## 1 Introduction
By extracting the fuzzy entropy of the vibration signal as the characteristic value, [PERSON] effectively improved the accuracy of the motor-bearing fault diagnosis [22]. [PERSON] calculated the energy entropy after EMD decomposition of the current signal and used a support vector machine (SVM) to identify fault types [23]. [PERSON] adopted permutation entropy to detect weak data mutations [24]. However, these entropy methods can only measure the complexity of time series on a single scale. For the cutting sound signal, considering the non-uniformity of the cutting medium and the complexity of drum pick distribution, it is difficult to ensure the accuracy of the cutting pattern recognition for the single time scale feature extraction of the cutting sound signal [25]. Therefore, to make up for the lack of single-scale accuracy, [PERSON] proposed multi-scale entropy to measure the complexity of time series on different scales [26; 27]. [PERSON] detected microseismic events by extracting the multi-scale permutation entropy of signals [28]. The multi-scale permutation entropy (MPE) calculates the permutation entropy of the signal at multiple scales. Compared with single-scale permutation entropy, MPE reflects more abundant signal feature information. Meanwhile, MPE has a strong anti-noise ability [29]. However, in the MPE calculation process, as the scale factor increases, the limitations of the time series coarse-graining process cause the time series to shorten, resulting in the loss of feature information [30]. Therefore, 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. The SVM has a simple algorithm structure and good generalization performance, effectively solving small sample pattern recognition [31]. The essence of the shearer cutting pattern is pattern recognition. The SVM has been widely used in the field of pattern recognition, and the recognition effect is good. Especially when there are few data samples, it can also carry out accurate classification and recognition research. Nevertheless, in the algorithm, the penalty parameter c and the kernel function parameter g significantly impact the learning performance [32; 33]. Therefore, it is necessary to optimize its parameters. The use of swarm intelligence optimization algorithms to optimize the parameters of support vector machines is a current research hotspot. Some scholars have applied the particle swarm optimization (PSO) algorithm, the genetic algorithm, and the artificial bee colony (ABC) algorithm to the parameter optimization process of the SVM [34; 35; 36]. However, the above optimization algorithm quickly falls into the local optimum, and the algorithm is time-consuming. This paper used a grey wolf optimizer (GWO) to optimize the parameters of the SVM. The GWO is an ideal SVM parameter optimization algorithm with a simple structure, easy implementation, and few control parameters [37]. Nevertheless, the GWO algorithm easily falls into the local optimum, and the later convergence speed is slow [38]. Thus, this paper proposed an improved grey wolf optimization algorithm (IGWO) to promote the recognition accuracy and efficiency of the SVM. The IGWO improves the global search capability of the algorithm by introducing the differential evolution (DE) algorithm and the nonlinear convergence factor. The method helps the algorithm jump out of the local optimum and improves the later convergence speed of the algorithm. This paper proposed a cutting sound signal recognition method based on the ICEEMDAN, IGWO, and SVM algorithms on the basis of previous research. First, the method applied ICEEMDAN to process the cutting sound signal. It selected the characteristic IMF components through the correlation coefficient and calculated its CMPE as the characteristic value. Then, this paper introduced differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm.