A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process
| Autor: | Efstratios N. Pistikopoulos, Melis Onel, Chris A. Kieslich |
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| Rok vydání: | 2020 |
| Předmět: |
Environmental Engineering
Computer science business.industry General Chemical Engineering Feature extraction Feature selection Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology Fault (power engineering) Fault detection and isolation Article Support vector machine 020401 chemical engineering Principal component analysis Benchmark (computing) Sensitivity (control systems) Artificial intelligence 0204 chemical engineering 0210 nano-technology business Biotechnology |
| Zdroj: | AIChE J |
| ISSN: | 0001-1541 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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