Feature Extraction Based on Hierarchical Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis
Author:
Affiliation:
1. College of Electrical Engineering and Automation, Anhui University, Hefei, China
2. China North Vehicle Research Institute, Beijing, China
Funder
Key Basic Research Project
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10132562.pdf?arnumber=10132562
Reference45 articles.
1. A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods
2. Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis
3. Bearing fault diagnosis using transfer learning and optimized deep belief network
4. Application of Min-Max Normalization on Subject-Invariant EMG Pattern Recognition
5. Fault Diagnosis of Train Plug Door Based on a Hybrid Criterion for IMFs Selection and Fractional Wavelet Package Energy Entropy
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1. A Nonlinear AutoRegressive-Based Noise Cancellation Method for Real-Time Fault Diagnosis of Rolling Bearings;IEEE Transactions on Instrumentation and Measurement;2024
2. Unsupervised rolling bearing fault diagnosis method across working conditions based on multiscale convolutional neural network;Measurement Science and Technology;2023-12-20
3. A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA;Sensors;2023-10-21
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