Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

Gharahbagheri, H.; Imtiaz, Syed; Khan, Faisal

doi:10.1021/acs.iecr.6b01916

Cited by 122 publications

(50 citation statements)

References 41 publications

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“…The second situation shows the nonlinear correlation between Y and X. The last Y is another random sequence, as seen in Equation (7). Mutual information entropy values in each situation are calculated with different scales shown in Figure 1.…”

Section: Correlation Testmentioning

confidence: 99%

Information entropy theory for steam turbine system monitoring study

Cui

et al. 2020

Engineering Reports

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A steam turbine is one of the components in a power plant. Considered as a strong coupling system, steam turbines may possess correlation information among the operation parameters. In this article, a correlation extraction method based on mutual information theory is proposed for performance monitoring. First, five functions between two variables including linear and nonlinear relationships are tested to validate the proposed method. In the first industrial case study, a sensor fault detection based on the governing stage pressure is conducted on the abnormal fluctuation mutual entropy values calculated by operation data. The results show the proposed method may effectively be used to detect sensor errors and locate the first error sample. Furthermore, uncertainty analysis on the heat rate is studied to sort the importance degree of the relative parameters. The mutual information entropy difference values are then calculated to quantify the sensitivity degree of the operation parameters to the heat rate index. The results in this case study are in accordance with the sequences by the Guide to the Expression of Uncertainty Measurement (GUM) and Monte Carlo (MC) methods. In conclusion, mutual information entropy may hence be used as a new solution for turbine monitoring, combined with information theory.

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Section: Correlation Testmentioning

confidence: 99%

Information entropy theory for steam turbine system monitoring study

Cui

et al. 2020

Engineering Reports

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“…This is because both approaches entail differentiating the statistical index, which is difficult if the chain involves a kernel function [86]. Nevertheless, many researchers have derived analytical expressions for either kernel contributions-based diagnosis [66,79,81,83,87,94,119,127,133,136,146,150,156,157,162,164,194,213,241,268,275,276,278,279,288,289,293] or kernel reconstructions-based diagnosis [86,117,140,155,161,163,176,217,236,254,265,285]. However, most derivations are applicable only when the kernel function is the RBF, Equation (5).…”

Section: Diagnosis By Fault Identificationmentioning

confidence: 99%

“…The Bayesian network is an architecture for causality analysis, where the concepts of Granger causality and transfer entropy are used to define if one variable is caused by another based on their time series data. In 2017, Gharahbagheri et al [236,237] used these concepts together with the residuals from kernel PCA models to generate a causal map for a fluid catalytic cracking unit (FCCU) and the TEP. A statistical software called Eviews was used to perform causality analysis.…”

Section: Diagnosis By Causality Analysismentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.

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“…Many researchers also explored the BN-based approach for fault isolation and multiple-fault diagnosis of different machinery systems [16][17][18], e.g. centrifugal compressors [19], chillers [20,21], chemical processes [22] and gear pumps [23]. Cai and his research team carried out a series of works on machinery fault diagnosis using BNs and the extension over the years [9,[24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

Wang

Stetsyuk

et al. 2019

ISA Transactions

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Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model.

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Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

Cited by 122 publications

References 41 publications

Information entropy theory for steam turbine system monitoring study

Information entropy theory for steam turbine system monitoring study

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

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