TY - GEN
T1 - Principal component analysis for fault detection and diagnosis. Experience with a pilot plant
AU - Villegas, Thamara
AU - Fuente, María Jesús
AU - Rodríguez, Miguel
PY - 2010
Y1 - 2010
N2 - This paper describes the application of Principal Component Analysis (PCA) for fault detection and diagnosis (FDD) in a real plant. PCA is a linear dimensionality reduction technique. In order to diagnosis the faults, the PCA approach includes one PCA model for each system behavior, i.e., a PCA model for normal operation conditions and a PCA model for each faulty situation. Data set is generated in closed loop. The method of fault detection and diagnosis is based on the definition of threshold minimum. These are calculated by the Q statistics and levels of significance. The PCA models outputs (in this case the Q statistics) are compared with theirs thresholds minimum, with and without faults. The only one that does not violate it threshold says us the actual system situation, i.e., identify the fault. Finally, this technique is applied to a two tanks system, and can be demonstrated that it is possible to detect and identify faults.
AB - This paper describes the application of Principal Component Analysis (PCA) for fault detection and diagnosis (FDD) in a real plant. PCA is a linear dimensionality reduction technique. In order to diagnosis the faults, the PCA approach includes one PCA model for each system behavior, i.e., a PCA model for normal operation conditions and a PCA model for each faulty situation. Data set is generated in closed loop. The method of fault detection and diagnosis is based on the definition of threshold minimum. These are calculated by the Q statistics and levels of significance. The PCA models outputs (in this case the Q statistics) are compared with theirs thresholds minimum, with and without faults. The only one that does not violate it threshold says us the actual system situation, i.e., identify the fault. Finally, this technique is applied to a two tanks system, and can be demonstrated that it is possible to detect and identify faults.
KW - Fault detection
KW - Fault diagnosis
KW - Principal component analysis (PCA)
UR - http://www.scopus.com/inward/record.url?scp=79958742967&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:79958742967
SN - 9789604742578
T3 - International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics - Proceedings
SP - 147
EP - 152
BT - Advances in Computational Intelligence, Man-Machine Systems and Cybernetics - 9th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, CIMMACS'10
T2 - 9th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, CIMMACS'10
Y2 - 14 December 2010 through 16 December 2010
ER -