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Abstract:

Integrating ecological factors into predictive maintenance practices represents a transformative shift in manufacturing, emphasising sustainability alongside operational efficiency. This paper presents a theoretical model of ecological predictive maintenance that highlights the dynamic management of production systems, focusing on minimising environmental impacts while enhancing productivity. Using the enabling technologies of Industry 4.0, such as sensors, artificial intelligence and digital twins, manufacturers can consistently monitor and improve production processes in line with the principles of a circular economy. The proposed framework confronts the challenges associated with data management and the requisite for proficient personnel, advocating for extensive training programs to equip the workforce with essential competencies. Additionally, the interaction between taxonomy and ontology within predictive maintenance systems is examined, highlighting the significance of standardised practices to enhance interoperability and operational efficiency. Ultimately, ecological predictive maintenance offers significant potential for mitigating climate change impacts, enabling manufacturers to foster a sustainable future while enhancing their corporate reputation and meeting consumer preferences for environmentally responsible practices. This study calls for ongoing research to refine these systems and promote collaboration across sectors, ensuring the full realisation of ecological predictive maintenance's benefits in the manufacturing landscape. The proposed solutions are innovative and have yet to be published or presented to a broader audience.

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Towards Sustainable Manufacturing: Implementing Ecological Predictive Maintenance for Enhanced Efficiency and Environmental Stewardship