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

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%.

An Improved Sparrow Search Algorithm for Optimizing Support Vector Machines

“…The initial population of the SSA algorithm is not uniformly distributed in the search space, so it has low ergodicity, less population variety and slow speed of convergence. Furthermore, the e -x exponential function in equation (3) shows that the algorithm has slightly faster decline speed and smaller disturbance range at the start of the iteration, resulting in insufficient search. However, the decline speed of the algorithm is slow and the disturbance range is too large in the middle and later stages.…”

“…The swarm intelligence optimization algorithm can solve this problem. Many scholars have used the swarm intelligence optimization algorithm, such as the Gray Wolf Optimization algorithm [3] (GWO), Whale Optimization Algorithm [4] (WOA) to optimize the SVM kernel parameters. However, the search accuracy of the GWO is easily affected by the head wolf and the search speed is low.…”

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

“…Support vector machine is a machine learning model for binary classification, which is especially suitable for classification of small samples and high-dimensional features [33]. In view of the above problems, inspired by the good optimization ability of the wolf pack intelligent optimiza-tion algorithm and the classification performance of SVM, this paper applies the grey wolf optimization (GWO) algorithm to optimize SVM parameters [34] and proposes a method for coal gangue identification based on GWO-SVM. This method is different from any previous method of coal gangue identification.…”

Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

“…Sun et al [20] proposed an equilibrium Gray Wolf Optimization algorithm with refracted reverse learning, which overcame the problem of the low population diversity of GWO groups in the later stage and reduced the possibility of falling into local extremes. Li et al [21] introduced a differential evolution algorithm and nonlinear convergence factor into the traditional GWO algorithm to solve the problem that the algorithm can easily fall into the local optimum. Zhou et al [22] proposed a nonlinear convergence factor and search mechanism so that, when hunting, the update of the wolf pack is not only affected by the three leading wolves but also by the position of the surrounding wolves.…”