Combination of KPCA and causality analysis for root cause diagnosis of industrial process fault

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

doi:10.1002/cjce.22852

Cited by 25 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the real applications, the successful fault source diagnosis needs the integration of many technologies including contribution plot, process casual analysis, and machine learning based fault classification, etc. 40,41 Further Discussions. In the proposed method, two important parameters are involved including the neighbor number k n for the local structure analysis and the moving window width w for the local probability analysis.…”

Section: ■ Case Studiesmentioning

confidence: 99%

“…For the complex fault with more than one variable, the contribution plot is still difficult to accurately locate and isolate the real fault variables. In the real applications, the successful fault source diagnosis needs the integration of many technologies including contribution plot, process casual analysis, and machine learning based fault classification, etc. , …”

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes

Deng

Cai

Cao

et al. 2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Kernel principal component analysis (KPCA) has been widely applied to the nonlinear process fault diagnosis field. However, it often does not perform well in the case of incipient faults because of the omission of local data information. To overcome this problem, one enhanced KPCA method, called the two-step localized KPCA (TSLKPCA), is proposed for incipient fault diagnosis in this work. The two steps are designed to mine the local data information better. At the first step, the KPCA optimization objective is modified by integrating the local structure preservation so that the extracted kernel components preserve the global and local data structure information simultaneously. At the second step, for the extracted kernel components, the local probability information is further mined by the Kullback Leibler divergence (KLD), which measures the variations of the kernel components’ probability distributions. On the basis of these two steps, the original kernel components are transformed into the KLD components, and the corresponding model is developed for incipient fault detection. To isolate the faulty variables, the contribution plot is constructed based on the mutual information between the measured variables and the KLD components obtained by TSLKPCA. Finally, two simulations of a numerical example and the continuous stirred tank reactor (CSTR) control system show that the proposed method has good incipient fault detection and diagnostic performance.

show abstract

Section: ■ Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes

Deng

Cai

Cao

et al. 2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Many dimension reduction methods in machine learning have been introduced into chemical process modeling with remarkable results, mainly consisting of two types: feature extraction (FE) and feature selection (FS). Methods of the former type, such as PCA, kernel PCA (KPCA), , partial least squares (PLS), locally linear embedding (LLE), and linear discriminant analysis (LDA), transform the original high-dimensional features into new sets of features in lower dimensions, and model dimensions are reduced. In contrast, FS methods do not transform the data and attempt to select the optimal subset from the full features set.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Data Dimensionality Reduction for Chemical Process Modeling Based on the Information Criterion Related to Data Association and Redundancy

Luo

Chen

et al. 2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Chemical process modeling is the basis for research and applications in related fields. With the development of industrial informatization, data-driven process modeling techniques are increasingly applied in chemical processes, helping to obtain more accurate results with less model development costs. However, due to the high-dimensional nonlinear characteristics of most chemical processes, problems such as the "curse of dimensionality" and information redundancy will render the models more prone to overfitting with reduced accuracies and weaker generalization abilities. Many data dimensionality reduction methods are adopted to mitigate the above problems, but most are limited by inaccurate association measurements and weak redundancy exclusion. In this paper, the extensive existence of data associations and information redundancies is first revealed through the analysis from an information-theoretic perspective. Then, a feature selection method based on conditional refined maximal information coefficient maximization (CRMICM) is proposed to improve the consistency of association measurement and the accuracy of redundancy estimation with limited samples. The final prediction modeling test for an actual fluidized catalytic cracking (FCC) process proves the extensive association between the variables and targets. Only a few variables are essential for the modeling, while the rest are redundant. Compared with other methods, CRMICM achieves the best dimensionality reduction effects on the FCC process data regarding the number of features and model prediction accuracy, showing its good applicability for chemical processes.

show abstract

Combination of KPCA and causality analysis for root cause diagnosis of industrial process fault

Cited by 25 publications

References 38 publications

Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes

Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes

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

Adaptive Data Dimensionality Reduction for Chemical Process Modeling Based on the Information Criterion Related to Data Association and Redundancy

Contact Info

Product

Resources

About