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Dr. Mohammed Azam is a Lead Engineer in the Department of Engineering at Hill International, King Abdullah Economic City, Saudi Arabia. He possesses extensive industrial experience in engineering, industrial automation, predictive maintenance, Artificial Intelligence, and smart manufacturing technologies. His research interests include Industry 4.0, IoT-enabled predictive maintenance, cloud computing, and intelligent industrial systems. He actively contributes to industry-focused innovation and applied engineering research.
The advent of Industry 4.0 has revolutionised industrial processes via the incorporation of Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data Analytics, and smart automation. Contemporary manufacturing sectors incessantly produce substantial quantities of operational data from interconnected machinery, industrial sensors, programmable logic controllers (PLCs), robotics, and cyber-physical systems. Conventional maintenance approaches, including reactive and preventive maintenance, can lead to unforeseen equipment malfunctions, extended downtime, elevated maintenance expenses, and diminished operational efficacy. Predictive Maintenance (PdM) employs AI-powered analytics, IoT-integrated sensors, and cloud computing to assess equipment condition, forecast possible malfunctions, calculate the remaining usable life (RUL) of machines, and enhance maintenance planning. Machine Learning (ML), Deep Learning (DL), Digital Twins, Edge AI, and Explainable Artificial Intelligence (XAI) significantly improve maintenance decision-making via intelligent diagnostics and real-time predictive analytics. This chapter delineates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues pertaining to predictive maintenance through AI, IoT, and cloud computing.