Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders

This paper provides a novel rock-coal interface recognition method based on stacked sparse autoencoders (SSAE). Given their different size and hardness, coal and rock generate different tail beam vibrations. Therefore, the rock-coal interface in top coal caving can be identified using an acceleration sensor to measure such vibrations. The end of the hydraulic support beam is an ideal location for installing the sensor, as proven by many experiments. To improve recognition accuracy, the following steps are performed. First, ensemble empirical mode decomposition method (EEMD) is used to decompose the vibration signals of the tail beam into several intrinsic mode functions to complete feature extraction. Second, the features extracted are preprocessed as the inputs of SSAE. Third, a greedy, layer-wise approach is employed to pretrain the weights of the entire deep network. Finally, fine tuning is employed to search the global optima by simultaneously altering the parameters of all layers. Test results indicate that the average recognition accuracy of coal and rock is 98.79 % under ideal caving conditions. The superiority of the proposed method is verified by comparing its performance with those of four other algorithms

Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network

In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy