Electrostatic Signal Self-Adaptive Denoising Method Combined with CEEMDAN and Wavelet Threshold
Abstrak
A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path electrostatic signal noise, which challenges the gas-path component condition monitoring and feature extraction techniques. Firstly, the integration of CEEMDAN addresses modal aliasing and intermittent signal challenges, while the proposed low-pass filtering method autonomously selects valuable signal components. Additionally, the application of the WT in the unselected components enhances the extraction of useful information, presenting a unique and advanced approach to electrostatic signal denoising. Moreover, the proposed method is applied to simulated signals with different input signal-to-noise ratios and experimental fault electrostatic signals of a micro-turbojet engine. The comparison with several traditional approaches in a denoising test for the simulated signals and experimental signals reveals that the proposed method performs better in extracting the effective components of the signal and eliminating noise.
Penulis (6)
Yan Liu
Hongfu Zuo
Zhenzhen Liu
Yu Fu
James Jiusi Jia
Jaspreet S. Dhupia
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 9×
- Sumber Database
- CrossRef
- DOI
- 10.3390/aerospace11060491
- Akses
- Open Access ✓