کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
406329 | 678076 | 2015 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
SVM and PCA based fault classification approaches for complicated industrial process
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: SVM and PCA based fault classification approaches for complicated industrial process SVM and PCA based fault classification approaches for complicated industrial process](/preview/png/406329.png)
چکیده انگلیسی
This work studies the fault classification issue focused on complicated industrial processes. The basic multivariate statistical approaches, i.e. support vector machine (SVM) as well as principal component analysis (PCA), are studied for multi-fault classification purpose. The Tennessee Eastman (TE) challenging benchmark, which contains 21 abnormalities from real world, is finally utilized to show the effectiveness of the approaches. Such a conclusion can be drawn from the simulation results: although SVM is a powerful tool for multi-classification purposes, the standard PCA approach still shows satisfactory results with less computational efforts.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 167, 1 November 2015, Pages 636–642
Journal: Neurocomputing - Volume 167, 1 November 2015, Pages 636–642
نویسندگان
Chen Jing, Jian Hou,