Abstract
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%.
Highlights
The distribution of coal and rock in coal mines is intricate
This paper proposed a cutting sound signal recognition method based on the ICEEMDAN, improved grey wolf optimizer (IGWO), and support vector machine (SVM) algorithms on the basis of previous research
This paper proposes a cutting pattern recognition model based on ICEEMDAN
Summary
The distribution of coal and rock in coal mines is intricate. Improper cutting operations will affect the process of coal mining. To improve the accuracy of the shearer cutting pattern recognition, this paper proposes the use of a composite multi-scale permutation entropy (CMPE) as the characteristic value of the cutting sound signal. This paper proposed a cutting sound signal recognition method based on the ICEEMDAN, IGWO, and SVM algorithms on the basis of previous research. This paper introduced differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm It optimized the parameters of the SVM and established an IGWO–SVM cutting pattern recognition model. (3) The paper built a cutting pattern recognition model based on the SVM combined with the cutting sound signal data It introduced the DE algorithm and nonlinear convergence factor to improve the GWO algorithm and optimized the identification model parameters.
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