Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks

TL;DR

This paper introduces a generalized multiscale feature extraction method using generative adversarial networks for accurate remaining useful life prediction of bearings, addressing data distribution issues and improving prediction performance.

Contribution

It proposes a novel GAN-based multiscale feature extraction approach that automates feature learning and enhances RUL prediction accuracy for bearings.

Findings

Outperforms conventional deep learning methods in RUL prediction

Effectively captures sequence features from vibration signals

Demonstrates robustness across different datasets

Abstract

Bearing is a key component in industrial machinery and its failure may lead to unwanted downtime and economic loss. Hence, it is necessary to predict the remaining useful life (RUL) of bearings. Conventional data-driven approaches of RUL prediction require expert domain knowledge for manual feature extraction and may suffer from data distribution discrepancy between training and test data. In this study, we propose a novel generalized multiscale feature extraction method with generative adversarial networks. The adversarial training learns the distribution of training data from different bearings and is introduced for health stage division and RUL prediction. To capture the sequence feature from a one-dimensional vibration signal, we adapt a U-Net architecture that reconstructs features to process them with multiscale layers in the generator of the adversarial network. To validate the proposed method, comprehensive experiments on two rotating machinery datasets have been conducted to predict the RUL. The experimental results show that the proposed feature extraction method can effectively predict the RUL and outperforms the conventional RUL prediction approaches based on deep neural networks. The implementation code is available at https://github.com/opensuh/GMFE.