Optimizing Industrial Machinery Maintenance through Machine Learning Predictions

In industrial area, the timely detection and prevention of machinery defects plays a vital role for ensuring operational efficiency and reducing downtime. Traditional methods of defect detection often rely on manual inspection, which can be time-consuming and prone to human error. In recent years, the advent of artificial intelligence, particularly Convolutional Neural Networks (CNNs) has revolutionized defect detection processes by enabling automated and accurate identification of anomalies in machinery components.