A Mining Truck Hydraulic System Of Intelligent Fault Diagnosis

Fuzzy neural network is an important research problem in the domain of intelligent fault diagnosis. FNN is composed of the neural network and fuzzy logic system. It is the organic integration of two parts. FNN can deal with the abstract information, such as the language information. It is good at self-learning and self-tuning. This thesis chiefly studies the fault diagnosis of the hydraulic systems in engineering equipment, comparing the intelligent fault diagnosis in existence as well. At last, the fuzzy neural network was selected to the equipment diagnosis. Based upon the description of the principles of the fuzzy theory and neural networks, this paper analyzes the defects and merits of both technology and stated the importance of its integration. On the theory of fuzzy BP network, this model compared a fuzzy reasoning method which can realize information translating knowledge through distilling,prioritization and fast selection of fuzzy rules. This model also takes an important role in both selecting knowledge selection and improving diagnosis validity. Through transferring of FNN weights into diagnosis comparing operator based on case-reasoning. Finally, the model study was applied to the hydraulic systems in mining truck, which helped to establish a fault diagnosis system in the goal of keeping the smooth operation of the equipments.