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.
Highlights
Nowadays, cutting pattern recognition for shearers, which aims at determining whether the shearer is cutting coal or rock, plays an important role in increasing coal output and avoiding cutting hard rock in fully-mechanized coal mining working faces
Enlightened by the above knowledge, this paper aims to propose an online cutting pattern recognition method using the cutting sound to overcome the disadvantages of high volume, low efficiency and low reliability of traditional ways
According to the relevant literature, natural γ-ray detection, Wavelet Packet Transform (WPT) and Probabilistic Neural Network (PNN), traditional EEMD and PNN, and the improved method in [17] were selected as representative approaches to make a comparison to the Improved Ensemble Empirical Mode Decomposition (IEEMD) and PNN
Summary
Nowadays, cutting pattern recognition for shearers, which aims at determining whether the shearer is cutting coal or rock, plays an important role in increasing coal output and avoiding cutting hard rock in fully-mechanized coal mining working faces. Due to the poor working conditions during the production process, online cutting pattern recognition is always a tough technical problem [1]. The shearer operators judge whether it is cutting coal, the rock, or coal gripping gangue, generally through the integration of geological conditions and the shearer cutting sound [4]. The operators adjust the shearer according to their comprehensive judgment. The cutting sound signal has its unique advantages relative to traditional vibration and current signals, such as ease of installation and maintenance, non-contact measurement and convenience for online analysis
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