Development of Predictive Maintenance Technologies for Critical Industrial Systems Using AI and IoT

Abstract

Raymond Kamgba

This paper explores the development and integration of advanced predictive maintenance technologies utilizing Artificial In- telligence (AI) and the Internet of Things (IoT) within critical industrial systems. The objective is to enhance reliability and efficiency by mitigating unplanned downtimes through real-time monitoring and predictive analytics. Through a comprehensive methodology encompassing data collection, algorithm development, system integration, field testing, and training, this study demonstrates the efficacy of AI and IoT in preempting equipment failures. Results indicate significant improvements in industri- al reliability, efficiency, and safety, with reduced maintenance costs and increased equipment uptime. By leveraging real-time data analytics and predictive algorithms, industries can transition from reactive to proactive maintenance strategies, thereby optimizing operational performance and contributing to industrial sustainability.

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