Real-Time Model-Based Fault Detection and Diagnosis for Alternators and Induction Motors

Abstract

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.