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Fault Diagnostics of a Gearbox via Acoustic Signal using Wavelet Features, J48 Decision Tree and Random Tree Classifier


  • School of Mechanical and Building Sciences, VIT University, Chennai – 600127, Tamil Nadu, India
  • Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur - 482005, Madhya Pradesh, India


Objectives: Heart of the transmission system in most machineries are gears for efficient power transmission. Even minor faults in gear can lead to major losses in terms of energy as well as in terms of money. The unwanted by-product while operating gear box are vibration and acoustic signals, which can be used for the condition monitoring and fault diagnosis of the gearbox. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the acoustic signals produced by the gearbox. Methods/Analysis: The acoustic signals were captured using microphone from a gearbox with artificially created fault conditions. A comprehensive study was carried out using different discrete wavelet transformations for feature extraction which was further used in generating J48 decision tree algorithm and subsequently it was employed for selection and classification of the extracted features. Finding: Through this study the classification accuracy obtained is 97.619% by varying the different parameter to achieve the highest accuracy level. Data used in this study is exclusively obtained through experiment and subsequently through J48 decision tree and random tree classification accuracy level is studied to accomplish the highest accuracy. Novelty/Improvements: The comparison of different discrete wavelet transforms of the acoustic signals proved Daubechies 5 Discrete Wavelet Transform is the best suited one to use. The methodology yielded a satisfactory classification accuracy of 97.619%, which is higher than what was obtained by similar experiments with different methodology till date. The results and their analysis is discussed in the study. The performance of this methodology may be further improved by using different classifiers.


Acoustic Signals, Gearbox, J48 Decision Tree, Random Tree, Wavelets

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