Particle accelerator power system early fault diagnosis based on deep learning and multi-sensor feature fusion

Abstract

Particle accelerators play a crucial role in scientific research and industrial applications, and enhancing their reliability, ensuring stable operation, and reducing downtime caused by faults are essential for achieving research goals. This paper introduces a novel particle accelerator fault diagnosis method based on deep learning and multi-sensor feature fusion. The approach employs one-dimensional convolution to extract signals from multiple sensors and achieves comprehensive feature fusion of multi-sensor data, effectively overcoming the limitations of individual sensors. It combines Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) to enhance spatiotemporal feature extraction. This approach efficiently extracts features from multiple sensors while concurrently reducing data length and training time. The study uses real signals from particle accelerators and aims to achieve early detection of faults by identifying abnormal signals preceding accelerator malfunctions. Comparative analysis with other machine learning models and the use of multiple evaluation metrics validate the effectiveness and generalizability of this method. The research provides a better performance fault diagnosis model with significant implications for reducing downtime, troubleshooting faults, and improving the reliability of particle accelerators.