A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Cai, Baoping; Zhao, Yubin; Liu, Hanlin; Xie, Min

doi:10.1109/tpel.2016.2608842

Cited by 362 publications

(136 citation statements)

References 28 publications

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“…The advantage with respect to a classical probabilistic temporal model like Markov chains is that the dynamic Bayesian networks are stochastic transition models factored over a number of random variables, over which a set of conditional dependency assumption is defined [4][5][6]. We adopt dynamic Bayesian networks to predict the future state of variables taking into consideration the observation of variables up to now.…”

Section: Series Parallel and Voting Systemmentioning

confidence: 99%

A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

Cai

Xie

Liu

et al. 2017

AIP Conference Proceedings

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Abstract. The word resilience originally originates from the Latin word "resiliere", which means to "bounce back". The concept has been used in various fields, such as ecology, economics, psychology, and society, with different definitions. In the field of critical infrastructure, although some resilience metrics are proposed, they are totally different from each other, which are determined by the performances of the objects of evaluation. Here we bridge the gap by developing a universal critical infrastructure resilience metric from the perspective of reliability engineering. A dynamic Bayesian networks-based assessment approach is proposed to calculate the resilience value. A series, parallel and voting system is used to demonstrate the application of the developed resilience metric and assessment approach.

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Section: Series Parallel and Voting Systemmentioning

confidence: 99%

A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

Cai

Xie

Liu

et al. 2017

AIP Conference Proceedings

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“…PCA is able to use multivariate data for sensor selection of BN input . Related works used this integration by modeling causal dependencies among the principal components (PCs) for prognostics and diagnostics . They used PCA to reduce dimensions of BN into PCA‐BN model for inferring the accidental consequences of an abnormality in the works.…”

Section: Introductionmentioning

confidence: 99%

Logic‐based probabilistic network model to detect and track faults in a process system

Tahoon

Rusli

Khan

et al. 2019

Process Safety Progress

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Process systems are becoming complex due to a higher dependency among operational variables and complex control loops. Principal component analysis (PCA) is widely used to reduce the dimensionality of the complex process systems, while Bayesian networks (BNs) are increasingly employed to model relationships among the operational variables. This article integrates these two methods (BN and PCA) through a logic‐based approach to study the fault conditions of a process system. A distillation pilot plant is used to test the integrated approach. The process monitoring data are analyzed using the PCA to identify the abnormality variables while the BN is developed using data‐driven learning. The variable dependency in the BN nodes is learned through maximum likelihood estimation. The results of the proposed approach are compared against the logic‐based full BN model. The study observes that the logic‐based PCA‐BN approach proposed improves the reliability of fault detection. While the logic‐based full BN provides a better understanding of fault propagation path through the unit, which helps track and troubleshoot the detected faults.

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“…Traditional risk modeling approaches are designed based on the Newtonian paradigm in which it is believed that systems can be easily understood by breaking them down to their smallest elements and describing how these elements interact . Most published risk and reliability analysis methods are based on statistical models of time‐to‐failure data assuming that systems' behavior is stable enough to accurately predict over their lifetime . The past cannot be compared well to current circumstances, and there is no coherent statistical model of system failure time that is proven to accurately predict the behavior of a system over its lifetime.…”

Section: Introductionmentioning

confidence: 99%

Lifecycle risk assessment of a technological system using dynamic Bayesian networks

Ashrafi

Zadeh

2017

Quality & Reliability Eng

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Investigations of technological systems accidents reveal that technical, human, organizational, as well as environmental factors influence the occurrence of accidents. Despite these facts, most traditional risk assessment techniques focus on technical aspects of systems and have some limitations of incorporating efficient links between risk models and human and organizational factors. This paper presents a method for risk analysis of technological systems. Application of the presented framework makes it possible to analyze the influence of technical, human, organizational, and environmental risk factors on system safety. It encompasses system lifecycle from design to operational phase to give a comprehensive picture of system risks. The developed framework comprises the following main steps: (1) development of a conceptual risk analysis framework, (2) identifying risk influencing factors in different levels of technical, human, organizational, and environmental factors providing the possibility of analyzing interactions in a multi‐level system, (3) modeling system risk using dynamic Bayesian network (DBN), (4) assignment of probabilities and risk quantification in node probability tables (NPTs) based on industry records and experts extracted knowledge, (5) implementation of the model for wind turbines risk analysis combining use of V‐model, risk factors, and DBN in order to analyze the risk, and (6) analyzing different scenarios and the interactions in different levels. Finally, the various steps of the framework, the research objective fulfillment, and case study results are presented and discussed.

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A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Cited by 362 publications

References 28 publications

A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

Logic‐based probabilistic network model to detect and track faults in a process system

Lifecycle risk assessment of a technological system using dynamic Bayesian networks

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