Abstract
Online health monitoring of electric motors is of paramount interest to various applications. As the operation of industrial processes becomes more complex, the cost of health monitoring increases dramatically. To this end, much efforts have been directed towards enhancement of fault diagnostics and prognostics in electric motors, largely based on conventional signal processing and pattern classification. This paper formulates and experimentally validates a recently reported technique, called Symbolic Dynamic Filtering (SDF), for early detection of stator voltage imbalance in three-phase induction motors. The SDF-based imbalance detection algorithm is built upon the principles of wavelet transforms and symbolic time series analysis.
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