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3 results for “CEEMDAN”
Dataset of Geomagnetic Storm Forcasting with CEEMDAN-CWT
<p>Dataset of geomagnetic storm forcasting with CEEMDAN-CWT, associated with manuscript 《A new method for predicting non-recurrent geomagnetic storms》.</p> <p> </p> <p><strong>Previous Work</strong></p> <p><strong>Ye, Q., Wang, C., He, F., Xue, B., & Zhang, X. (2022). The frequency-domain characterization of Cosmic Ray Intensity variations before Forbush decreases associated with geomagnetic storms. Space Weather, 20, e2021SW002863. https://doi.org/10.1029/2021SW002863</strong></p>
Data from: Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN
Gearbox plays most essential role in the modern machinery for transmitting the required torque along with motion and contributes to wide range of applications. Any failure in gearbox components affects the productivity and efficiency of the system. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence leads to failure of the whole mechanism. Ensemble Empirical Mode Decomposition (EEMD) comprises advancement and valuable addition in Empirical Mode Decomposition (EMD) and has been widely used in fault detection of rotating machines. However, intrinsic mode functions (IMFs) produced by EEMD often carry the residual noise. Also, the produced IMFs are different in number due to addition of white Gaussian noise, which leads to final averaging problem. To alleviate these drawbacks, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was previously presented. This paper describes and presents the implementation of CEEMDAN for fault diagnosis of simulated local defects using sound signals in a fixed-axis gearbox. Statistical parameters are extracted from decomposed sound signals for different simulated faults. Results shows the effectiveness of CEEMDAN over EEMD in order to obtain more accurate IMFs and fault severity.
Data from: Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN
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