Predictive maintenance has been a key component of the aerospace sector in the United Kingdom, helping to guarantee the dependability and security of aircraft. Predictive maintenance systems have reportedly cut maintenance costs by 15% and unscheduled downtime by 20% in major airlines, according to a Johnson (2017) study. The findings of this study are divided into conceptual and contextual research gap categories. This paper further describes the machine learning approach for predictive maintenance in manufacturing companies, furthermore, highlights on the potential opportunities have been suggested at the end of this paper.

Cyber-physical systems (CPS) are thought to be among industry 4.0’s primary enablers. CPS technology bridges the gap between the physical and cyber worlds by integrating knowledge from several fields. An important use of Industry 4.0 is predictive maintenance (PdM), which can use a CPS-based strategy in intelligent operations to reduce machine downtime and related expenses. This paper discusses the application of machine learning to intelligent maintenance of Cyber Physical systems. As CPS become more complex and widespread across industries, maintaining their reliability and performance is critical. This paper further describes how machine learning algorithm can be used to predict system failure, develop repair plans and further highlights the potential significant improvements in CPS maintenance strategies.

This paper explores the development and integration of advanced predictive maintenance technologies utilizing Artificial Intelligence (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 industrial 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.