کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
689892 | 889650 | 2009 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fault detection and diagnosis in process data using one-class support vector machines
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
تکنولوژی و شیمی فرآیندی
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چکیده انگلیسی
In this paper, a new approach for fault detection and diagnosis based on One-Class Support Vector Machines (1-class SVM) has been proposed. The approach is based on a non-linear distance metric measured in a feature space. Just as in principal components analysis (PCA) and dynamic principal components analysis (DPCA), appropriate distance metrics and thresholds have been developed for fault detection. Fault diagnosis is then carried out using the SVM-recursive feature elimination (SVM-RFE) feature selection method. The efficacy of this method is demonstrated by applying it on the benchmark Tennessee Eastman problem and on an industrial real-time Semiconductor etch process dataset. The algorithm has been compared with conventional techniques such as PCA and DPCA in terms of performance measures such as false alarm rates, detection latency and fault detection rates. It is shown that the proposed algorithm outperformed PCA and DPCA both in terms of detection and diagnosis of faults.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Process Control - Volume 19, Issue 10, December 2009, Pages 1627-1639
Journal: Journal of Process Control - Volume 19, Issue 10, December 2009, Pages 1627-1639
نویسندگان
Sankar Mahadevan, Sirish L. Shah,