A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, integrity assessment of industrial facilities and/or microseismicity studies. In all these cases, the early and reliable detection of possible damage is of paramount importance and is practically limited by detectability of transient signals on the background of random noise. The proposed triggering algorithm is based on a logarithmic derivative of the power spectral density function. It was tested on the synthetic data, which mimics the actual ultrasonic acoustic emission signal recorded continuously with different signal-to-noise ratios (SNR). Considerable advantages of the proposed method over established fixed amplitude threshold and STA/LTA (Short Time Average / Long Time Average) techniques are demonstrated in comparative tests.
Plastic deformation and fracture of materials is accompanied by generation of elastic wave transients known as acoustic emissions (AE). A novel complex methodology combining the statistical analysis of distributions of time intervals between the successive AE events, and the unsupervised cluster analysis of the time series is proposed to test for possible correlations between emitting sources and to highlight their intrinsic dynamics. Aiming at revealing the essential temporal features of the twinning and dislocation dynamics, the proposed methodology was applied to the AE signals produced during plastic deformation in a magnesium alloy where both primary deformation modes - dislocation slip and twinning - operate concurrently. It has been undoubtedly demonstrated that the mechanical twinning belongs to a class of non-Poisson processes having a memory of the past in the millisecond range. As opposes to the correlated behaviour of twins, it was shown that the dislocation slip falls into the category of Poisson processes caused by independent sources.
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