Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals

By Jaouher Ben Ali, N. Fnaiech, L. Saidi, Brigitte Chebel-Morello, Farhat Fnaiech. 2015

731 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b095bcbaeed48e6e16ac2fd2185df5498b9425dd

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. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. Réne-Vinicio Sánchez
8. Dong Yang

In Mechanical Engineering

1. Naipeng Li
2. Yaguo Lei
3. Réne-Vinicio Sánchez
4. Jing Lin
5. Dong Yang
6. Weiting Zhang
7. Tao Yan
8. Liang Guo

In Mechanics of Materials

1. Yaguo Lei
2. Naipeng Li
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. Réne-Vinicio Sánchez
8. Zhiqiang Chen

Citations in the corpus

• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “An ANN-based model was adopted in [95] to perform condition”
• A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, methodology): “Jaouher et al. (2015) proposed an automatic bearing fault diagnosis method based on empirical mode decomposition and artificial neural network.”
• Gearbox Fault Identification and Classification with Convolutional Neural Networks (2015, methodology): “The artificial neural network combining with empirical mode decomposition was applied for automatic bearing fault diagnosis based on vibration signals [21].”
• Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network (2015, neutral): “In the literature, maintenance strategies are classified into three types [3]:”