Belt Drive Condition Monitoring using Generalised Gaussian Distribution based Entropy Features

Belt drives are important and commonly used in industry to transmit power. In this paper, the vibration analysis is carried out on the centrifugal fan belt drive under different operating conditions. The real-time vibration signals were acquired for analysing and predicting the healthy and fault conditions at different operating speeds. Initially, the acquired signals were segmented, and the feature of each segment was calculated. An unique Generalized Gaussian Distribution - Refined Composite Multiscale Dispersion Entropy is considered for extracting the features with different shape parameters
$\alpha$
. The primary feature is chosen from the feature set with the smallest standard deviation for a specific data set. Finally, the primary features of different vibration signals are then used to train a Neural Network to predict the healthy and faulty conditions of the belt drive. In comparison to the other approaches taken into consideration in this study, the suggested method has the best accuracy, precision, and recall.