|
One of the research problems investigated these days is early fault detection. To this aim, advanced signal processing algorithms are employed. The present paper makes an attempt at early fault detection in a toothed gear. In order to evaluate the technical conditio, artificial neural networks were used. The suport vector machines is a relatively new and rarely employed technique for evaluating the conditio of machines, particularly toothed gears. The available literature offers very promising results of using this method. In order to compare the obtained results, a multi-layer perceptron network was created. Such standard neural network guarantees high effectiveness. The vibration signal obtained from a sensor is seldom a material for direct analysis. First, it needs to be processed to bring out the informative part of the signal. To this aim, a wavelet transform was used. The presented results concern both a „raw” vibration signal and processed one, investigated for two neural networks. The wavelet transform has proved to significantly improve the accuracy of condition evaluation, and the results obtained by the two networks are consistent with one another.
|
