Classification of Vibration in Coal Mining Industry via Deep Neural Network
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Abstract
Recently, the electricity from coal mining industry is still necessary because it serves as a primary electrical source in many areas in response to high demand in power. However, coal mining can harm the miners, environment, and villages near mining site due to ground vibration from blasts during the operation. Hence, every coal mine industry is required to report the ground vibration for safety purposes. Mostly, the ground vibration data comes from the vibration sensors deployed around the mining site, and the vibration data will be sent to the control room. Due to tons of the vibration data, operators have difficulty in classifying the blast vibrations from the records which causes time-consuming and possible human errors during process. To solve these problems, this article proposes the Deep Neural Network (DNN) model for a blast vibration classification with 3 hidden layers. the ground vibration data used in training and validating the DNN are collected by the Mae Moh mine site in Thailand. As the result, the designed DNN meets the standard with the accuracy of 100%.
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