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Fault Diagnosis of Helical Gear Box using Vibration Signals through Random Tree and Wavelet Features
Objectives: Gearbox, being an important component in the mechanism of many industrial machines can have a few faults mostly by fatigue cracking under cyclic contact stressing. Most of the implements presently being utilized in the industries for the gearbox fault diagnosis are dependent upon the vibration signals which are accumulated from the gearbox. Methods: A machine based learning approach has been utilized for the detection of faults with the utilization of vibration signals that have been acquired from helical gearbox setup. The features were extracted from the collected vibration signals using wavelets. The significant features were selected using a Decision Tree algorithm. The selected features from this approach were then classified using random tree algorithm and higher accuracy was achieved. Findings: The random tree algorithm used for the classification of the wavelets which were extracted from the vibration signals of the gearbox resulted in a classification accuracy of 90.4%. This classification accuracy is unique in terms of the vibration signals that have been acquired utilizing the accelerometer from the helical gearbox setup. The higher classification is achieved after feature extraction, selection and classification. Improvements/Applications: The classification accuracy achieved using the random tree algorithm was higher than the previously attained values for the gearbox. The higher accuracy would result in better fault diagnosis for the helical gearbox setup.
Decision Tree, Fault Diagnosis, Helical Gearbox, Machine Learning, Random Tree Algorithm, Wavelet Features.
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