Ultrasonic imaging recognition of coal-rock interface based on the improved variational mode decomposition

Wei, Wei; Li, Li; Shi, Wan-fa; Liu, Jiapeng

doi:10.1016/j.measurement.2020.108728

Cited by 28 publications

(10 citation statements)

References 30 publications

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“…So far, a large number of scholars have conducted extensive research on coal-rock interface identification methods, and proposed a variety of coal-rock interface identification methods. The most influential methods are gamma-ray inspection, 5 ultrasonic inspection, 6 infrared inspection, 7 image inspection 8,9 and so on. However, because the working environment of the shearer is noisy, dusty and dark, the collected sound and image signals contain more interference information.…”

Section: Introductionmentioning

confidence: 99%

A novel recognition method of shearer cutting status based on SDP image and MCK-DCNN

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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To solve the problem of large interference of shearer rocker arm vibration signal and difficulty in feature selection, and recognize accurately the cutting status of shearer, a novel pattern identification method based on Symmetrized Dot Pattern (SDP), Local Mean Decomposition (LMD) and Multi-Scale Convolution Kernel Deep Convolutional Neural Network (MCK-DCNN) is presented. Firstly, the vibration signal of shearer rocker arm is decomposed by LMD to get multiple product functions (PFs). The previous three PFs are transformed into SDP images with different features by SDP method, which are input into MCK-DCNN model to automatically extract features and identify shearer cutting status. The method can achieve the classification rate of 97.9%, which is superior to 1D_CNN and LeNet. The comparison result indicates that the method can provide technical support for improving the automatic coal cutting performance of the shearer.

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Section: Introductionmentioning

confidence: 99%

A novel recognition method of shearer cutting status based on SDP image and MCK-DCNN

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Zhong et al used similar simulation tests and numerical simulation tests to study the adaptability of the inclined trapezoidal and straight-walled semicircular arched roadway to the coal and rock interbed roadway; orthogonal simulation tests were conducted to obtain 6 coal rock instability factors of interlevel roadways [13,14]. Wei et al studied ultrasonic imaging recognition of coal-rock interface based on the improved variational mode decomposition [15]. Shou et al studied experimental and analytical investigation on the coupled elastoplastic damage model of coal-rock [16].…”

Section: Introductionmentioning

confidence: 99%

Instability Analysis and Reinforcement Support Technology of Coal‐Rock Interbed Roadway in Gaojiazhuang Coal Mine

Liu

Guo-hua

2021

Advances in Civil Engineering

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In order to effectively solve a series of problems such as the difficulty of coal and rock interbed roadway support in Gaojiazhuang Coal Mine and get a scientific and reasonable optimization scheme of surrounding rock support, theoretical analysis, numerical simulation, ultrasonic detection, field-effect test, and other means are adopted to analyze the instability of coal and rock interbed roadway. The results show that the interbedded roadway has weak interbedded cementation, and its ore pressure is more intense due to the influence of its interbedded weak structural plane. Based on Mohr’s strength envelope principle, it is proposed that horizontal stress is the main factor that causes a wide range of shear displacement, penetration crack, and surrounding rock failure of the roof of this kind of roadway. Through the finite element numerical simulation analysis, the deformation and failure law, stress distribution characteristics, and failure area distribution characteristics of coal and rock interbedding roadway surrounding rock are theoretically revealed, and the control effect of different support schemes on roadway surrounding rock deformation is greatly different. Based on the ultrasonic detection technology, it is proved that the roadway side failure has strong zoning characteristics, and the failure range and stress distribution range of the surrounding rock of the belt roadway in the 2103 working face of Gaojiazhuang Coal Mine are detected. Finally, the coupling strengthening support scheme combining prestressed anchor cable and bolt is proposed. The engineering application and the observation of surrounding rock deformation show that the reinforced support technology can effectively enhance the stability of the surrounding rock of the interbed roadway in Gaojiazhuang Coal Mine, and it has a good reference for the surrounding rock conditions of this kind of roadway.

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“…The latest signal processing methods include Wavelet Transform (WT) [ 22 ], Empirical Mode Decomposition (EMD) [ 23 ], Ensemble Empirical Mode Decomposition (EEMD) [ 24 ], Empirical Wavelet Transform (EWT) [ 25 ], and Variational Mode Decomposition (VMD) [ 26 ]. To address the shortcomings of Fourier Transform (FT) and Wavelet Transform (WT) signal processing methods in coal and rock recognition, Wei et al proposed an improved Empirical Variational Mode Decomposition (EVMD) method for ultrasonic echo signal processing [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Liu

et al. 2021

Entropy

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This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.

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Ultrasonic imaging recognition of coal-rock interface based on the improved variational mode decomposition

Cited by 28 publications

References 30 publications

A novel recognition method of shearer cutting status based on SDP image and MCK-DCNN

A novel recognition method of shearer cutting status based on SDP image and MCK-DCNN

Instability Analysis and Reinforcement Support Technology of Coal‐Rock Interbed Roadway in Gaojiazhuang Coal Mine

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

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