An Investigation of the Application of Sound Spectrum Features in Classifying Impeccable and Defective Gears

The purpose of this study is to provide a systematic approach to address the industrial challenge of recognizing impeccable and defective gears by analyzing the spectrum of sound waves produced by the gears when in operation if attached to a testing circuit during sorting. This work classifies the gears into two classes, impeccable and defective classes. The spectra from several samples from both impeccable and defective gears were analyzed and five audio features were extracted from their spectra namely short-time energy, zero-crossing rate, Spectral entropy, pitch, and block energy entropy. It was found that there is a significant difference between the two classes. In training the algorithm, 5D features vectors from 20 feature vectors from impeccable gears and another 5D features vector from 20 defective gears as training samples to determine the discriminating point. In testing the algorithm, 20 samples were extracted randomly from the impeccable and defective gears but whose status was clearly known by visual inspection. The results of gear status given by the algorithm were compared to that of visual inspection. The samples were classified by using the Support Vector Machine (SVM) learning classification approach. A promising efficiency of 95% was obtained.