AI-Driven Predictive Maintenance System for Smart Manufacturing Environments

Thesis Abstract

The increasing complexity and operational demands of modern manufacturing environments necessitate innovative solutions to minimize equipment downtime and optimize maintenance practices. Traditional maintenance strategies, such as reactive and preventive approaches, often lead to unplanned outages and resource wastage, thus prompting the need for more intelligent, data-driven maintenance systems. This study aims to develop and validate an artificial intelligence (AI)-driven predictive maintenance framework tailored for smart manufacturing environments, with the primary objective of enhancing equipment reliability and reducing maintenance costs through early fault detection and proactive intervention. The research adopts a mixed-methods approach, combining quantitative data analysis with qualitative insights. The quantitative component involves a cross-sectional survey and experimental validation within a manufacturing plant specializing in automotive component assembly, with a population of 150 machines monitored over twelve months. A stratified random sampling technique was employed to select a representative sample of 60 machines, ensuring diverse equipment types and operational conditions. Data collection instruments include sensor-based diagnostic logs, machine operational parameters, and maintenance records, complemented by semi-structured interviews with maintenance personnel and engineers to capture contextual information. The validity of data collection instruments was established through pilot testing and expert validation, while reliability was measured using Cronbach's alpha coefficients exceeding 0.85. Analytical procedures comprise multiple techniques machine learning algorithms, such as Random Forest and Support Vector Machines, were trained on historical sensor data to develop fault prediction models; regression analysis was employed to evaluate the relationship between sensor features and maintenance outcomes; and receiver operating characteristic (ROC) analysis assessed the models’ predictive accuracy. The framework also incorporated a theoretical foundation grounded in the Theory of Maintenance Optimization and the Technology Acceptance Model (TAM), providing conceptual insights into the factors influencing the adoption and effectiveness of AI-based predictive systems. Key expected findings include high predictive accuracy of machine learning models with an area under the ROC curve exceeding 0.85, indicating reliable early fault detection capabilities. The study anticipates that integrating AI algorithms with real-time sensor data significantly improves maintenance decision-making, evidenced by an estimated 25% reduction in unplanned outages and a 15% decrease in maintenance costs compared to traditional regimes. Furthermore, qualitative analysis is expected to reveal organizational and cultural factors affecting technology adoption, such as worker trust and managerial support. This research contributes to the existing body of knowledge by offering a comprehensive, empirically validated framework for AI-driven predictive maintenance tailored to smart manufacturing contexts. It bridges the gap between theoretical models and practical implementation, highlighting the pivotal role of technological, organizational, and human factors in optimizing maintenance strategies through AI. The study's integrated approach advances understanding of how machine learning techniques can be effectively deployed to mitigate equipment failures in complex production environments. The main conclusion affirms that AI-empowered predictive maintenance systems substantially enhance operational efficiency and equipment longevity. Based on these findings, the study recommends that manufacturing firms integrate AI-driven fault prediction models into their maintenance workflows, invest in sensor and data infrastructure, and foster organizational cultures supportive of technological innovation. Future research should explore longitudinal impact assessments and the scalability of predictive maintenance systems across different manufacturing sectors, as well as the integration of emerging AI techniques, such as deep learning, to further refine predictive accuracy and robustness.