This track focuses on the latest technological innovations in predictive maintenance, emphasizing machine learning algorithms and their applications. Participants will explore case studies that illustrate the successful integration of these technologies in various engineering sectors.

This session will delve into machine learning methodologies specifically designed for fault detection in engineering systems. Researchers will present novel algorithms and frameworks that enhance the accuracy and efficiency of fault identification.

This track will examine various condition monitoring techniques employed in industrial environments, highlighting their role in predictive maintenance. Discussions will include sensor data analysis and the impact of real-time monitoring on equipment reliability.

Participants will explore predictive analytics methodologies that facilitate effective equipment health management. The focus will be on data-driven strategies that optimize maintenance schedules and improve operational efficiency.

This session will address the challenges and solutions related to anomaly detection within Industrial IoT frameworks. Emphasis will be placed on the integration of sensor data and advanced analytics to identify deviations from normal operational patterns.

This track will compare supervised and unsupervised learning techniques in the context of maintenance optimization. Participants will discuss the advantages and limitations of each approach, supported by empirical research findings.