Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis

By Jing Tian, Carlos Morillo, Michael H. Azarian, Michael G. Pecht. IEEE transactions on industrial electronics (1982. Print), 2016

458 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/17083315066569bdb7539cdab069896fd1071416

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. Yihao Xue
2. Mengjie Huang
3. Rui Yang
4. Zidong Wang
5. Hee-Jun Kang
6. Roberto Ferrero
7. Duy-Tang Hoang
8. Jing Lin

In Mechanical Engineering

1. Hee-Jun Kang
2. Duy-Tang Hoang
3. Jing Lin
4. Kaixuan Liang
5. Jinyang Jiao
6. Ming Zhao

Citations in the corpus

• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “machine [32], k-nearest neighbor [33], artificial neural net-”
• A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion (2020, methodology): “The fault diagnosis for internal bearings has been extensively studied by researchers with both approaches: VS-based [7], [8] and motor CS-based approaches [9], [10].”