CTNet: A data-driven time-frequency technique for wind turbines fault diagnosis under time-varying speeds

Author:

Zhao Dezun,Shao Depei,Cui Lingli

Funder

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

Publisher

Elsevier BV

Reference47 articles.

1. GWEC G W E C Global wind report 2023[J], 2023.

2. A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis[J];Cui;Expert Syst Appl,2024

3. Wind turbine planetary gearbox fault diagnosis via proportion-extracting synchrosqueezing chirplet transform[J];Zhang;J Dyn, Monit Diagn,2023

4. Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time–frequency representation[J];Zhao;Mech Syst Signal Process,2024

5. Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based support vector machine[J];Wang;ISA Trans,2023

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