| With the transition of coal industry from labor-intensive to technology-intensive, the “less humanizing” or “unmanned” of fully mechanized coal face is becoming the key to achieve safety and high efficiency production. In a fully mechanized coal face, shearer, hydraulic support and scraper conveyor need to work together, and as the core mechanical and electrical equipment, the automation degree of shearer directly affects the safety production of fully mechanized coal face. Therefore, it is necessary to study on the intelligent control technology of shearer to mprove the automation level of coal production process, reduce the operating personnel, and provide the technical support for the safe and effective production of fully mechanized coal face.In this paper, the key technologies research and system development of shearer intelligent control were taken as the goals, and the shearer intelligent control methods were studied based on the cutting path tracking and working status evaluation. The stable and reliable working of shearer could be achieved through the main control means of traction speed regulation and the secondary control means of drum cutting height adjustment. Furthermore, the key technologies of this control system such as cutting path tracking, working status evaluation, traction speed regulation and intelligent coordinated control were deeply researched, and the main results could be expounded as follows:(1) By analyzing the basic structure and working mechanism of shearer, this paper put forward the control scheme, control flow and the overall structure of shearer intelligent control system with coupling the functional requirement and technical elements.(2) The shearer cutting path tracking method was proposed based on the coal seam distribution boundary tendency. The feature point set was established by the conventional points, key points and abnormal points to describe the boundary curve of coal seam. Then, the prediction algorithm for coal seam distribution boundary was presented through the fusion of improved D-S evidence theory and multiple neural networks, and the concrete implementation steps and flowchart of the algorithm was provided. Furthermore, three interpolation algorithms were used to fit and optimize the feature points and the cutting path generation method with better performance was obtained.(3) The evaluation system and model of shearer working status were established, and four kinds of identified patterns were determined, including normal, transition, abnormal and danger. The data preprocessing methods were provided based on attribute reduction and value reduction. The pattern recognition algorithm was proposed through the integration of roughs set theory and BP neural network. Ulteriorly, the expert knowledge base of shearer abnormal working state was constructed based on the fuzzy logic theoy to provide reliable reference basis for the intelligent contro of shearer.(4) The adjustment method of shearer traction speed was proposed through the combination of T-S cloud inference network and particle swarm optimization algorithm to realize the real-time control for traction speed. Then, the dynamic coordinated control thought was introduced based on the stable and reliable working of shearer and combined control of traction speed and drum height. The prioritization criterion of control instruction was presented to design the shearer intelligent coordinated control method and process.In this study, the shearer intelligent control methods were verified through the laboratory experiment and industrial test. The results showed that the shearer cutting path tracking error based on the proposed method was less than 0.05 m. The proposed evaluation system of shearer working status could accurately identify the real-time working mode. The expert knowledge base could accurately judge the abnormal reason, which provided the basis for the dynamic coordinated control of traction speed and drum height to realize the intelligent control of shearer. |
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