Towards data-driven predictive maintenance for vacuum ion pumps at European XFEL.

Large-scale scientific facilities, such as the European XFEL, are highly complex, comprising multiple subsystems that must operate in coordination to produce high-quality scientific output. Any fault within such subsystems can cause unexpected interruptions, with a significant impact on the scientific output. Therefore, it is fundamental to detect abnormal behavior in components well in advance, allowing for timely interventions and efficient maintenance planning. Vacuum ion pumps are an integral part of these facilities, and their smooth operation is essential to maintain overall performance. However, monitoring a large number of pumps is challenging and requires significant human efforts. In this paper, we propose the application of machine-learning techniques to develop an early fault detection methodology for vacuum ion pumps. We conducted several studies to investigate the utilization of the Support Vector Machine and Convolutional Neural Networks to classify pressure data obtained from multiple vacuum ion pumps installed at the European XFEL. In addition, we tackle a common issue while collecting the training data from reliable subsystems, that is, the rarity of fault-related examples. This can hinder the model's ability to generalize effectively, potentially leading to poor performance when deployed on unseen data. To address these challenges, we investigate two methods, cost-sensitive learning and the Synthetic Minority Over-sampling Technique (SMOTE), in order to improve classification performance on imbalanced datasets. Our developed system shows promising results and significantly automates the fault identification process with an F1-score of 80%, thus reducing manual efforts.