AI-Driven Predictive Maintenance for Sustainable Industrial Equipment Management

Thesis Abstract

The escalating operational costs and environmental impacts associated with industrial equipment maintenance necessitate innovative strategies to enhance sustainability and efficiency within manufacturing systems. Traditional preventive maintenance approaches, characterized by fixed schedules and reactive repairs, often result in unnecessary downtime and resource wastage, underscoring the need for a more predictive and data-driven methodology. This study aims to develop and validate an AI-driven predictive maintenance framework that optimizes equipment reliability, reduces operational costs, and minimizes environmental footprint, thereby contributing to sustainable industrial management practices. The specific objectives include (1) to analyze existing maintenance paradigms within industrial settings and identify their limitations; (2) to design a predictive maintenance model leveraging machine learning algorithms for fault detection and failure prognosis; (3) to evaluate the effectiveness of the model in real-world industrial environments; and (4) to integrate sustainability metrics into maintenance decision-making processes for enhanced environmental performance. Guided by the Diffusion of Innovations theory and the Theory of Planned Behavior, the study examines how organizational and technological factors influence the adoption of AI-enabled predictive maintenance strategies. Employing a mixed-methods research design, the qualitative component involves thematic analysis of interviews with 30 maintenance engineers and plant managers across manufacturing industries to explore perceptions, barriers, and facilitators related to AI implementation. The quantitative component encompasses a longitudinal experimental study involving 15 industrial facilities, with a total sample size of 150 distinct pieces of critical equipment monitored over 12 months. Data collection instruments include IoT sensors embedded in equipment, maintenance logs, and structured questionnaires administered to operational staff. The IoT sensor data is analyzed using time-series analysis and supervised machine learning algorithms such as Random Forest and Support Vector Machines to develop fault prediction and remaining useful life models. The validity and reliability of the predictive models are assessed through cross-validation techniques and confusion matrix performance metrics. The effectiveness of the predictive maintenance model is evaluated through statistical comparison of key performance indicators before and after implementation, including Mean Time Between Failures (MTBF), maintenance costs, downtime duration, and energy consumption. Factor analysis and regression analysis are employed to identify significant predictors of equipment failure and environmental impact. The results are expected to demonstrate that AI-based predictive maintenance significantly improves equipment reliability, reduces maintenance expenses by at least 20%, and decreases energy consumption and waste generation, advancing sustainability objectives within industrial operations. This research contributes novel insights into the integration of AI technologies with sustainability metrics in maintenance management, filling a notable gap in empirical research on eco-efficient industrial practices. It also provides a validated framework for implementing AI-driven predictive maintenance in diverse industrial contexts, emphasizing the alignment of technological innovation with environmental sustainability. The study concludes by recommending the adoption of integrated AI maintenance systems, emphasizing employee training to foster organizational change, and advocating for policy incentives to support sustainable industry transformation. The findings underscore the potential for AI-driven predictive maintenance to revolutionize industrial equipment management, promoting long-term operational resilience and environmental stewardship. Future research avenues include exploring the scalability of the models across different sectors, integrating advanced sensor technologies, and developing decision-support systems that account for broader sustainability indicators. This study thereby advances both theoretical knowledge and practical application, offering a comprehensive approach to sustainable industrial management through intelligent maintenance solutions.