Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis

ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.net/journal/jcc
E-mail: [email protected]

Citations

"Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis"
written by Jian Di, Leilei Wang,
published by Journal of Computer and Communications, Vol.6 No.7, 2018

has been cited by the following article(s):

[1]	Investigating the interactive effect of financial constraints and development on the relationship between stock liquidity and the company's future investment
	Financial Management …, 2023

[2]	One-dimensional convolutional neural network based bearing fault diagnosis
	Open Access Library Journal, 2022

[3]	New intelligent fault diagnosis approach of rolling bearing based on improved vibration gray texture image and vision transformer
	wei, M Ning-ge, Z Xu-hui… - Proceedings of the …, 2022

[4]	Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results
	2020

[5]	Developing a Hybrid Expert/Data-Driven Health Index for Railway Axleboxes Using Auto-Encoder Neural Networks
	2019 Prognostics and System Health Management Conference (PHM-Paris), 2019

[6]	Low-Speed Bearing Fault Diagnosis Based on ArSSAE Model Using Acoustic Emission and Vibration Signals
	IEEE Access, 2019

[1]	Scalable and generalizable AI-based intrusion detection in AIoT: a review and lightweight ensemble solution International Journal of Data Science and Analytics, 2026 DOI:10.1007/s41060-025-00980-z

[2]	Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture Engineering Reports, 2025 DOI:10.1002/eng2.70202

[3]	Optimized GRU with Self-Attention for Bearing Fault Diagnosis Using Bayesian Hyperparameter Tuning Algorithms, 2025 DOI:10.3390/a18090576

[4]	New intelligent fault diagnosis approach of rolling bearing based on improved vibration gray texture image and vision transformer Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2022 DOI:10.1177/09544062221085871

[5]	European Workshop on Structural Health Monitoring Lecture Notes in Civil Engineering, 2021 DOI:10.1007/978-3-030-64594-6_51

[6]	Low-Speed Bearing Fault Diagnosis Based on ArSSAE Model Using Acoustic Emission and Vibration Signals IEEE Access, 2019 DOI:10.1109/ACCESS.2019.2909756

[7]	Developing a Hybrid Expert/Data-Driven Health Index for Railway Axleboxes Using Auto-Encoder Neural Networks 2019 Prognostics and System Health Management Conference (PHM-Paris), 2019 DOI:10.1109/PHM-Paris.2019.00037