Research On The Key Technologies Of Cooperative Control For Equipments In The Fully Mechanized Coal Face

At present, the fully mechanized mining equipment is given priority to artificialfield operation in coal mine. Since the production environment of fully mechanizedmining face is harsh and changing, artificial field operation is time-consuming,laborious and inefficient. Not only that, physical condition and mental state of theoperator will also affect the control accuracy, reliability and safety of mechanical andelectrical equipment. It is easy to cause casualties in the event of accidents. Therefore,it is required to study the cooperative control theory and method for fully mechanizedmining equipment, improve the automation level of the production process, reduce thefield operators and realize safe and efficient production of fully mechanized miningface.Based on shearer, hydraulic supports and scraper conveyor as the research objectin the fully mechanized coal face, this paper studied cooperative control mechanismbetween shearer, hydraulic supports and scraper conveyor in depth.Cooperative control method for shearer, hydraulic supports and scraper conveyorproposed through the combination of working mechanism analysis, model building,and industrial experiment which laid the theoretical foundation for the realization ofautomation and fewer people in fully mechanized mining face. The main researchcontent and achievement included:(1) This paper analyzed the structure and functions of shearer, hydraulic supportand scraper conveyor in depth and built physical sensing system for shearer, hydraulicsupports and scraper conveyor. Gestures cooperative control model and performancecooperative control model are built for the fully mechanized mining equipments basedon the workflow.(2) Considering that the production environment for fully mechanized miningequipment is complexly changing and sensor signal is susceptible to interference, thispaper analyzed deficiencies of the common methods for eliminating sensor signalnoise, designed an energy and correlation method based on wavelet packet transformtheory and implemented noise elimination of sensor signal which contained compositenoise. Based on the sensor signal after eliminating noise, this paper proposed amulti-sensor information fusion method based on fuzzy logic and probabilistic neuralnetwork, established state space of fully mechanized mining equipment andimplemented the correct judgment for working status of fully mechanized mining equipment.(3) Using the measuring characteristics from different scales of the infrareddevice and rotary encoder, this paper achieved accurate position of shearer byintroducing dynamic adjustment factor. This paper proposed a fuzzy control theorybased on the variable of environment condition to improve the veracity of theshearer’s remember cutting path. A smooth control method of shearer’s drum forcutting path based on the double-coordinator system to ensure the smoothness of thecutting path. Using non-contact hall-circuit to achieve alignment signals of hydraulicsupports, this paper implemented automatic alignment of hydraulic supports. Thispaper also analyzed the relationship between running speed of shearer and operatingfrequency of hydraulic supports and implemented adaptive attitude control forhydraulic supports based on position and speed of shearer; analyzed cooperativerelationship between shearer, hydraulic supports and scraper conveyor andimplemented adaptive deflection control for scraper conveyor based on position andspeed of shearer.(4) On basis of building a roll prediction model based on improved Elman neuralnetwork, this paper implemented state prediction for fully mechanized miningequipment.A kind of control method of collaborative multi-motor drive based onimmune algorithm is proposed to achieve uniform output of multi-motor drive processof shearer and scraper conveyor. This paper built an adaptive control model forpressure output based on dynamic prediction of hydraulic pressure from statistics ofmovement of hydraulic supports and made hydraulic supports gained relatively stabledynamic pressure. By analyzing coupling relationship between the main parametersduring running process of fully mechanized mining equipments, taking stateprediction of improved Elman neural network and expert knowledge as the basis andproduction/consumption ratio as evaluation index, this paper proposed a kind ofcooperative control method of global multi-level optimization for shearer, hydraulicsupports and scraper conveyor.From December,2012to July,2013, the industrial experiment of cooperativecontrol method for fully mechanized mining equipments studied in this paper wasperformed in NO.22210fully mechanized mining face at NO.6coal mine ofPingdingshan Coal co., LTD and NO.2115fully mechanized mining face atChangcun coal mine of Yima Coal Industry Group separately. The experiment resultindicated that the accuracy error of cutting path for shearer drum is about5cm and the fluctuations mean of adjacent sampling points of cutting path is smaller than2cm. Themethod can meet the accuracy and smoothness requirements of cutting path. Thegestures of hydraulic supports and scraper conveyor can be adjusted according toposition and speed of shearer. The improved Elman neural network can preciselypredict running state of fully mechanized mining equipment. The cooperative controlproduct mode improves the automation level of fully mechanized coal face comparedwith the manual operation mode greatly. The reliability and stability of fullymechanized equipments have significantly increased, the working conditions of coalminers are improved and the production/consumption ratio index increased116.1%.