| This thesis studies machine fault detection and diagnosis using Artificial Neural Networks (ANN). The ANN techniques include feedforward backpropagation networks (FFBPN), self organizing maps (SOM) and a combination of SOM and FFBPN. The particular application is on variable speed AC induction motors. The primary focus of the work is to employ appropriate technology to monitor and detect normal and faulty induction motors, provide a warning and diagnose the faults at an early stage.; Major faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using measurement data from stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs are discussed. This study shows that the feedforward ANN with a very simple internal structure can give satisfactory results, while SOMs can classify the type of motor faults during steady state working conditions. The experiment results also show that the feedforward ANN is the more promising scheme in the case where fault data from induction motors is available. (Abstract shortened by UMI.)... |
