TY - GEN
T1 - Real-time model-based fault detection and diagnosis for alternators and induction motors
AU - Leite, Daniel F.
AU - Hell, Michel B.
AU - Diez, Patrícia H.
AU - Ganglio, Bernardo S.L.
AU - Nascimento, Lucas O.
AU - Costa, Pyramo
PY - 2007
Y1 - 2007
N2 - This paper describes a real-time model-based fault detection and diagnosis software. The Electric Machines Diagnosis System (EMDS) covers field winding shorted-turns fault in alternators and stator windings shorted-turns fault in induction motors. The EMDS has a modular architecture. The modules include: acquisition and data treatment; well-known parameters estimation algorithms, such as Recursive Least Squares (RLS) and Extended Kalman Filter (EKF); dynamic models for faults simulation; faults detection and identification tools, such as M.L.P. and S.O.M. neural networks and Fuzzy C-Means (FCM) technique. The modules working together detect possible faulty conditions of various machines working in parallel through routing. A fast, safe and efficient data manipulation requires a great DataBase Managing System (DBMS) performance. In our experiment, the EMDS real-time operation demonstrated that the proposed system could efficiently and effectively detect abnormal conditions resulting in lower-cost maintenance for the company.
AB - This paper describes a real-time model-based fault detection and diagnosis software. The Electric Machines Diagnosis System (EMDS) covers field winding shorted-turns fault in alternators and stator windings shorted-turns fault in induction motors. The EMDS has a modular architecture. The modules include: acquisition and data treatment; well-known parameters estimation algorithms, such as Recursive Least Squares (RLS) and Extended Kalman Filter (EKF); dynamic models for faults simulation; faults detection and identification tools, such as M.L.P. and S.O.M. neural networks and Fuzzy C-Means (FCM) technique. The modules working together detect possible faulty conditions of various machines working in parallel through routing. A fast, safe and efficient data manipulation requires a great DataBase Managing System (DBMS) performance. In our experiment, the EMDS real-time operation demonstrated that the proposed system could efficiently and effectively detect abnormal conditions resulting in lower-cost maintenance for the company.
UR - http://www.scopus.com/inward/record.url?scp=35048850905&partnerID=8YFLogxK
U2 - 10.1109/IEMDC.2007.383577
DO - 10.1109/IEMDC.2007.383577
M3 - Conference contribution
AN - SCOPUS:35048850905
SN - 1424407435
SN - 9781424407439
T3 - Proceedings of IEEE International Electric Machines and Drives Conference, IEMDC 2007
SP - 202
EP - 207
BT - Proceedings of 2007 IEEE International Electric Machines and Drives Conference, IEMDC 2007
T2 - IEEE International Electric Machines and Drives Conference, IEMDC 2007
Y2 - 3 May 2007 through 5 May 2007
ER -