Real-Time Monitoring of Machine Learning for Robotic Perception: An Overview of Emerging Patterns

Author(s)	Saifeena Narul Afwah
Country	Indonesia
Abstract	This project outlines the development and deployment of a non-contact vibration sensor designed to capture data from rotating machinery for early detection of bearing faults. The collected vibration signals undergo denoising using the Hilbert transform. Subsequently, Principal Component Analysis (PCA) and Sequential Floating Forward Selection (SFFS) are applied for dimensionality reduction and feature selection, respectively. The selected essential features are then utilized with Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to detect and categorize various bearing issues. This comprehensive approach offers an efficient and proactive method for monitoring bearing health and maintenance, emphasizing rapid defect identification and resulting in significant time, effort, and equipment maintenance cost savings.
Keywords	Machine Learning, Fault Prediction, Fuzzy Convolution Neural Network (FCNN), Heterogeneous Sensing Data Fusion
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2022
Published On	2022-02-10

About AIJFR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-91574-37535	editor@aijfr.com

Advanced International Journal for Research