A Study on Deep Learning Algorithms for Bearing Fault Diagnostics

An Evaluation of Deep Learning Approaches for Bearing Fault Diagnostics

Abstract

In this article, our literature on carrying fault diagnosis with profound expertise algorithms systematically discusses current ones. DL algorithms have displayed a revived interest, for the industry and for the academy of intelligent machinery fitness, while traditional machineries, like the artificial neural network, principal component research, vector assistance, etc. have successively contributed to carrying defects identification and categorization for decades. We would first include a short overview of traditional ML approaches, and then delve into new DL algorithms for fault applications. In this post, we address the typical DL approaches. Specifically, the dominance of the DL-based approaches was evaluated in terms of the extracting function loss and classification results.