To solve the problem of large interference of shearer rocker arm vibration signal and difficulty in feature selection, and recognize accurately the cutting status of shearer, a novel pattern identification method based on Symmetrized Dot Pattern (SDP), Local Mean Decomposition (LMD) and Multi-Scale Convolution Kernel Deep Convolutional Neural Network (MCK-DCNN) is presented. Firstly, the vibration signal of shearer rocker arm is decomposed by LMD to get multiple product functions (PFs). The previous three PFs are transformed into SDP images with different features by SDP method, which are input into MCK-DCNN model to automatically extract features and identify shearer cutting status. The method can achieve the classification rate of 97.9%, which is superior to 1D_CNN and LeNet. The comparison result indicates that the method can provide technical support for improving the automatic coal cutting performance of the shearer.
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