“…However, it should be pointed out that the feedback of destruction requires precise event localization results, and event localization heavily depends on the quality of the original waveform of the sensor. The key factors in the accurate location of AE events [15] mainly include the accurate identification of events [16] and the selection of location methods [17]. First of all, the signals caused by rock fracture should be selected from many vibration signals, and then the corresponding index should be extracted for positioning (sound waveforms are the foundation for accurate positioning, and with this foundation, index information such as take-off points can be extracted for positioning).…”
The localization results of acoustic emission (AE) events can reflect the location and pattern of burst-prone rock failures. However, event localization heavily depends on the quality of the original waveform of the sensor. Therefore, this study analyzed the AE waveform of a rock sample under compression to evaluate its failure localization and quality. From the research results, it could be seen that the initial failure was relatively calm, with clear take-off points, which can be better used for accurate AE event positioning. However, the later failure was severe, causing the take-off points of most sensors to be very unclear, and positioning methods that rely on take-off points cannot be used for positioning, let alone simply using the positioning results of the built-in software. This research result reminds researchers who use AE signals for event localization to first examine the quality and status of the original waveform, providing a basis for obtaining accurate localization results, in order to further accurately study the subsequent failure patterns. The above facts indicate that the initial failure is small and scattered, while the later failure is large and concentrated, with certain fractal characteristics.
“…However, it should be pointed out that the feedback of destruction requires precise event localization results, and event localization heavily depends on the quality of the original waveform of the sensor. The key factors in the accurate location of AE events [15] mainly include the accurate identification of events [16] and the selection of location methods [17]. First of all, the signals caused by rock fracture should be selected from many vibration signals, and then the corresponding index should be extracted for positioning (sound waveforms are the foundation for accurate positioning, and with this foundation, index information such as take-off points can be extracted for positioning).…”
The localization results of acoustic emission (AE) events can reflect the location and pattern of burst-prone rock failures. However, event localization heavily depends on the quality of the original waveform of the sensor. Therefore, this study analyzed the AE waveform of a rock sample under compression to evaluate its failure localization and quality. From the research results, it could be seen that the initial failure was relatively calm, with clear take-off points, which can be better used for accurate AE event positioning. However, the later failure was severe, causing the take-off points of most sensors to be very unclear, and positioning methods that rely on take-off points cannot be used for positioning, let alone simply using the positioning results of the built-in software. This research result reminds researchers who use AE signals for event localization to first examine the quality and status of the original waveform, providing a basis for obtaining accurate localization results, in order to further accurately study the subsequent failure patterns. The above facts indicate that the initial failure is small and scattered, while the later failure is large and concentrated, with certain fractal characteristics.
“…The arrival time of the signal at the sensor is one of the two main parameters necessary to determine the location of the damage [6][7][8]. Since the propagation velocity of the wave, which is the other parameter in this problem, is very high (approximately 2500-3500 m/sec) in the concrete material [9], even microsecond errors in the arrival time calculation cause the damage Research Article location to be calculated with high margin errors [10]. For this reason, the problem of accurate picking of this arrival time in the pre-trigger window, which is mixed with noise, has been one of the focal points of signal processing studies for structural damage assessment in the literature.…”
To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wave-based methods is to place appropriate sensors on the structure, to detect acoustic wave propagation and to analyze these signals the sensors transformed. The arrival time of these recorded signals to the sensors is the most significant parameter used to determine critical information such as the time and location of the damage. Therefore, the accurate calculation of the arrival time affects the accuracy of the damage detection. In this study, effects of the signal-to-noise ratio (SNR), sampling frequency, length of the signal, and length of the focal window on determining the arrival time of the signals to the sensors were investigated. For this purpose, an energy-traced arrival time picking approach (Akaike Information Criterion, AIC), which is the frequently used method in the literature, has been applied to a typical acoustic signal originated from a concrete cracking. The results of the study suggest the necessity of noise elimination, the optimum level of data logging and the ratios of focal window lengths for accurate time of arrival detection in the field monitoring of the structures using acoustic methods.
“…Meanwhile, the failure of the stressed rocks could be represented only as AE signals monitored by a limited number of sensors. Additionally, the fracturing process inside the rock sample could not be visible in real time to calibrate the AE signals 19 . Figure 1 Schematic view of AE characterization by the experiment and MTM.…”
Acoustic emission (AE) characterization is an effective technique to indirectly capture the failure process of quasi brittle rock. In previous studies, both experiments and numerical simulations were adopted to investigate the AE characteristics of rocks. However, as the most popular numerical model, the moment tensor model (MTM) cannot be constrained by the experimental result because there is a gap between MTM and experiments in principle, signal processing and energy analysis. In this paper, we developed a particle-velocity-based model (PVBM) that enabled direct monitoring and analysis of the particle velocity in the numerical model and had good robustness. The PVBM imitated the actual experiment and could fill in gaps between the experiment and MTM. AE experiments of marine shale under uniaxial compression were carried out, and the results were simulated by MTM. In general, the variation trend of the experimental result could be presented by MTM. Nevertheless, the magnitudes of AE parameters by MTM presented notable differences of more than several orders of magnitude compared with those by the experiment. We sequentially used PVBM as a proxy to analyse these discrepancies and systematically evaluate the AE characterization of rocks from the experiment to numerical simulation, considering the influence of wave reflection, energy geometrical diffusion, viscous attenuation, particle size and progressive deterioration of rock material. The combination of MTM and PVBM could reasonably and accurately acquire AE characteristics of the actual AE experiment of rocks by making full use of their respective advantages.
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