Study On The Theory And Method Of Coal-Rock Interface Recognition Based On Multi-sensor Data Fusion Technique

In this thesis, the theory and method of coal-rock interface recognition(CIR) based on multi-sensor data fusion technique are investigated systematically.CIR can make the shearer have the ability to automatically trace the coal-rock interface. It cannot only contribute to mine automation and high efficiency, but also reduce the content of rock and the other mineral that must be removed in the process of coal beneficiation. It is one of the key techniques of mining automation. The present methods collect information by the single sensor only. Because every single sensor has its own special certain precision and application range, collecting information by the single sensor is limited to a certain area and mistakes identification is sometimes unavoidable. Researching and developing an identification system uses multi-sensor is a tendency in order to provide design base for the height adjustment system. Due to the above consideration, the paper has proposed a totally new coal-rock interface recognition method based on multi-sensor data fusion technique with multi-sensor collecting response signal of cutting force. The method avoids the problem of signal transmission, has no limits of zymurgy condition and mining process, can enhance precision and accuracy greatly with multi-signal data fusion technique.The principle of shearer and the force of drum are analyzed. It is concluded: during the period of cutting coal and rock, drum resistance torque, radius acting force and responses of structure straight-line vibrating and drum twist vibrating vary with the variation of shearing media. These variations contain the information of shearing media, monitoring these data changes, with suitable signals process and multi-sensor data fusion(multi physical effects analysis and comprehensive), then coal rock interface recognition canbe realized. Through analyzing, oil pressure signal, arm vibration signal, motor cutting current signal, torsional moment and torsional vibration signals of drum shaft are collected as the information resources of multi-sensor.CIR testing system is established, and the model shearer and model coal wall are designed and manufactured. The control system is manufactured, and test scheme is decided. Through the test, volumes of testing data that are taken as information resource have collected.Feature extraction is carried out with wavelet packet technique because of its advantages. The sensitive frequency bands of signals are judged and their features can be extracted by the energy method. Feature extraction with wavelet packet technique finishes the conversion of pattern space to extraction space, filtrates the factors that cannot reveal sample essentials. It compresses greatly feature dimensionality, acquires the characteristic variables which can reveal sample attributive. It provides correct and reliable feature level data for data fusion.Data fusion, as a cross synthetic information process technique, presents power predominance of carrying out coal-rock interface recognition. The paper probes the advantage of information fusion with the fuzzy neural network(FNN) technique, constructs the two-level FNN. For the CIR problem, the structure and training algorithm of FNN are decided, the input and output of FNN are fuzzed and the algorithm is modified. The results show that the test data can be used to train and simulate with the neural network, FNN, two-level FNN. System adopted FNN technique with the two-level structure can carry out the CIR and the system adopted the modified algorithm has the higher identification ratio which is more than 90%. The method of CIR based on multi-sensor data fusion technique is feasible and more reliable.