Fault Diagnosis of Machines Using Deep Convolutional Beta-Variational Autoencoder

Abstract

Industries are using fault diagnosis methods to prevent any downtime, which eventually led them to make profits and take necessary steps beforehand to avoid any mishaps. In recent years, deep learning methods have shown extraordinary performance in massive data applications with advancement in computing power. In this article, a novel intelligent fault diagnosis scheme based on deep convolutional variable-beta variational autoencoder (VAE) is proposed to extract discriminative features. A new min–max algorithm for data points reduction and a random sampling technique to get 2-D data has been proposed. The proposed fault diagnosis combines all intermediate steps (from preprocessing to classification) in a single framework, and an end-to-end training has been performed. The proposed training method with variable beta uses VAE as a feature extractor and classifier rather than just being a probabilistic generative model, which further improved the performance of the overall model. The proposed scheme reduces the needs of domain/expertise knowledge on time-series data. The proposed method has also been validated in the presence of noise. The proposed approach is validated through two case studies by utilizing rotating machinery datasets: First, on the case western reserve university vibration dataset (VD), and second, on the air compressor acoustic dataset (AD). Highest accuracies obtained are 99.93% and 99.91% on case western reserve university VD and air compressor AD, respectively, using the proposed scheme. Finally, a comparative study has been presented.