Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

By Jinde Zheng, Haiyang Pan, Junsheng Cheng. 2017

355 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/da288b947b99107f69944d16866451844352ea32

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. Jun Yan
2. Witold Glowacz
3. Isaac Triguero
4. Ping Xie
5. Guoqian Jiang
6. Haibo He
7. Enrique Onieva
8. Mikel Canizo

In Mechanical Engineering

1. Witold Glowacz
2. Fengshou Gu
3. A. Głowacz

Citations in the corpus

• Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing (2023, background): “As for bearings, their operating conditions, application scenarios, or environment noises are usually variable from one application to another [20].”
• Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, background): “istics [20] and contain complex patterns at multiple time scales, which has been discussed in literature [20]–[22].”
• Vibration-Based Fault Diagnosis of Commutator Motor (2018, methodology): “+e CMFE (composite multiscale fuzzy entropy) and ESVM (ensemble support vector machines) were used for detection of rolling bearing faults [9].”
• Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, methodology): “As a consequence, there has been a proliferation of machine learning techniques for time series anomaly detection [6, 13, 14, 15].”