Fault detection and pathway analysis using a dynamic Bayesian network

Amin, Md. Tanjin; Khan, Faisal; Imtiaz, Syed

doi:10.1016/j.ces.2018.10.024

Cited by 112 publications

(33 citation statements)

References 31 publications

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“…Gharahbagheri et al adopted KPCA and the Bayesian network for process monitoring and fault diagnosis in TEP and a catalytic cracking unit [203]. Amin et al proposed a dynamic Bayesian network model that could be updated with monitored process data, through which the fault could be detected and diagnosed precisely [204]. More Bayesian network-based fault diagnosis methods are covered in the review by Cai et al [201].…”

Section: Probability Reasoning-based Methodsmentioning

confidence: 99%

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

Sun

2022

Processes

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Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. The implementation framework of data-driven process monitoring methods is first introduced. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. Finally, the challenges and possible solutions for actual industrial applications are discussed.

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Section: Probability Reasoning-based Methodsmentioning

confidence: 99%

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

Sun

2022

Processes

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“…It is worth noting that most above techniques require conditional probability distributions of node states under concerned fault conditions, which is not easy to be satisfied. This limitation was addressed in two DBN based studies (Yu & Rashid, 2013;Amin et al, 2019). They developed novel indexes to quantify the abnormality likelihood at each node and assumed the nodes in true fault propagation pathways should present higher abnormality likelihood.…”

Section: Bayesian Networkmentioning

confidence: 99%

Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities

et al. 2020

Control Engineering Practice

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“…This interpretable structure sets them apart from ‘black-box’ concepts of other machine-learning methods. Besides, there are well-established algorithms for the automatic learning of Bayesian networks from data, and they are widely used in Systems Biology, e.g., to model cellular networks [ 1 ], protein signaling pathways [ 2 ], gene regulation networks [ 3 – 5 ], or as medical decision support systems [ 6 ]. For a thorough introduction to Bayesian networks see for example Koski and Noble [ 7 ] or Koller and Friedman [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

et al. 2021

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In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network.

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Fault detection and pathway analysis using a dynamic Bayesian network

Cited by 112 publications

References 31 publications

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities

From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

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