negatively correlated with temperature and more sensitive to mode shape than other three factors.

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