Intelligent Fault Diagnosis Of Coal Mine Ventilator

With the increasingly complex of mechanical equipment, the reliability and safety and maintenance problems of daily production have aroused people's attention. In order to solve this problem,build timely and accurate positioning and diagnosis fault diagnosis system is the key. Coal mine ventilator is important for enterprise's ventilation equipment, its fault is one of the main reasons led to the gas explosion. Therefore, it's a great significance to implementation of fault detection and diagnosis for mine ventilator. Based on rough sets and artificial neural network theory, focus on the fault feature reduction and the BP neural network fault diagnosis in the application of fan , proposed a scheme of intelligent diagnosis of the mine ventilator based on the rough set - neural network, in order to realize common mechanical fault diagnosis effectively.This article try several aspects exploratory as follows:(1) Based on the advantages and disadvantages of rough sets and neural network ,choose mine fan as research object for this fault diagnosis model. Discussed the discretization and reduction problem, illustrates its related problems such as classification ability and decision accuracy .(2) Study and analyzed the structures and algorithms of the BP neural network theory and Integrated neural network. Achieves the training and simulation using the neural network toolbox of MATLAB .(3) In this topic, selected the dataset UCI sample as fan fault sample, used the rough sets reductied the sample, and combined with BP neural network finished the fault diagnosis of mine fan.(4) Compared the results between the two methods, which the neural network diagnosis and rough set neural network diagnosis, an example show the diagnosis model is feasible for Large sample data.Finally, It is proved that neural network using rough sets is feasibility and advantage of fault diagnosis.