Shearer Fault Diagnosis And Fault Prediction

The safety of the coal mine production is the foundation and guarantee for the sustainable economic development of coal resources. With the frequent occurrence of coal mine accidents, how to improve the reliability and safety of coal mining machinery and equipment has become an urgent need to address the issue. Shearer as the main equipment of large coal mines mechanized coal mining operations, due to its long-term work in the harsh environment, the probability of failure is quite high, so the research for effective fault diagnosis and fault forecast method has great practical value and theoretical significance.(1)This article on the basis of in-depth research at home and abroad for shearer fault diagnosis and prediction methods, comparative analysis of the shortcomings of neural networks, expert systems and other methods for Shearer Fault diagnosis and prediction, the fuzzy neural network with expert system combining hybrid intelligent diagnosis method is studied, and the algorithm will be used in fault monitoring and diagnosis of the shearer. The algorithm can predict and diagnose quickly and accurately for Shearer failure.(2)Through the relational database model,we establish knowledge base for shearer fault diagnosis and failure prediction,in order to facilitate data calls.(3)we use the recursive synthesis BP neural network to study the shearer hydraulic traction device system, compared to the conventional BP neural network, the recursive synthesis BP neural network addition the connection weights between the input and output layer.Simulation results show that the recursive synthesis BP network can quickly and accurately predict failures on the shearer.(4)In this paper, based on the fuzzy BP neural network improved algorithm,it will fuzzy pretreat the fault data of Shearer,and expert system will classify the shearer fault, they will provide decision support to policymakers for Shearer fault prediction and diagnosis. The experimental results show that this method can improve the efficiency and accuracy of Shearer fault prediction and diagnosis,and it provide reliable basis for the Shearer fault prediction and diagnosis...