Title :
Support vector machines for fault detection
Author :
Batur, Celal ; Zhou, Ling ; Chan, Chien-Chung
Author_Institution :
Dept. of Mech. Eng., Akron Univ., OH, USA
Abstract :
Support vector machines (SVMs), based on Vapnik´s statistical learning theory is a new tool that can be used for fault detection and isolation in dynamic systems. This paper presents a new approach that combines the system identification technique and the SVM learning algorithm for fault detection and isolation in dynamic systems. A conventional heat exchanger dynamics is used to illustrate the technique.
Keywords :
fault location; heat exchangers; learning automata; statistical analysis; SVM learning algorithm; fault detection; fault isolation; heat exchanger dynamics; statistical learning theory; support vector machines; Condition monitoring; Fault detection; Fault diagnosis; Least squares methods; Machine learning; Statistical learning; Support vector machines; System identification; Technological innovation; Training data;
Conference_Titel :
Decision and Control, 2002, Proceedings of the 41st IEEE Conference on
Print_ISBN :
0-7803-7516-5
DOI :
10.1109/CDC.2002.1184704
