Comparative analysis of Direct and Indirect Model Reference Adaptive Control by Extended Kalman Filter
Volume-3 | Issue-3
Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review
Volume-3 | Issue-4
Integrated Renewable Energy Management System for Reduced Hydrogen Consumption using Fuel Cell
Volume-3 | Issue-1
Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Artificial Intelligence based Business Process Automation for Enhanced Knowledge Management
Volume-3 | Issue-2
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Design of Effective Smart Communication System for Impaired People
Volume-2 | Issue-4
Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Power Transfer Capability Recognition in Deregulated System under Line Outage Condition Using Power World Simulator
Volume-3 | Issue-4
Transformer Oil Diagnostic Tests Analysis using Statistical Correlation Technique
Volume-4 | Issue-3
Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model
Volume-3 | Issue-4
Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Volume-3 | Issue-3
Performance Analysis of Multiple Pico Hydro Power Generation
Volume-2 | Issue-2
Energy Efficient Data Mining Approach for Estimating the Diabetes
Volume-3 | Issue-2
Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
Volume-3 | Issue-2
A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Volume - 6 | Issue - 1 | march 2024
Published
07 March, 2024
This study presents a survey of approaches for proactive fault detection in rotating machinery, with a focus on the early identification of bearing faults to enhance equipment reliability and operational efficiency. Traditional methods, relying on physical sensors and manual inspections, often lack the ability to provide timely insights into emerging faults. In contrast, the surveyed approaches integrate non-contact vibration sensors with advanced machine learning techniques, revolutionizing fault detection capabilities. The study presents a brief overview of the methods used in classification of the rotating machinery defects using the machine learning and recommends a combination of machine learning methods at different stages to overcome the challenges of the traditional methods. The collected vibration signals undergo noise reduction via the Hilbert transform, followed by dimensionality reduction and feature selection using Independent Component Analysis (ICA) and Genetic Algorithms (GA), respectively. The selected features are then employed for fault detection and categorization using Random Forest (RF) and Deep Belief Networks (DBN). The Future work will involve the implementation and evaluation of these approaches in real-world industrial settings to validate their effectiveness and reliability.
KeywordsIndependent Component Analysis (ICA) feature selection Genetic Algorithms (GA) bearing issue detection Random Forest (RF) Deep Belief Networks (DBN) proactive monitoring equipment maintenance
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