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

TL;DR

This paper introduces a semi-supervised deep learning method based on variational autoencoders for bearing anomaly detection, effectively leveraging limited labeled data to improve detection accuracy in practical scenarios.

Contribution

It proposes a novel semi-supervised learning framework using VAEs for bearing fault diagnosis, outperforming existing semi-supervised and supervised methods.

Findings

Achieves 3% to 30% accuracy improvement over baseline methods.

Effectively utilizes small labeled datasets for fault detection.

Demonstrates robustness across multiple bearing datasets.

Abstract

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.