AI-Based Predictive Maintenance Systems for Smart Manufacturing: A Review and Future Outlook

Abstract

Predictive maintenance (PdM) is one of the enabling technologies of smart manufacturing in which artificial intelligence (AI) is used to predict equipment troubles, or even better anticipate the failure of equipment, based on sensor data, machine learning algorithms, and digital twins. The traditional techniques of maintenance such as the reactive maintenance technique and the preventive maintenance technique will most probably introduce undue downtimes, wastages or redundancy. In comparison, AI-oriented predictive maintenance is a combination of real-time data analytics, industrial internet (IIoT), and existing deep learning technology will allow equipment to operate 24/7, minimize the risk of operations, and make decisions. It is a review article on the use of AI-based predictive maintenance in smart manufacturing. It talks about the establishment of PdM, how AI can develop real-time faults and use machine learning, deep learning and deep reinforcement learning procedures. Providing the advantages of PdM systems, the list below states with comment of automatic saving in case of downtime, of low cost and sustainability of aerospace case, case of automotive and case of energy. The issue of the heterogeneity of the data, non-standardization, threat to cybersecurity, and explainable AI is solved. Predictive maintenance and its combination with Industry 4.0, such as digital twins, edge computing, and blockchain, is another item on the list of the ways to make manufacturing systems more autonomous and resilient. The paper ends with a definition of future directions that include hybrid AI model, federated learning of collaborative PdM, and explainable AI model of trust and adoption. Smart manufacturing ecosystems can enable all this by adding intelligence to maintenance to move to more sustainable, reliable, and adaptable operations as defined in Industry 5.0.