Acoustical condition monitoring of a mechanical gearbox using artificial neural networks

Abstract

The work presented here forms part of a study into the application of self-learning networks to the complex field of machine condition monitoring. There are already several methods by which machines can be automatically monitored, but the development of a simplified nonintrusive "intelligent" system would be advantageous. Some work has been undertaken on the application of time encoded speech (TES) to automatic speech recognition using neural networks. It seemed feasible to try a similar technique to classify the acoustic emissions of a mechanical object. Initial experimentation was carried out using the speech system on a diesel engine. However the implementation described here involves a simplified form of data application to that employed previously. It consists of a simple conversion of microphone TES acoustic data into a matrix of frequency of code occurrence which can be directly applied to an artificial neural network (ANN).<>