Research On Fault Diagnosis Of Shearer Hydraulic System Based On Data Fusion

As one of the important component in shearer, hydraulic system has the function of controlling height and brake. Shearer electromechanical integration equipment is higher with the constant improvement of the automation level. At the same time, shearer hydraulic system failure frequency is also increasing. Therefore, the fault diagnosis technology is applied to shearer hydraulic system efficiently and effectively, not only can enhance productivity, but also prevent safety accidents.In this thesis, the significance of shearer hydraulic system fault diagnosis is introduced,fault diagnosis method based on data fusion is proposed on the basis of broad understanding of the shearer hydraulic system research status, development trend and common fault diagnosis method.The basic mechanical principle of shearer is introduced. The failure mechanism is analyzed from hydraulic pumps, motors and brake pump station from shearer hydraulic system. The fault of causes and results are elaborated.Afterwards, the concept of data fusion is introduced simply. The fault diagnosis methods which including neural networks and D-S evidence theory are proposed respectively. Through example, the former is easy to fall into local minimum, more than that, the initial weights and threshold value exist a greater randomness. It can reduce the accuracy of fault diagnosis, so the former needs to be further optimized through the genetic algorithm.Eventually, a secondary fault diagnosis model of shearer hydraulic system is established.The fault is diagnosed locally in characteristic class. The basic probability distribution function of the test sample is calculated through to the calculation related output. It is fused by D-S evidence theory, getting a more intuitive diagnosis. The validity of the method is confirmed.