Machine Learning Approaches in Sustainable Maintenance Practices

The cost of maintenance activities generally accounts for the second largest portion of the operational budget after energy costs in an industrial ecosystem. The significant reason behind the continuing rise of maintenance costs reflects on the application of highly automated and technologically complex machineries involved in the manufacturing process. As such, the implementation of sustainable maintenance practices, in the realm of Industry 5.0 and the integration of Machine Learning (ML)/Artificial intelligence (AI) methodologies, is essential more than ever for enterprises to predict and avoid system failures and to subsequently save cost and sustain in the business. In this work, we investigated the ML/AI approaches and the importance of holistic human-machine integration in industrial facilities. Additionally, we will create a framework for human-centric methods of integrating automation into sustainable maintenance practices where human intervention remains crucial in interpreting complex data, making judgment calls in uncertain scenarios, and applying context-specific knowledge that machines alone cannot provide. By placing humans at the center of our framework, businesses can ensure a more holistic approach to maintenance that accounts for both technological advancements and the irreplaceable value of human ingenuity while still taking full advantage of the remarkable potential of ML methodologies and AI-driven technologies as a viable process for achieving reliability and productivity. In the next step, we intend to offer such a designed framework to local manufacturing facilities in Georgia, and to analyze the responses from a pilot test conducted in that environment.