Multi-mode signal fusion and improved residual dense network fault diagnosis of nuclear power plant
Author:
Affiliation:
1. Beijing Information Science & Technology University,Mechanical Electrical Engineering School,Beijing,China,100192
2. China Nuclear Power Engineering Co., Ltd,Instrumentation and Control Institute,Beijing,China,100089
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295756.pdf?arnumber=10295756
Reference15 articles.
1. Online detection for bearing incipient fault based on deep transfer learning
2. Rolling bearing fault diagnosis based on optimized VMD and MHA-DenseNet[J];dong;Combined Machine Tools and Automated machining Technology,2022
3. Bearing Fault Diagnosis Based on Multiple Transformation Domain Fusion and Improved Residual Dense Networks
4. A novel dense residual network based on Adam-S optimizer for fault diagnosis of bearings under different working conditions
5. A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults
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