A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditions

Author:

Wu Jie,Tang Tang,Chen Ming,Wang Yi,Wang Kesheng

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference41 articles.

1. Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., & Marchand, M. (2014). Domain-adversarial neural networks.arXiv preprint arXiv:1412.4446.

2. Online signature verification by continuous wavelet transformation of speed signals;Alpar;Expert Systems with Applications,2018

3. Bearing faults diagnosis using fuzzy expert system relying on an improved range overlaps and similarity method;Berredjem;Expert Systems with Applications,2018

4. Case Western Reserve University Bearing Data Center 12k drive end bearing data 2019.

5. C. Chen Z. Li J. Yang B. Liang May). A cross domain feature extraction method based on transfer component analysis for rolling bearing fault diagnosis 2017 IEEE 5622 5626.

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