TY - GEN
T1 - Fault detection and diagnosis for nonlinear systems
T2 - A support vector machine approach
AU - Ortaç-Kabaoglu, Rana
AU - Eksin, Ibrahim
AU - Yesil, Engin
AU - Güzelkaya, Müjde
PY - 2009
Y1 - 2009
N2 - In this paper, a fault detection and diagnosis (FDD) technique for nonlinear systems based on support vector machines (SVM) is presented. Support vector regression (SVR) has been used in fault detection process and support vector classification (SVC) has been used in diagnosis process. In fault detection process, the confidence band idea represents the normal operating conditions of the system. The upper and the lower boundaries of the confidence band are modelled by two different SVR machines. A fault is detected when an output signal exceeds the upper or lower bounds of the generated confidence band. A support vector multi-classification method, one-against-all, has been used to classify the occurring fault within the group of expected and predefined faults in technical system. The performance of the proposed FDD method is illustrated on simulation example involving a two-tank water level control system under faulty conditions.
AB - In this paper, a fault detection and diagnosis (FDD) technique for nonlinear systems based on support vector machines (SVM) is presented. Support vector regression (SVR) has been used in fault detection process and support vector classification (SVC) has been used in diagnosis process. In fault detection process, the confidence band idea represents the normal operating conditions of the system. The upper and the lower boundaries of the confidence band are modelled by two different SVR machines. A fault is detected when an output signal exceeds the upper or lower bounds of the generated confidence band. A support vector multi-classification method, one-against-all, has been used to classify the occurring fault within the group of expected and predefined faults in technical system. The performance of the proposed FDD method is illustrated on simulation example involving a two-tank water level control system under faulty conditions.
KW - Fault detection and diagnosis
KW - Support vector classification and regression
KW - Two tank water level system
UR - http://www.scopus.com/inward/record.url?scp=79955711342&partnerID=8YFLogxK
U2 - 10.3182/20090921-3-TR-3005.00063
DO - 10.3182/20090921-3-TR-3005.00063
M3 - Conference contribution
AN - SCOPUS:79955711342
SN - 9783902661661
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
BT - 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing
PB - IFAC Secretariat
ER -