Learning the correlation of beam position monitors for Pohang synchrotron light source using neural networks

Abstract

Neural networks are used to learn the correlation of the beam position monitors (BPM) which trace the electron beam orbit in the storage ring of the Pohang Synchrotron Light Source. Since a beam in the storage ring passes through many BPMs, there is a correlation among the measurements of those monitors. A perceptron is trained to predict one BPM's measurement given other BPMs' measurements. If the predicted value of a perceptron is different from the actual measurement, the corresponding BPM can be considered to have a fault. Test results indicate that the neural network approach has a potential for actual fault diagnosis of BPM. Compared to the current diagnosis methods, the neural network approach is more economical and less disruptive. If is shown to perform better than a numerical approach.