A nonlinear total variation based denoising method for electrostatic signal of low signal-to-noise ratio. (November 2022)

• Record Type: Journal Article
• Authors: Zhong, Zhirong; Zuo, Hongfu; Jiang, Heng
• Is Part Of: Advances in mechanical engineering. Volume 14:Number 11(2022)
• Publication Date: 2022-11
• Subjects: Aero-engine electrostatic monitoring -- total variation denoising (TVD) -- electrostatic sensor -- time-series signal processing -- signal denoising; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/16878132221136942
• Languages: English
• ISSNs: 1687-8132

Abstract

Aero-engine electrostatic monitoring technology (EMT) is a novel and effective condition monitoring technology. With the help of EMT, effective monitoring of early failures can be achieved. Since the electrostatic monitoring of the running engine will be strongly interfered, the sampled electrostatic signal has various noise components and low signal-to-noise ratio (SNR). After analyzing the source of the noise components carried by the electrostatic signal, this paper proposes a method for electrostatic signal denoising in a strong interference environment, which is based on the nonlinear total variation theory. In the experiments, the simulated electrostatic measurement signal and the actual test-run electrostatic measurement signal were used as the analysis objects, and the denoising test was carried out by using the proposed method. Meanwhile, the denoising effect was compared and analyzed with other classical methods. The experimental results show that the proposed denoising method can effectively remove random noise, electromagnetic pulse and periodic noise in electrostatic signal, and is more applicable to the measured electrostatic signal with low SNR than the classical electrostatic signal denoising methods such as wavelet threshold denoising method and empirical mode decomposition method.