Dynamic Observation of ML for Robotic  Perception: A Survey of Recent Innovations

Author(s)	J. Bhaskar, L. M. Bharathi
Country	India
Abstract	This project outlines the development and deployment of a non-contact vibration sensor designed to gather data from rotating machinery to facilitate early detection of bearing faults. The Hilbert transform is employed to reduce noise in the vibration signals, which are then processed using Principal Component Analysis (PCA) for dimensionality reduction and Sequential Floating Forward Selection (SFFS) for feature selection. Key features are utilized to identify and classify various bearing problems through Support Vector Machines (SVM) and Artificial Neural Network (ANN) algorithms. This approach offers a proactive and efficient solution for monitoring bearing health, emphasizing rapid fault detection and resulting in considerable savings in time, effort, and maintenance costs.
Keywords	Machine Learning, Fault Prediction, Fuzzy Convolution Neural Network (FCNN), Heterogeneous Sensing Data Fusion
Field	Engineering
Published In	Volume 4, Issue 4, July-August 2023
Published On	2023-07-28

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Advanced International Journal for Research