TY - JOUR
T1 - Early detection of stator voltage imbalance in three-phase induction motors
AU - Samsi, Rohan
AU - Ray, Asok
AU - Mayer, Jeffrey
N1 - Funding Information:
This work has been supported in part by NASA under Contract No. NNC07QA08P and Cooperative Agreement No. NNX07AK49A.
PY - 2009/1
Y1 - 2009/1
N2 - 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.
AB - 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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U2 - 10.1016/j.epsr.2008.06.004
DO - 10.1016/j.epsr.2008.06.004
M3 - Article
AN - SCOPUS:54049136119
SN - 0378-7796
VL - 79
SP - 239
EP - 245
JO - Electric Power Systems Research
JF - Electric Power Systems Research
IS - 1
ER -