Efficient and Secure Communication in Vanets Using Block Chain

Abstract

This project describes the design and implementation of a non-contact vibration picker for capturing data from spinning machinery in order to detect bearing faults early. The Hilbert transform is used to denoise the collected vibration signals, and the dataset is then analyzed by Principal Component Analysis (PCA) and Sequential Floating Forward Selection (SFFS) for dimensionality reduction and feature selection, respectively. The most essential attributes are then used to identify and categorize various bearing issues using Support Vector Machines (SVM) and Artificial Neural Network (ANN) algorithms. The entire methodology provides an effective and proactive way for bearing health monitoring and maintenance, emphasizing fast defect identification and leading to significant savings in time, effort, and equipment maintenance costs.