Optimization of Predictive Maintenance Processes for Mechatronic Systems Using Artificial Intelligence

In the fast-evolving landscape of industrial automation, maintaining the performance and reliability of mechatronic systems has become increasingly critical. Traditional maintenance strategies, such as reactive and timebased preventive approaches, often lead to inefficiencies, unplanned downtime, and excessive operational costs. This research examines how artificial intelligence (AI) optimizes predictive maintenance (PdM) for mechatronic systems, aiming to enhance uptime, extend asset life, and reduce maintenance costs.The study adopts a mixed-methods approach, combining a detailed literature review with case study analysis and model development. By examining real-world implementations from industries such as manufacturing and rail transport, the research identifies key enablers and obstacles in the transition to AI-driven PdM. A structured framework is proposed, guiding organizations through the steps of data acquisition, model selection, integration, and continuous improvement. Findings suggest that AI, particularly machine learning and deep learning techniques, significantly enhances the accuracy and timeliness of failure predictions when supported by high-quality sensor data. However, challenges such as data scarcity, system integration, model transparency, and ethical concerns remain barriers to widespread adoption. This work contributes a practical, scalable roadmap for organizations aiming to move beyond traditional maintenance paradigms. It emphasizes the value of aligning technological innovation with human expertise to create more resilient, efficient, and intelligent maintenance ecosystems in the era of Industry 4.0.