Research on Rolling Bearing Fault Diagnosis Method Based on Harmonic Noise Kurtosis-Time Characteristic Blind Deconvolution
Author:
Affiliation:
1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
2. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China
Funder
Nature Science Foundation of Beijing, China,
Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/19/10367905/10663514.pdf?arnumber=10663514
Reference49 articles.
1. Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN
2. Density-Based Affinity Propagation Tensor Clustering for Intelligent Fault Diagnosis of Train Bogie Bearing
3. Remaining Useful Life Prognosis Method of Rolling Bearings Considering Degradation Distribution Shift
4. Fast nonlinear convolutional sparse filtering: A novel early-stage fault diagnosis method of rolling bearing
5. DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows
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