# Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders

arXiv: https://arxiv.org/abs/1912.01096 · HTML: https://mlsanity.com/paper/1912.01096/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Learning for Time Series: Contrastive or Generative?](https://mlsanity.com/paper/2403.09809/) (2024)
- [Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features](https://mlsanity.com/paper/2104.10519/) (2021)
- [AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use](https://mlsanity.com/paper/2509.15153/) (2025)
- [Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry](https://mlsanity.com/paper/2112.07356/) (2021)
- [Deep evolving semi-supervised anomaly detection](https://mlsanity.com/paper/2412.00860/) (2024)

## Abstract (arXiv)

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.