A Framework for Predictive Maintenance Optimization in Mechanical Systems

Thesis Abstract

Mechanical systems are integral to industrial operations, yet they are frequently susceptible to unforeseen failures that result in significant operational downtime, increased maintenance costs, and safety risks. Traditional reactive and preventive maintenance approaches often lead to suboptimal asset performance, emphasizing the necessity for a proactive maintenance strategy rooted in predictive analytics. This study aims to develop an integrated framework for optimizing predictive maintenance in mechanical systems, thereby enhancing reliability, reducing operational costs, and extending equipment lifespan. The specific objectives are to investigate the current maintenance practices, identify key failure indicators using condition monitoring data, formulate predictive models employing machine learning techniques, and design decision-making protocols for maintenance scheduling. The research adopts a mixed-methods approach, combining qualitative and quantitative methodologies. It begins with a comprehensive qualitative analysis through semi-structured interviews with maintenance engineers and plant managers from manufacturing firms, supplemented by a review of existing maintenance protocols, to understand contextual factors influencing maintenance decisions. Concurrently, a quantitative, observational study is conducted on a sample of 50 critical mechanical assets operating within a large manufacturing plant over a 12-month period. Data collection involves condition monitoring sensors such as vibration, temperature, and acoustic sensors, integrated with maintenance log records. The reliability and validity of data collection instruments are ensured through calibration protocols and repeated measurements. Quantitative data are analyzed using statistical techniques such as regression analysis and multivariate analysis of variance (MANOVA) to identify significant failure predictors and performance trends. Predictive modeling is performed utilizing advanced machine learning algorithms, including Random Forests, Support Vector Machines, and Artificial Neural Networks, to develop accurate failure prediction models. These models are validated through cross-validation techniques, and their predictive performance is assessed via metrics such as accuracy, precision, recall, and the F1 score. An analytical framework based on the Theory of Constraints and the Reliability-Centered Maintenance (RCM) theory guides the formulation of decision rules for maintenance scheduling, prioritization, and resource allocation, aiming to minimize unplanned downtime and optimize maintenance costs. Expected findings include the identification of critical failure indicators detectable via non-invasive condition monitoring, the development of robust predictive models with high accuracy in failure forecasting, and a structured decision-making protocol that effectively integrates model outputs with operational constraints. It is anticipated that the framework will demonstrate a significant reduction in unexpected breakdowns, maintenance costs, and equipment downtime. Additionally, the study will reveal key factors influencing maintenance decision-making processes and highlight best practices for integrating predictive analytics into existing maintenance regimes. This research contributes to knowledge by providing an empirically validated, theoretically grounded framework for predictive maintenance optimization, operationalizable across various mechanical systems within manufacturing contexts. It advances the application of machine learning in maintenance planning and offers a systematic approach for integrating condition monitoring, failure prediction, and decision support systems into routine operations. The main conclusion emphasizes the transformative potential of predictive analytics-driven maintenance frameworks in increasing operational efficiency and safety. Recommendations include the adoption of the proposed framework within manufacturing organizations, investment in sensor technologies, and employee training on predictive maintenance practices. It also advocates for further research to extend the framework to other industrial sectors, incorporate emerging IoT solutions, and enhance real-time decision-making capabilities to adapt to dynamic operational environments.