Study on Multiple Fractal Analysis and Response Characteristics of Acoustic Emission Signals from Goaf Rock Bodies

Xie, Xuefang; Li, Shaoqian; Guo, Jiang

doi:10.3390/s22072746

Cited by 14 publications

(9 citation statements)

References 42 publications

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“…In the 1980s, Grassberger introduced the concept of a multifractal. Unlike monofractal methods, multiple fractal methods describe the fractal structure using spectral functions, which allow us to assess the variability of an object at different levels and conduct a more in-depth analysis [28].…”

Section: Introductionmentioning

confidence: 99%

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Zagretdinov,

Ziganshin,

Izmailova

et al. 2024

Fractal Fract

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In this paper, the possibility of using monofractal and multifractal analysis of acoustic signals of pipelines to detect leaks is considered. An experimental stand has been created to study the fractal characteristics of acoustic signals of pipelines with “slit” type defects. During the experiments, defects of the “slit” type pipeline with dimensions of 2 mm, 8 mm, and 20 mm were modeled. Detrended fluctuation analysis (DFA) and the multifractal detrended fluctuation analysis (MF-DFA) were used. As a result of the experimental studies, it was found that the occurrence of leakage leads to the occurrence of anticorrelated vibrations in a pipeline with multifractal properties. The analyses of acoustic signals by DFA and MF-DFA methods make it possible to reliably determine the leakage. The Hurst exponent and the width of the multifractal spectrum can serve as indicators of the occurrence of leaks in pipelines.

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Section: Introductionmentioning

confidence: 99%

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Zagretdinov,

Ziganshin,

Izmailova

et al. 2024

Fractal Fract

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“…Ring counting is the number of oscillations of the transducer over the threshold signal, which is a monitoring datum related to signal amplitude in acoustic emission testing. It reflects the rock fracture situation and is widely used for analyzing the evolution of rock cracks [ 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Mechanical Behavior and Crack Evolution of Goaf Surrounding Rock with Different Roof-Contacted Filling Rates

Wang

Huang

et al. 2023

Materials

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The goaf formed by mining is filled and treated, which greatly improves the safety and stability of the surrounding rock. During the filling process, the roof-contacted filling rates (RCFR) of goaf were closely related to the stability control of the surrounding rock. The influence of the roof-contacted filling rate on the mechanical characteristics and crack propagation of the goaf surrounding rock (GSR) has been studied. Biaxial compression experiments and numerical simulation experiments were conducted on samples under different operating conditions. The results were as follows: (1) The peak stress, peak strain, and elastic modulus of the GSR are closely related to the RCFR and the goaf size; they increase with the increase of the RCFR, and decrease with the increase of the goaf size; (2) In the initial loading stage, a small number of cracks are generated, and the acoustic emission ringing count increases slowly. The mid-loading stage is the crack initiation and rapid expansion, and the cumulative ring count curve shows a “stepwise” growth. In the later loading stage, cracks continue to propagate and form macroscopic fractures, but the number of rings significantly decreases; (3) Shear cracks are prone to occur in the rock part of the GSR; tensile cracks are prone to occur in the backfill; and the crack propagation speed in the rock is faster than in the backfill. Stress concentration is the direct cause of GSR failure. The maximum concentrated stress of rock mass and backfill is 1~2.5 times and 0.17~0.7 times of the peak stress of the GSR, respectively.

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“…In previous studies, some experiments often overlooked the viewing of the original waveform and the optimization selection of algorithms and directly used the built-in software of the AE monitoring instrument for positioning, which lacked process analysis and affected the credibility of the positioning results [40]. The exploration and accurate processing of the original waveform is an important foundation for subsequent recognition and localization [41,42]. The quality of the original waveform directly determines the accuracy of event recognition and the method of picking up take-off points for event localization [43].…”

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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Study on Multiple Fractal Analysis and Response Characteristics of Acoustic Emission Signals from Goaf Rock Bodies

Cited by 14 publications

References 42 publications

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Mechanical Behavior and Crack Evolution of Goaf Surrounding Rock with Different Roof-Contacted Filling Rates

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

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