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

Sensors

Jing Xu¹  Zhongbin Wang¹  Chao Tan¹  Lei Si¹  Xinhua Liu¹ 

[1] School of Mechatronic Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China; E-Mails:

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

DOI  :  10.3390/s151127721

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.