Rolling bearing fault diagnosis based on channel attention multi-scale convolution-BiLSTM network
Author:
Affiliation:
1. School of Mechanical Electrical Engineering, Beijing Information Science & Technology University,Beijing,100192
Funder
National Natural Science Foundation (NNSF) of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606566.pdf?arnumber=10606566
Reference16 articles.
1. Intelligent fault diagnosis and remaining useful life prediction of rotating machinery;Lei;Xi’ an Jiaotong University,2016
2. Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis
3. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders
4. Feature-Level Attention-Guided Multitask CNN for Fault Diagnosis and Working Conditions Identification of Rolling Bearing
5. DCNN-Based Multi-Signal Induction Motor Fault Diagnosis
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