Multi-Class SVMs for Automatic Detection and Diagnosis inProcess Control Loops

Journal of Control Engineering and Applied Informatics

Nelendran Pillay¹ 

[1] DURBAN UNIVERSITY OF TECHNOLOGYDEPT. OF ELECTRONIC ENGINEERINGSOUTH AFRICA

关键词: controller performance assessment;    support vector machines;    autocorrelation function;    feature extraction;    PI controllers.;   

DOI  :  

学科分类：农业科学（综合）

来源: Societatea Romana de Automatica si Informatica Tehnica

【 摘 要 】

The aim of this paper is to present a novel framework using Multi-Class Support Vector Machines (MC-SVMs) to classify the performance of closed loop single-input-single-output feedback controllers. A SVM is trained to recognize descriptive statistical patterns originating from an Autocorrelation Function (ACF) of process data vectors. ACF patterns emanating from different closed loop behaviors are used in the feature extraction procedure. Simulation study and application to real world industrial data sets show that the MC-SVM classification tool is capable of detecting and diagnosing problematic control loops with very good accuracy and efficiency.

【 授权许可】

CC BY