Memory cutting of adjacent coal seams based on a hidden Markov model
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ORIGINAL PAPER
Memory cutting of adjacent coal seams based on a hidden Markov model Wei Li & Chengming Luo & Hai Yang & Qigao Fan
Received: 29 May 2013 / Accepted: 26 September 2013 # Saudi Society for Geosciences 2013
Abstract Height adjustment of a shearer cutting drum is one of the key processes involved when the shearer swings its cutting drum up and down on a fully mechanized mining face. Direct sensors are used to recognize the coal–rock interface for adjusting the shearer cutting drum; however, these sensors exhibit poor reliability and accuracy. A traditional memory cutting method is applied to avoid the deficiencies of direct sensors, but this method results in large residual errors and frequent adjustments of the shearer cutting drum. This paper proposes a hidden Markov model (HMM) memory cutting method for the shearer. The height of the shearer cutting drum is modeled by describing the collaborative automation of the shearer, scraper conveyor, and hydraulic supports. After analyzing the principle of traditional memory cutting for the shearer cutting drum, HMM memory cutting is developed by employing data correlation of adjacent coal seams. Moreover, the effectiveness of HMM memory cutting is compared with traditional memory cutting. Results indicate that HMM memory cutting effectively predicts the accurate height of the shearer cutting drum and reduces its adjustment frequency. The HMM memory cutting method tracks the coal–rock interface efficiently and enables the shearer to cut more coal seams and less rocks. Keywords Shearer . Memory cutting . Hidden Markov model . Adjacent coal seam . Data correlation
Introduction Coal is one of the most important energy resources in the world (Benndorf 2013). An upsurge in coal output leads to W. Li : C. Luo (*) : H. Yang : Q. Fan School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China e-mail: [email protected]
irreconcilable challenges between coal productivity and coal safety. Less miners or unmanned mining is the key to achieve high productivity, high efficiency, and safe mining. Automated mining is regarded as the Holy Grail in the international mining industry (Ju and Xu 2013). Fully mechanized mining face is the core segment of automation mining. This form of mining is equipped with large machines, including the shearer, scraper conveyor, and hydraulic supports. Mining equipments of fully mechanized mining face undertake a complicated chain of coal mining activities, including coal breaking, coal charging, and roof supporting (Paul et al. 2012; Wang et al. 2012). In almost all cases, coal mining activities result in various accidents such as potential rock falls, presence of toxic gasses, the poor ventilation, and machinery failure (Singh et al. 2013; He et al. 2012). Many attempts on the automation, information, and integration of coal mine have been made to reduce mine disaster and ensure mine safety (Hosseini et al. 2013; Ashgari and Esfahani 2013). The cooperative movement among the shearer, scr
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