Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large Speed Fluctuation Conditions-Reference-Cited by-同舟云学术

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Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large Speed Fluctuation Conditions

Published:2022 Issue: Volume: Page:1-12
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

An Zenghui¹^ORCID,Jiang Xingxing²^ORCID,Yang Rui¹,Zhang Hanyuan³,Liu Jie⁴^ORCID,Shen Changqing²^ORCID

Affiliation:

1. School of Mechanical and Electrical Engineering, Shandong Jianzhu University, Jinan, China

2. School of Rail Transportation, Soochow University, Suzhou, China

3. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, China

4. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Prospective Application Research of Suzhou

Opening Foundation of Shanxi Key Laboratory of Advanced Manufacturing Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/4389054/09933861.pdf?arnumber=9933861

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1. Faulty rolling bearing digital twin model and its application in fault diagnosis with imbalanced samples;Advanced Engineering Informatics;2024-08

2. DNOCNet: A Novel End-to-End Network for Induction Motor Drive Systems Fault Diagnosis Under Speed Fluctuation Condition;IEEE Transactions on Industrial Informatics;2024-06

3. Inverse physics–informed neural networks for digital twin–based bearing fault diagnosis under imbalanced samples;Knowledge-Based Systems;2024-05

4. A New Dual-Domain Signal Collaborative Transfer Network for Bearing Fault Diagnosis;IEEE Transactions on Instrumentation and Measurement;2024

5. Selective Feature Reinforcement Network for Robust Remote Fault Diagnosis of Wind Turbine Bearing Under Non-Ideal Sensor Data;IEEE Transactions on Instrumentation and Measurement;2024

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