Design and Application of Simulating Cutting Experiment System for Drum Shearer

Peng, Tianhao; Li, Changpeng; Zhu, Yanmin

doi:10.3390/app11135917

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…In order to obtain the sound signal generated by the shearer cutting, based on the similarity theory, this paper takes the MG2X160/710WD thin shearer as the prototype machine. 31 It develops the shearer simulation cutting experimental system, as described in Figure 9.…”

Section: Establishment Of the Experimental Systemmentioning

confidence: 99%

Noise reduction method of shearer’s cutting sound signal under strong background noise

Li¹,

Peng²,

Zhu³

et al. 2022

Measurement and Control

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In coal and rock recognition technology, the acquisition of sound signals is affected by background noise. It is challenging to extract cutting features and accurately identify cutting patterns effectively. Therefore, this paper proposes an approach for combined noise reduction of the cutting sound signal based on the improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN) and a singular value decomposition (SVD). First, the method used the ICEEMDAN method to decompose the noisy signal into several intrinsic mode functions (IMF). It calculated the correlation coefficient between the IMF component and the noisy signal and then selected the noisy IMF components based on the threshold formula. Meanwhile, this method constructed a Hankel matrix of the noisy IMF component signals. It used SVD technology to obtain the singular values. According to the singular value standard energy spectrum curve, the paper determined the order of the effective singular value and removed the noise component in the signal. Then, the denoised IMF and noiseless IMF components are superimposed and reconstructed to obtain the noise-reduced cutting sound signal. Finally, it applied simulation signal and simulated shearer cutting experiment to verify the performance of the method. The results show that the proposed method can effectively remove the influence of background noise in the signal and retain the characteristic frequencies of the original cutting sound signal. Compared with traditional noise reduction methods, the ICEEMDAN-SVD combined noise reduction method performs better in noise reduction evaluation standards of signal-noise ratio and root mean square error. It achieved a better noise reduction effect, which could help coal and rock recognition technology based on sound signals.

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Section: Establishment Of the Experimental Systemmentioning

confidence: 99%

Noise reduction method of shearer’s cutting sound signal under strong background noise

Li¹,

Peng²,

Zhu³

et al. 2022

Measurement and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…It took the cutting drum as the research object. Meanwhile, a similarity coefficient of 1 4 was applied to develop the system [49]. According to the existing data, the compressive strength of artificial coal samples is about 2.5 times that of natural coal samples.…”

Section: Cutting Sound Signal Acquisitionmentioning

confidence: 99%

A Cutting Pattern Recognition Method for Shearers Based on ICEEMDAN and Improved Grey Wolf Optimizer Algorithm-Optimized SVM

2021

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When the shearer is cutting, the sound signal generated by the cutting drum crushing coal and rock contains a wealth of cutting status information. In order to effectively process the shearer cutting sound signal and accurately identify the cutting mode, this paper proposed a shearer cutting sound signal recognition method based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN) and an improved grey wolf optimizer (IGWO) algorithm-optimized support vector machine (SVM). First, the approach applied ICEEMDAN to process the cutting sound signal and obtained several intrinsic mode function (IMF) components. It used the correlation coefficient to select the characteristic component. Meanwhile, this paper calculated the composite multi-scale permutation entropy (CMPE) of the characteristic components as the eigenvalue. Then, the method introduced a differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm. It used the improved GWO algorithm to realize the adaptive selection of SVM parameters and established a cutting sound signal recognition model. According to the proportioning plan, the paper made several simulation coal walls for cutting experiments and collected cutting sound signals for cutting pattern recognition. The experimental results show that the method proposed in this paper can effectively process the cutting sound signal of the shearer, and the average accuracy of the cutting pattern recognition model reached 97.67%.

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“…GAO and ZHANG [5] presented a method of radial size design of the relief groove of torque shaft based on combining positive reverse reliability with unloading coefficient. PENG et al [6] designed a simulation cutting experiment system to test the reliability of shearer spiral cutting drum. YANG et al [7] analyzed the dynamic characteristics of key parts of shearer under different working conditions in order to optimize the shearer hardware structure.…”

Section: Introductionmentioning

confidence: 99%

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

Zhipeng

2023

Matéria (Rio J.)

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Shearer reliability is considered as one of the most important indexes in longwall mining production. However, the traditional reliability methods are based on the specific distribution of the failure parameters, which are incongruent in the actual practice. Therefore, a novle shearer reliability prediction method based on support vector machine (SVM) with chaotic particle swarm optimization (CPSO) is proposed. It combines the advantages of the high accuracy of SVM and the fast convergence of CPSO, where the chaos idea is introduced to particle swarm optimization for the particle initialization, inertia weight coefficient optimizing and premature convergence treatment. Then this CPSO is used to select and optimize the important parameters of SVM. Ultimately, the optimized parameters are used to obtain a superior CPSO-SVM method for reliability prediction.To show the effectiveness of the proposed method, two numerical comparisons are designed respectively using the literature data and the actual shearer data from the coal mine enterprise. The research results reveal the prediction accuracy and validity of the proposed method.

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Design and Application of Simulating Cutting Experiment System for Drum Shearer

Cited by 10 publications

References 24 publications

Noise reduction method of shearer’s cutting sound signal under strong background noise

Noise reduction method of shearer’s cutting sound signal under strong background noise

A Cutting Pattern Recognition Method for Shearers Based on ICEEMDAN and Improved Grey Wolf Optimizer Algorithm-Optimized SVM

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

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