Arrival Picking of Acoustic Emission Signals Using a Hybrid Algorithm Based on AIC and Histogram Distance

Chen, Hongpeng; Yang, Zhensheng

doi:10.1109/tim.2020.3041103

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…In addition, it also includes some other methods, such as machine learning [21], cluster analysis [22,23], cross-correlation [24], neural networks [25], deep learning [26,27], etc. The time difference localization method relies on accurate algorithms for picking, such as STA/LTA [28], ultrasonic transmission [29], Akaike information criterion [30], C-Means clustering [31], delay time [32], wavelet transform [33], etc. Of course, if the event is interfered with by background noise or other factors, filtering measures should be taken to reduce noise or remove interference [34].…”

Section: Introductionmentioning

confidence: 99%

Waveform Complexity and Positioning Analysis of Acoustic Emission Events during the Compression Failure Process of a Rock Burst Prone Sample

Zhang,

Yu,

et al. 2024

Buildings

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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.

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Section: Introductionmentioning

confidence: 99%

Waveform Complexity and Positioning Analysis of Acoustic Emission Events during the Compression Failure Process of a Rock Burst Prone Sample

Zhang,

Yu,

et al. 2024

Buildings

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“…In recent years, studies on P-wave pickup times have been improved with STA/LTA and AIC methods. Chen and Yang [4] proposed bin-to-bin distance and the AIC method to enhance picking accuracy. Wang et al [5] applied singlesegment Autoregressive-Bayesian information criterion (AR-BIC) and Autoregressive-Akaike information criterion (AR-AIC) algorithms to solve the complexity of the amplitude distribution in the time series of AE waveform data during P-wave pickup.…”

Section: Introductionmentioning

confidence: 99%

Method for the P-wave arrival pickup of rock fracture acoustic emission signals under strong noise

Luo,

Bespal’Ko,

et al. 2024

Meas. Sci. Technol.

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This research aimed to investigate the accuracy of picking of P-wave arrival times in rock fracture acoustic emission signals. In order to simulate the mining scenario, Gaussian white noise and pulse noise were added to the data collected in the laboratory. CEEDMAN+Wavelet was improved in this paper, where the Spearman rank correlation coefficient was adopted to effectively select IMFs for denoising which retained the inherent characteristics of the rock fracture signal. The absolute amplitude and energy change rate of the envelope signal, calculated based on the Hilbert transform, were used as the input of the STA/LTA normalization algorithm to pickup the P-wave arrival time. The reliability of this method was tested on 30 groups of recorded rock fracture laboratory data and 60 groups of added noise data. Taking the manual pickup results as the standard, the errors of CEEDMAN+Wavelet+STA/LTA+AIC method with the absolute amplitude of the signal as the input are all within 10ms, and 86.67% of the results are within 5ms. The method proposed in this paper effectively addressing the issue of false pickup caused by the sensitivity of AIC and traditional STA/LTA method for strong noise, and achieving relatively high accuracy and stability in processing low SNR signals. This work contributes to monitor microscopic changes in rock bodies and is of great significance for the prediction and monitoring of geological disasters.

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“…Numerous studies exist in the literature demonstrating the effectiveness of AIC on the signals. Current studies are generally aimed to improve the method by developing it with different approaches and/or automating it during structural monitoring [17][18][19]. In this study, instead to search the method that captures the arrival time most accurately, the question of how the improvements that can be made on the signal form affect the arrival time estimation has been investigated.…”

Section: Introductionmentioning

confidence: 99%

Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

Tayfur

2024

Sakarya University Journal of Science

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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.

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Arrival Picking of Acoustic Emission Signals Using a Hybrid Algorithm Based on AIC and Histogram Distance

Cited by 8 publications

References 22 publications

Waveform Complexity and Positioning Analysis of Acoustic Emission Events during the Compression Failure Process of a Rock Burst Prone Sample

Waveform Complexity and Positioning Analysis of Acoustic Emission Events during the Compression Failure Process of a Rock Burst Prone Sample

Method for the P-wave arrival pickup of rock fracture acoustic emission signals under strong noise

Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

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