ArticleAIChE journal. American Institute of Chemical Engineers2019
A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process.
Article in AIChE journal. American Institute of Chemical Engineers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An evolutionary weighted feature influence factor feature selection method for fault detection in the Tennessee Eastman complex chemical process.Scientific reports · 2026Article
- An optimized ensemble framework for machinery fault detection in IoT environments.Scientific reports · 2026Article
- Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression.Health information science and systems · 2022Article
- Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.Scientific reports · 2022Article
- Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data.Sensors (Basel, Switzerland) · 2021Article
- Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry.Sensors (Basel, Switzerland) · 2021Article
- A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities.AIChE journal. American Institute of Chemical Engineers · 2020Article
- Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.PLoS computational biology · 2020Article
- Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control.Industrial & engineering chemistry research · 2020Article
- Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization.PloS one · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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