  Most of the data-driven approaches applied to bearing fault diagnosis up to
date are established in the supervised learning paradigm, which usually
requires a large set of labeled data collected a priori. In practical
applications, however, obtaining accurate labels based on real-time bearing
conditions can be far more challenging than simply collecting a huge amount of
unlabeled data using various sensors. In this paper, we thus propose a
semi-supervised learning approach for bearing anomaly detection using
variational autoencoder (VAE) based deep generative models, which allows for
effective utilization of dataset when only a small subset of data have labels.
Finally, a series of experiments is performed using both the Case Western
Reserve University (CWRU) bearing dataset and the University of Cincinnati's
Center for Intelligent Maintenance Systems (IMS) dataset. The experimental
results demonstrate that the proposed semi-supervised learning scheme greatly
outperforms two mainstream semi-supervised learning approaches and a baseline
supervised convolutional neural network approach, with the overall accuracy
improvement ranging between 3% to 30% using different proportions of labeled
samples.
