A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural NetworkReport as inadecuate




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School of Mechatronic Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China

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School of Information and Electrical Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China





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Author to whom correspondence should be addressed.



Academic Editor: Vittorio M. N. Passaro

Abstract In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition IEEMD and Probabilistic Neural Network PNN is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function IMF components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method. View Full-Text

Keywords: cutting pattern recognition; coal mining; sound signal; Improved Ensemble Empirical Mode Decomposition; intrinsic mode function; Probabilistic Neural Network cutting pattern recognition; coal mining; sound signal; Improved Ensemble Empirical Mode Decomposition; intrinsic mode function; Probabilistic Neural Network





Author: Jing Xu 1, Zhongbin Wang 1,* , Chao Tan 1, Lei Si 1,2 and Xinhua Liu 1

Source: http://mdpi.com/



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