Classification of unbalance and misalignment in induction motors using orbital analysis and associative memories

Fault detection in induction motors is an important task in industry when production greatly depends of the functioning of the machine. This paper presents a new computational model for detecting misalignment and unbalance problems in electrical induction motors. Through orbital analysis and signal vibrations, unbalance and misalignment motor faults can be mapped into patterns, which are processed by a classifier: the Steinbuch Lernmatrix. This associative memory has been widely used as classifier in the pattern recognition field. A modification of the Lernmatrix is proposed in order to process real valued data and improve the efficiency and performance of the classifier. Experimental patterns obtained from induction motors in real situations and with a certain level of unbalance or misalignment were processed by the proposed model. Classification results obtained in an experimental phase indicate a good performance of the associative memory, providing an alternative way for recognizing induction motor faults.