A nonlinear total variation based denoising method for electrostatic signal of low signal-to-noise ratio

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.

Electrostatic Signal Self-Adaptive Denoising Method Combined with CEEMDAN and Wavelet Threshold

“…Additionally, the method requires manual specification of the number of VMD modes, which can be a limitation. (4) TVD [19] shows a notable improvement in reducing noise while preserving the main features of the signal in Figure 20d. It effectively suppresses noise, resulting in a cleaner signal.…”

“…This duration is sufficient to observe transient behaviors in the system under study without unnecessary data redundancy. We selected a 2000 Hz sampling rate based on best practices from several studies and similar research in signal processing [19,[32][33][34]. According to the Nyquist theorem, these studies recommend a sampling rate of at least twice the highest frequency component in the signal.…”

“…Moreover, it lacks a standardized metric for evaluating decomposition results, and the components derived from VMD do not adhere to orthogonality constraints [18]. Zhong et al applied the Total Variation Denoising (TVD) [19] method to denoise electrostatic signals from aero-engine exhaust. TVD is a signal processing technique used to reduce noise in a signal while preserving important features such as edges.…”

The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis

“…Therefore, However, during the actual engine testing, it was observed that the electrostatic sensor had hysteresis. When high-speed airflow from the engine passes through the sensor, the particle fault signal often appears as a positive pulse signal (for particles carrying a positive charge) or a negative pulse signal (for particles carrying a negative charge) [52], as shown in Figure 13. For the same input quantity, the input signal for the sensor's positive excursion (increasing input quantity) or negative excursion (decreasing input quantity) is not of equal magnitude, and this phenomenon is known as hysteresis.…”