Early Failure Detection in Secondary Cryogenic Pumps Through Machine Learning Techniques in the Context of Industry 4.0

This chapter delves into the critical role of secondary cryogenic pumps in the LNG regasification process and the challenges associated with their operation. It emphasizes the need for early failure detection to ensure safety and efficiency. The study focuses on developing a machine learning tool to identify anomalous pump operations before incidents occur, using historical data and advanced techniques like Random Forest and Deep Learning. The methodology involves data preprocessing, model selection, and validation, with a detailed comparison of different machine learning algorithms. The chapter also presents case studies of pumps in various operational scenarios, highlighting the differences in behavior before and after failures. It concludes with recommendations for operation and maintenance based on the analysis, demonstrating the potential of machine learning in predictive maintenance and the importance of continuous monitoring and data analysis.