Fault Detection and Classification Using Artificial Neural Networks

Prof.Suraj S.Shinde; Mr. Vinayak N. Jadhav; Mr.Akshay N. Sanap

Fault Detection and Classification Using Artificial Neural Networks

Authors: Prof.Suraj S.Shinde, Mr. Vinayak N. Jadhav, Mr.Akshay N. Sanap

Unique Paper ID: 178768
Volume: 11
Issue: 12
PageNo: 6473-6476

Keywords: Fault Detection Fault Classification Artificial Neural Networks (ANNs) Predictive Maintenance Machine Learning Signal Processing

Abstract:
Fault detection and classification (FDC) is a critical aspect of maintaining system reliability and safety in industrial applications, power systems, and mechanical structures. Traditional fault diagnosis methods often rely on manual inspection or statistical techniques, which may lack accuracy and efficiency in complex systems. Artificial Neural Networks (ANNs) have emerged as a powerful tool for automated fault detection due to their ability to learn complex patterns from data. This project Fault detection and classification using artificial neural networksexplores the application of ANNs for fault detection and classification in various systems. The proposed approach involves pre-processing sensor data, extracting relevant features, and training a neural network model to identify and categorize faults. Different ANN architectures, such as Multilayer Perceptron (MLP), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), are evaluated for their performance in fault diagnosis. The study demonstrates the effectiveness of ANNs in improving fault detection accuracy, reducing false alarms, and enabling real-time monitoring. Experimental results on benchmark datasets (such as motor vibration signals, power system faults, or bearing defects) validate the model's robustness. The findings suggest that ANN-based fault detection systems can significantly enhance predictive maintenance, minimize downtime, and optimize operational efficiency

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 6473-6476

Fault Detection and Classification Using Artificial Neural Networks

Available:https://ijirt.org/Article?manuscript=178768

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