A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks

Duan, Rongxing; Zhou, Huilin

doi:10.1016/j.egypro.2012.02.255

Cited by 36 publications

(13 citation statements)

References 6 publications

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“…For example, Volkanovski et al [44] evaluate the reliability of a power system for energy delivery by constructing a fault tree structure, which represents the system configuration and includes all the possible flow routes of interruption of the power supply from the generators to the loads, including energy transfer limitations, common cause failure of power lines, energy flows and the capacity of generators, and loads in the power system. Duan and Zhou [45] also use the fault tree analysis and Bayesian networks for fault detection of a system for oil pressure warning instructions in an aircraft engine, where a diagnostic decision tree to guide maintenance personnel to make more efficient decisions when attempting to repair the system is obtained. An advanced Bayesian non-linear state estimation technique called Unscented Kalman Filtering to detect faults in HVAC (heating, ventilation, and air conditioning) components is presented by Bonvini et al [46].…”

Section: Classification-based Methodsmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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Section: Classification-based Methodsmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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“…It is also worth mentioning here that the BN is updated using realtime data from a conditioning monitoring system. Staying in the field of fault diagnosis, Duan and Zhou (2012) formulated a technique that uses FTs for modeling and BNs for inference purposes. The diagnostic importance factor (DIF) is generated using BNs.…”

Section: Decision-making Studiesmentioning

confidence: 99%

Risk and reliability assessment in chemical process industries using Bayesian methods

Roy

Srivastava

Sinha

2014

Reviews in Chemical Engineering

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Chemical process industries (CPI) are usually home to a large number of complex systems and components required for various operations involving hazardous chemicals. The intense operating conditions and complex interactions between the systems make the chemical plants vulnerable to accidents. Quotidian incidents and mishaps can lead to catastrophic incidents. Thus, the area of risk assessment and reliability analysis in CPI has been of much interest to the research community. The complexity of processes in CPI demands a risk assessment tool that can adapt itself to the dynamic environment and can efficiently model the functional and sequential dependencies between the components and the effects of external factors, component degradation, and variation in operating conditions. The risk assessment tool must have applicability during the operational lifetime of the system to serve as a platform for decision making and risk management. The unavailability of empirical data for some variables is another pertinent issue in risk analysis and reliability assessment in CPI. Analysts often have to work with subjective information such as expert opinion. Bayesian statistical methods based on the Bayes theorem are considered by many to be an effective tool to address the above-mentioned issues. These methods are based on the subjective interpretation of probability that helps to model the epistemic uncertainties and easily propagates them through complex system models. The methods provide a formal systematic way to incorporate subjective information into calculations. The inherent updating property accoutres these methods with the ability to deal with real-time changes. The opponents, however, point out that the methods produce high overconfidence and randomness in computed answers. In the last two decades, an increased interest can be seen in the research community toward the use of Bayesian methods in risk assessment. This paper presents a comprehensive literature review of the application of Bayesian methods through Bayesian parameter estimation techniques and Bayesian updating procedures in process industries. Both these techniques have been extensively used in various aspects of risk analysis, which are very pertinent in CPI. The purpose of the study is to produce an effective reference guide for scholars interested in applying Bayesian techniques to risk and reliability assessment in CPI. AbbreviationsAI artificial intelligence ASP accident sequence precursor BN Bayesian network CCF common cause failure CPI chemical process industry CPT conditional probability table CTBN continuous time Bayesian network DAG directed acyclic graph DBN dynamic Bayesian network DCS distributed control system DDT diagnostic decision tree DIF diagnostic importance factor DOOBN dynamic object-oriented Bayesian network DRA dynamic risk assessment DTBN discrete time Bayesian network EB empirical Bayes ESD emergency shutdown EVT extreme value theorem ET event tree FB fully Bayes FMEA failure mode effects analysis FFT fast Fourier transform FT fa...

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“…The widespread use of FTA is due to its simplicity, although such simplicity also has several drawbacks. The most prominent drawback is that FTA's limited modeling representation can only handle logical relations [12,13].…”

Section: Literature Reviewmentioning

confidence: 99%

Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net

Chen¹,

Liu

Chen³

2019

Applied Sciences

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This study proposes a cognition-adaptive approach for the administrative control of human-machine safety interaction through Internet of Things (IoT) data. As part of Industry 4.0, a human operator possesses various characteristics, but cannot be consistently understood as well as a machine. Thus, human-machine interaction plays an important role. This study focuses on incumbent challenges on the basis of estimated mental states. Given the operation logs from data recording hardware, a Hidden Markov model on top of a human cognitive model was trained to capture a production line worker’s sequential faults. Our study found that retaining workers’ attention is insufficient and tracking the state of perception is key to accomplishing production tasks. A safe workflow policy requires attention and perception. Accordingly, our proposed Petri Net enhances operation safety and improves production efficiency.

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A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks

Cited by 36 publications

References 6 publications

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Risk and reliability assessment in chemical process industries using Bayesian methods

Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net

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