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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

Vibration-Based Signal Analysis for Shearer Cutting Status Recognition Based on Local Mean Decomposition and Fuzzy C-Means Clustering