Abstract
This paper proposes an intelligent recognition method for shearer cutting state based on deep learning theory, to solve the problems where the picks are prone to various failure forms during the cutting of coal and rock masses by the shearer. The failure will lead to the decline on the stability of the entire machine of the shearer and affect the safety production. Specially, a 1:1 simulation bench is used for simulating underground mining conditions to measure and collect the cutting loads of picks and establish a sample database. Deep learning-based intelligent recognition method is an effective tool that can break away from the dependency of prior knowledge and recognition experience, and sparse. In this paper, a promising deep learning method called sparse filtering is proposed for intelligent recognition of shearer cutting. So sparse filtering is applied to construct an automatic feature extraction model, and softmax regression is adopted as a classifier for cutting pick state recognition. Furthermore, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> regularization term is added to the cost function of sparse filtering to prevent the problem of excessive model training and weights. The proposed method for identifying the cutting status of the shearer can effectively monitor the cutting status of the picker, thereby improving the safety and stability of the cutting of the shearer and promote the coal mining efficiency.
Highlights
Shearer is the main mechanical equipment used for the underground mining of coal resources, and pick is the direct contact part of the shearer to cut coals and rocks [1,2,3]
(4) State recognition: All the learned features are combined with the label data to train the softmax, and the other samples are adopted as test samples
We investigate the selection of the input dimension of Sparse filtering (SF)
Summary
Shearer is the main mechanical equipment used for the underground mining of coal resources, and pick is the direct contact part of the shearer to cut coals and rocks [1,2,3]. K. Zhang et al.: An Unsupervised Intelligent Method for cutting pick state recognition timely find and replace the failure cutting teeth, and improve the working efficiency and service life of shearer cutting head. Zhang et al [11] proposed a method for identifying the failure mode of the pick alloy head based on back propagation neural network to realize the monitoring and recognition of the failure mode of the pick alloy head during the cutting process of the shearer. Wang et al [19] proposed an artificial immune algorithm based dynamic health evaluation method to accurately identify the dynamic health states of shearer, so as to reduce the operation troubles and production accidents, and improve the coal mining efficiency.
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