Fault Diagnostics of a Gearbox with Acoustic Signals Using Wavelets and Decision Tree

Objectives: This study aims at devising a methodology for accurately predicting the different fault conditions of gears in a gearbox using acoustic signals. Statistical Analysis: The acoustic signals are captured for several artificially created fault conditions of different magnitude and the wavelet features are extricated from captured acoustic signals. Subsequently,prominent features are selected by utilizing J48 Decision tree which discerns the most dominant traits among the allocated data obtained from wavelet transform of the acoustic signals followed by Random Forest for the classification of features. Findings: Out of a total of eleven features extracted, six were selected through Decision Tree and Random forest was used for feature classification of acoustic signals using wavelet features. Several iterations were conducted on the wavelet features by varying different parameters and the maximum percentage accuracy was found to be 99.76%. The instances of misclassification of features were minimal in Random Forest and it proved to be an efficient and precise classifier. Hence, Random Forest proved to be an easy to use, fast and accurate classifier that could classify various kinds of wavelet features efficiently. Applications: The methodology can be used to provide accurate real time results about the condition of gear teeth.