Real-time model-based fault detection and diagnosis for alternators and induction motors

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.