###### Abstract  
In order to accurately acquire shearer cutting status, this paper proposed a pattern recognition method, based on the local mean decomposition (LMD), time-frequency statistical analysis, improved Laplacian score (LS), and fuzzy C-means (FCM) clustering algorithm. The LMD was employed to preprocess the vibration signals of shear cutting coal seam, and several product functions (PFs) were obtained. Following this, 14 time-frequency statistical parameters of the original signal and optimal PF were extracted. Additionally, the improved LS algorithm was designed to ensure the accurate estimation of features, and a new feature vector could be selected. Subsequently, the obtained eigenvector matrix was fed into a FCM to be clustered, for optimal clustering performance. The experimental examples were provided to verify the effectiveness of the methodology and the results indicated that the proposed algorithm could be applied to recognize the different categories of shearer cutting status. pattern recognition; feature extraction; feature selection; Laplacian score; fuzzy C-means clustering +
Footnote †: journal: Applied Sciences  
0000-0001-6000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00  
many fields [7]. EMD can decompose the complex signal into a sum of intrinsic mode functions (IMFs), and each IMF contains the local characteristics of the original signal. However, during the process of decomposition, EMD is often faced with the end effect and mode mixing problems, etc., restricting its applications [8; 9; 10]. In 2005, [PERSON] proposed a novel signal analysis method, named the local mean decomposition (LMD), and initially used it to process the electroencephalogram [11]. LMD can adaptively decompose a complicated multi-component signal into several product functions (PFs), and each PF is the product of an amplitude envelope signal and a purely frequency-modulated signal. Because of the peculiarity of LMD, this decomposition method has attracted many scholars' attention in recent years. In [12], the LMD method was used to decompose the vibration signals of a rolling bearing, and the results were able to provide the basis for the fault diagnosis. In [13], a fault diagnosis method based on LMD and multiscale fuzzy entropy was proposed, in order to fulfill the fault pattern identifications for a rolling bearing. [PERSON] et al. [14] proposed a fault diagnosis method for a gearbox, based on the local mean decomposition, permutation entropy, and an extreme learning machine. The research results indicate that the LMD method possesses a high suitability for analyzing non-stationary signals, which can be employed to perform the preprocessing of shearer cutting vibration signals in this paper. Naturally, after signal reprocessing, feature extraction should be fulfilled in the pattern recognition system of shearer cutting status. Time-frequency statistical parameters can describe the waveform distribution characteristics of vibration signals, in the time and frequency domain [15; 16]. Thus, the time and frequency domain feature parameters are comprehensively utilized to acquire more feature information in this paper. However, not all of the features are closely related to each kind of cutting status, and too many inputs for a classifier will consume more classification time and reduce the accuracy. Hence, in order to avoid the dimension disaster, many traditional feature selection algorithms have been proposed, such as the Fisher score (FS) [17], Laplacian score (LS) [18; 19], Variant score (VS) [20], and so on. Some heuristic feature selection methods, such as the genetic algorithm, particle swarm optimization, and chaotic simulated annealing methods, have also been mentioned and applied in much of the literature [21; 22; 23].Nevertheless, these methods have their own defects and limitations. In this paper, an improved Laplacian score, based on FS, was presented, in order to select appropriate features as the new feature vectors.