Semi-Supervised Machine Learning for Motor Eccentricity Fault Diagnosis


Eccentricity is one major indicator of mechanical faults in electric machines and needs to be detected early to avoid machine failures. Data-driven techniques based on machine learning and deep learning algorithms have been proposed in recent years for motor fault detection. However, the majority of these methods use supervised learning algorithms and require large, labelled datasets, which can be challenging to obtain. In this paper, we propose a semi-supervised learning method based on a deep generative model using a variational auto-encoder for eccentricity fault quantification. Good pre- diction accuracy can be achieved when only a small subset of training data has labels.