A Neuro Detector Based on the Cybernetic Concepts for Fault Detection in Electric Motors

In this study, an auto-associative neural network ( AANN) is designed as a fault detector using the cybernetic concepts. In this sen se, an artificial neural network structure is connected with a finite state system or a finite au tomata and an AANN topology is described as a virtual detector. In terms of the practical appli cation, vibration signals, which are taken from an induction motor of 5 HP for both the healthy and faulty motor cases, are considered in the spectral domain. The vibration signal presented in the healthy motor case is separated into 4 blocks and the spectral set of these blocks is used a input and target pattern sets during the training of the AANN. After the training process, a new vibration spectrum, which is defined in the faulty motor case is applied to this trained ne twork and the faulty case is determined by the error variation at output nodes of the AANN. In thi s application, the error signal shows huge amplitudes between 2 and 4 kHz as an indicator of t he bearing damage.