Abstract
This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.
Highlights
This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection
The decomposed Intrinsic Mode Functions (IMFs) components were reconstructed by the correlation coefficient and energy density to further highlight the power frequency characteristics
In order to enhance the accuracy and efficiency of coal rock hardness identification, the multi-scale permutation entropy of stator current was determined as the identification trait by analyzing the relationship between stator current and load
Summary
The variation in the load will influence the current value of the motor. The current is proportional to the load torque. Where Te and TL are the electromagnetic torque and output torque, respectively; J is the moment of inertia of the motor; pn is the pole number of the motor; ωm is the motor speed; Zi is the cutting resistance of the i-th pick in the working area, which is related to the rock hardness Pk ; ri is the cutting radius of the i-th pick in the working area; μ is the reduction ratio of the reducer; and η is the transmission efficiency of the reducer It can be seen from Equation (1) that TL and ωm are constant when the motor is stable. According to Equation (9), the periodic fluctuations in the load torque are reflected in the stator current as amplitude modulation and phase modulation. The fluctuation in the load torque impacts the current value, and the motor current signal in the frequency domain
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