Abstract
This study employs the mechanical vibration and acoustic waves of a hydraulic support tail beam for an accurate and fast coal-rock recognition. The study proposes a diagnosis method based on bimodal deep learning and Hilbert-Huang transform. The bimodal deep neural networks (DNN) adopt bimodal learning and transfer learning. The bimodal learning method attempts to learn joint representation by considering acceleration and sound pressure modalities, which both contribute to coal-rock recognition. The transfer learning method solves the problem regarding DNN, in which a large number of labeled training samples are necessary to optimize the parameters while the labeled training sample is limited. A suitable installation location for sensors is determined in recognizing coal-rock. The extraction features of acceleration and sound pressure signals are combined and effective combination features are selected. Bimodal DNN consists of two deep belief networks (DBN), each DBN model is trained with related samples, and the parameters of the pretrained DBNs are transferred to the final recognition model. Then the parameters of the proposed model are continuously optimized by pretraining and fine-tuning. Finally, the comparison of experimental results demonstrates the superiority of the proposed method in terms of recognition accuracy.
Highlights
Coal is an important source of energy, accounting for approximately 29.21% of primary energy consumption of the world in 2015 according to BP Statistical Review of World Energy (June 2016)
restricted Boltzmann machine (RBM) was proposed by Smolensky in 1986 [32]; it is a probabilistic graphical model that can be explained by stochastic neural network
In particular after Hinton et al proposed deep belief networks (DBN) based on RBM as a basic component [33], RBM has been successfully implemented in dimensionality reduction [34], feature learning [35], and classification [36]
Summary
Coal is an important source of energy, accounting for approximately 29.21% of primary energy consumption of the world in 2015 according to BP Statistical Review of World Energy (June 2016). Infrared detection uses a cutting machine to produce different temperatures when it cuts to coal and rock and determine whether the cut is coal or rock This method has a quick response and is ideal for real-time application. With bimodal learning and transfer learning processes, the proposed method has high recognition accuracy and has the advantages of low cost, extensive adaptability, insensitivity to the environment, and simplified technical difficulty. Another important step in the recognition is extracting effective features from measured signals.
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