Robust one-class SVM for fault detection

Xiao, Yingchao; Wang, Huangang; Wang, Xu; Zhou, Junwu

doi:10.1016/j.chemolab.2015.11.010

Cited by 65 publications

(27 citation statements)

References 32 publications

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“…Table 2 compares the performance of this study to the other existing data-driven process monitoring techniques (Mahadevan and Shah, 2009; Yin et al, 2014; Xiao et al, 2016) in terms of fault detection rate. This comparison demonstrates the power of the proposed data-driven process monitoring framework.…”

Section: Resultsmentioning

confidence: 99%

“…Then, we present the application of the nonlinear (Kernel-dependent) SVM-based feature selection algorithm to process monitoring of continuous processes. Previous studies have used SVM for fault detection in chemical processes (Mahadevan and Shah, 2009; Yin et al, 2014; Xiao et al, 2016). Here, fault detection is achieved through two-class SVM models, where the feature selection algorithm further improves the model accuracy and also reveals diagnosis of the detected fault.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Fault Detection and Identification in Continuous Processes via nonlinear Support Vector Machine based Feature Selection

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Kieslich

Guzman

et al. 2018

Computer Aided Chemical Engineering

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Rapid detection and identification of process faults in industrial applications is crucial to sustain a safe and profitable operation. Today, the advances in sensor technologies have facilitated large amounts of chemical process data collection in real time which subsequently broadened the use of data-driven process monitoring techniques via machine learning and multivariate statistical analysis. One of the well-known machine learning techniques is Support Vector Machines (SVM) which allows the use of high dimensional feature sets for learning problems such as classification and regression. In this paper, we present the application of a novel nonlinear (kernel-dependent) SVM-based feature selection algorithm to process monitoring and fault detection of continuous processes. The developed methodology is derived from sensitivity analysis of the dual SVM objective and utilizes existing and novel greedy algorithms to rank features that also guides fault diagnosis. Specifically, we train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy of the fault detection models and perform fault diagnosis. We present results for the Tennessee Eastman process as a case study and compare our approach to existing approaches for fault detection, diagnosis and identification.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous Fault Detection and Identification in Continuous Processes via nonlinear Support Vector Machine based Feature Selection

Onel

Kieslich

Guzman

et al. 2018

Computer Aided Chemical Engineering

View full text Add to dashboard Cite

show abstract

“…SVM formulations have been continuously improved to tackle wide range of engineering problems involving classification, regression, outlier detection as well as clustering analysis. They have drawn significant interest for fault detection due to their high generalization and effective nonlinear data handling ability . The main idea behind SVM classification is the following.…”

Section: Svm Classification: Key Concepts and Application In Continuomentioning

confidence: 99%

“…The Tennessee Eastman process (Figure ), an extensively used benchmark case for comparative assessment of process monitoring algorithms, was designed by the Eastman Chemical Company . Numerous data‐driven fault detection and diagnosis methodologies tested with the Tennessee Eastman process are available in the literature . The process is based on a real industrial process, in which the components, kinetics, and operating conditions have been modified for proprietary reasons .…”

Section: Tennessee Eastman Process: Model and Datasetmentioning

confidence: 99%

“…However, the assumption of Gaussian distributed process data poses limitation in producing accurate fault detection and diagnosis with these techniques . Other data‐based methods centering around classification/regression‐based analysis have been proposed that employ artificial neural network (ANN) and more recently deep learning algorithms, classification and regression decision trees (CART) as well as different support vector machines (SVMs) formulations being support vector classification (SVC), support vector regression (SVR), and support vector data description (SVDD) . In particular, a major advantage of SVMs is their ability to provide nonlinear and robust models for non‐Gaussian distributed process data, and due to their succinct representation as convex nonlinear optimization problem to obtain global parameters for models.…”

Section: Introductionmentioning

confidence: 99%

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A nonlinear support vector machine‐based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process

2019

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In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dual C‐SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault‐specific two‐class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results. © 2018 American Institute of Chemical Engineers AIChE J, 65: 992–1005, 2019

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