Efficient harmonic regeneration noise reduction‐based Wiener filter for acoustic emission signal detection

Abstract

In this Letter, the authors propose to apply the harmonic regeneration noise reduction (HRNR)‐based Wiener filter (W‐HRNR) to detect acoustic emission (AE) signal from the noisy environment. HRNR technique can regenerate the harmonics to overcome the distortion which occurs with overestimation of conventional Wiener filter and to preserve the essential signal content for accurate detection of AE signal. W‐HRNR is implemented in short‐time Fourier transform domain. Decision direct method is employed to estimate the spectral gain and signal‐to‐noise ratio (SNR) of the signal in each frame. Evaluation of the algorithm is carried out using peak‐SNR (PSNR) and mean square error (MSE). The performance of the proposed algorithm has been demonstrated using a simulated AE burst signal and AE signals acquired from compression test and drilling test. Results have proven the remarkable performance of W‐HRNR over existing conventional Wiener filter for AE with accurate detection by preserving the useful signal information even under high noise conditions at low frequencies. Moreover, W‐HRNR also achieved a significant improvement in PSNR and MSE over the conventional filter.

Early Detection of Coal Spontaneous Combustion by Complex Acoustic Waves in a Concealed Fire Source

“…Traditional signal analysis and processing techniques: with the acoustic signal from the time domain signal through the Fourier transform to the frequency domain, the signal waveform is described as a variable as frequency, reflecting the frequency and frequency domain of the signal. Prajna et al 93 94 used wavelet transforms to amplify local characteristics in the time and frequency domains and found that processed signals can accurately reflect the frequency components of the original acoustic signals, the noise was eliminated effectively, but there are limitations to the application of wavelet and Fourier transforms to certain nonstationary and nonlinear signals.…”

“…Traditional signal analysis and processing techniques: with the acoustic signal from the time domain signal through the Fourier transform to the frequency domain, the signal waveform is described as a variable as frequency, reflecting the frequency and frequency domain of the signal. Prajna et al 93 used a harmonic regenerative noise reduction (HRNR)-based Wiener filter to detect acoustic emission (AE) signals from noisy environments, and the HRNR technique can regenerate harmonics, overcome distortions that occur with overestimation of the conventional Wiener filter, and preserve the essential signal content of AE signals. Liang et al 94 used wavelet transforms to amplify local characteristics in the time and frequency domains and found that processed signals can accurately reflect the frequency components of the original acoustic signals, the noise was eliminated effectively, but there are limitations to the application of wavelet and Fourier transforms to certain nonstationary and nonlinear signals.…”

Four‐dimensional dynamic force measurement system for micro‐deep hole drilling

“…4 a , and the output signal u o can be expressed linearly as: where U i is the sensor supply voltage, R is the nominal value of sensor and ΔR is variation under pressure. In addition, a digital moving average filter (MAF) is used after the analogue LPF [5], it achieves a signal‐to‐noise improvement ratio of more than 19 as shown in Fig. 4 b , and still maintains good dynamic response performance at the same time.…”