Deep Learning for Real-Time Monitoring of Industrial Systems

Abstract

Real-time monitoring of industrial systems is critical for ensuring operational efficiency, minimizing downtime, and maintaining safety. Deep learning techniques have proven to be highly effective in analyzing complex industrial data, enabling predictive maintenance, fault detection, and process optimization. This article explores the application of deep learning models in real-time monitoring of industrial systems, highlighting their potential to improve productivity and reduce operational risks. It also addresses the challenges, benefits, and future directions for implementing deep learning in industrial environments.