Affiliations: [a] School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| [b] School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| [c] School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Correspondence:
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Corresponding author. T. Narendiranath Babu, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. E-mail: [email protected].
Abstract: This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %.
