Fault Identification and Diagnosis in Air Compressor Systems using RBF Neural Networks

Abstract

Fault identification and diagnosis in air compressor systems are critical for maintaining operational efficiency, reducing downtime, and minimizing maintenance costs. Traditional diagnostic methods often struggle with the complexity and variability of faults in these systems. This research paper explores the use of Radial Basis Function (RBF) neural networks for the identification and diagnosis of faults in air compressor systems. RBF neural networks, known for their powerful pattern recognition capabilities, are employed to enhance the accuracy and reliability of fault detection. The study details the structure and training of RBF neural networks, the methodology for fault diagnosis, and presents experimental results demonstrating the effectiveness of this approach. The findings show that RBF neural networks can significantly improve fault diagnosis accuracy, providing a robust and efficient framework for real-time fault detection in air compressor systems. This research contributes to the development of more reliable and efficient maintenance strategies for industrial applications.