Integration of Machine Learning Algorithms in Mechatronic Systems for Predictive Maintenance

Abstract

The integration of machine learning algorithms in mechatronic systems has emerged as a promising approach for achieving efficient and reliable predictive maintenance strategies. This abstract provides an overview of the application of machine learning techniques in mechatronic systems for predictive maintenance, highlighting the benefits, challenges, and future directions in this field. Predictive maintenance plays a crucial role in ensuring the optimal performance and longevity of mechatronic systems, such as industrial machinery, automotive systems, and robotics. Traditional maintenance approaches rely on predetermined maintenance schedules or reactive maintenance, which can result in unnecessary downtime, high maintenance costs, and unexpected failures. To address these limitations, the integration of machine learning algorithms has gained significant attention in recent years. Machine learning algorithms offer the ability to analyse large volumes of data collected from various sensors embedded in mechatronic systems. These algorithms can identify patterns, anomalies, and trends within the data, enabling predictive maintenance decisions. By utilizing historical data, machine learning algorithms can learn the normal behaviour of the system and predict potential failures or maintenance requirements in advance.