Open AccessInduction Motor Fault Classification using Pattern Recognition Neural Network
Author(s) -
Shaina Grover,
Amandeep Sharma,
Lini Mathew,
Shantnu Chaterji
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i7591.078919
Subject(s) - induction motor , stator , artificial neural network , rotor (electric) , fault (geology) , bar (unit) , artificial intelligence , computer science , engineering , pattern recognition (psychology) , machine learning , control engineering , mechanical engineering , physics , voltage , seismology , geology , meteorology , electrical engineering
The industrial growth has escalated the use of induction motors as prime movers in modern industry. This is due to its low cost, simple construction and ruggedness. Although rugged, these may fail earlier than expected life due to, excessive mechanical, electrical and environmental stresses. Automatic Artificial Intelligence (AI)-based systems are nowadays widely employed in the domain of induction motor fault identification with high success rate. Artificial neural network are utilized extensively for the detection and diagnosis of various induction motor faults. These systems generally use supervised learning, where the models are pre-trained such that these are skilled enough to classify the absence or presence of faults in motor under investigation. In this paper, a highly effective approach for detection of different motor fault conditions, based on pattern recognition technique is presented. In the proposed method the statistical time domain features are computed from three phase motor current and used as inputs of ANN. Seven different classes of motor conditions: healthy, broken rotor bar, broken rotor bar with stator winding short circuit and inner and outer race bearing defects were considered. The results indicates that the proposed methodology is highly effective for diagnosis of various induction motor faults with high success rate.