Memory cutting of adjacent coal seams based on a hidden Markov model

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.