Development of AI-Driven Predictive Maintenance System for Hydraulic Machinery

Thesis Abstract

Hydraulic machinery forms the backbone of numerous industrial processes, yet their operational efficiency and longevity are often compromised by unexpected failures and maintenance inefficiencies. Traditional maintenance approaches, primarily reactive or scheduled preventive strategies, pose significant limitations by leading to unplanned downtimes, increased operational costs, and suboptimal resource utilization. This study aims to develop an advanced artificial intelligence (AI)-driven predictive maintenance system tailored for hydraulic machinery, with the objective of enhancing fault detection accuracy, reducing maintenance costs, and improving machinery uptime. Specific objectives include analyzing existing maintenance protocols, designing a machine learning-based predictive model, and evaluating its performance in real-world operational environments. The research adopts a mixed-methods approach, integrating quantitative and qualitative data collection and analysis techniques. The quantitative phase involves gathering data from a sample size of 150 hydraulic units across manufacturing and construction industries over a twelve-month period. Data collection instruments encompass sensor-based data acquisition systems capturing parameters such as pressure, temperature, vibration, and flow rate, complemented by maintenance records and operational logs. Qualitative data are obtained through semi-structured interviews with maintenance engineers to extract insights into current maintenance challenges and perceptions of AI integration. The validity and reliability of sensor data are ensured through calibration and repeated measurements, while interview data are analyzed using thematic analysis to identify recurrent themes and insights. Data analysis employs feature extraction and preprocessing techniques, followed by the application of supervised machine learning algorithms including Random Forest, Support Vector Machine (SVM), and Neural Networks to develop predictive models of machinery health status. Model performance is evaluated through cross-validation, with metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves. The study further explores the integration of the developed models into a real-time monitoring dashboard, tested under operational conditions for validation. Analytical frameworks are grounded in the Theory of Maintenance Optimization and the Predictive Maintenance Model, emphasizing data-driven decision-making. Expected findings indicate that AI-based models outperform traditional threshold-based fault detection methods, with Neural Network models achieving an overall predictive accuracy of 92%, and significant reductions in false positives and missed failures. The predictive maintenance system is anticipated to facilitate early fault detection, optimize maintenance scheduling, and extend machinery lifespan. Insights from maintenance personnel suggest a high acceptance of the AI-driven approach, contingent on usability and integration into existing workflows. This research contributes to the body of knowledge by advancing the understanding of AI applications in hydraulic machinery maintenance, demonstrating the feasibility and effectiveness of machine learning models in predictive diagnostics, and proposing an integrated framework for real-time monitoring and decision support. It offers practical pathways for industrial stakeholders seeking to optimize maintenance strategies through technological innovation. The study concludes that adopting AI-driven predictive maintenance systems can substantially reduce operational costs, improve reliability, and promote sustainable resource utilization in hydraulic machinery management. Based on these findings, recommendations include further refinement of models through continuous data collection, integration with Internet of Things (IoT) platforms for enhanced connectivity, and initiatives for staff training to foster acceptance and effective utilization of AI tools. Future research directions are suggested to explore the scalability of the system across different machinery types and industrial sectors, as well as the potential for incorporating deeper learning algorithms and advanced sensor technologies to further improve predictive accuracy and system robustness.