Fault Diagnosis of Helical Gearbox through Vibration Signals using Wavelet Features, J48 Decision Tree and Random Forest Classifiers

Abstract

Objective: Gearbox being the backbone of transmission system is designed and manufactured very carefully so that there is minimum compliance in the system. However, there are still faults and failures which usually occur in the system. The failure in helical gearbox is more prominent in bearings rather than gears which are the main components of the system. Gearbox is susceptible to failures because of reasons like misalignment, vibration and shocks. In this paper wavelet feature extraction is used along with random forest algorithm to diagnose faults in gearbox. The vibration signals were used for extracting wavelet features. Features were selected using J48 Decision Tree and were classified using random forest algorithm. A detailed study has been done to ensure that the optimum number of features was used and the factor was iterated so that maximum classification accuracy is obtained. The results are presented along with the conclusion. Method Analysis: The classification accuracy is obtained by 3 steps namely, feature extraction, feature selection and feature classification. By obtaining the Decision Tree the most important factors are selected to obtain maximum classification accuracy at minimum number of features to reduce calculations in real time application. The number of features and depth of data is iterated to obtain the maximum classification accuracy. Findings: Through this research random forest algorithm was tested for fault diagnosis of gearbox and a better classification accuracy was obtained. These results can be further used for fault diagnosis in industries for any gearbox related problems. Application/Improvements: An extensive investigation is done by a random forest algorithm which produced better forecasting than the other algorithms. Based on the overall study, random forest was found as the most preferred classification algorithm that achieved the best classification accuracy of 93.08% which is better than the other algorithms.