A review on data-driven fault severity assessment in rolling bearings

By M. Cerrada, Réne-Vinicio Sánchez, Chuan Li, Fannia Pacheco, Diego Cabrera, José Valente de Oliveira, Rafael E. Vásquez. 2018

645 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/dc91a7a555eee6f9bdd838949f7e27976f60d52a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

In Mechanical Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

In Mechanics of Materials

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

Citations in the corpus

• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “its simplicity and efficiency, the mean square error (MSE) has been widely applied to design the deep autoencoder loss function [23].”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, neutral): “tion [23].”