A new data generation approach with modified Wasserstein auto-encoder for rotating machinery fault diagnosis with limited fault data

Author:

Zhao Ke,Jiang HongkaiORCID,Liu Chaoqiang,Wang Yanfeng,Zhu Ke

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference42 articles.

1. Modified deep auto-encoder driven by multi-source parameters for fault transfer prognosis of aero-engine;He;IEEE Trans. Ind. Electron.,2022

2. Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing;Chen;Measurement,2019

3. Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis;Zhao;Knowl.-Based Syst.,2021

4. Modified stacked auto-encoder using adaptive morlet wavelet for intelligent fault diagnosis of rotating machinery;Shao;IEEE/ASME Trans. Mechatronics,2021

5. A fusion CWSMM-based framework for rotating machinery fault diagnosis under strong interference and imbalanced case;Li;IEEE Trans. Ind. Inf.,2021

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