Ore Transport Trucks Hydraulic System Condition Monitoring And Fault Diagnosis System

With the fast development of industrial science and technology, all kinds of equipments are developing continuously, which is not only on equipments'function but also on their automation level. So, nowadays, the productive equipments of industry are developing towards high automatic integration level.Mining truck which acts as the important productive equipment plays a key function in mining industry, especially in underground mineral transportation. Before 1980s, the mining trucks used in china nearly depended on import, but since 1980s, many Chinese companies have begun to develop mining truck, and big progress has been made through a few years'effort. However, the automation level of their mining trucks is still very low, especially on their fault diagnosis ability. Now, the mining trucks made in domestic nearly depend on technician's experiences to diagnose faults, which is not only in contrary with the development of industrial equipments but also not beneficial for the enhancement of productive efficiency of equipments. Therefore, according to the design level and application condition of domestic mining trucks, it is necessary to improve their fault self-diagnosis ability. Hydraulic system is the main part of mining truck, and its function directly influence the function of the whole mining truck, moreover, whether hydraulic system has a self-diagnosis ability will influence productive efficiency of equipment, hence, it is necessary to improve hydraulic system's fault intelligent diagnosis ability.In this paper, one fault diagnosis method based on fuzzy neural network for hydraulic system of mining truck has been given combining with the application level, character of mining truck's hydraulic system structure, fault diagnosis methods in common use in hydraulic system and so on. Through comparing the fault diagnosis result utilizing fuzzy neural network with the fault diagnosis result utilizing logic, it is proved that the result adopting fuzzy neural network to hydraulic system fault diagnosis is more accurate, valid and reliable. Moreover, this paper also presents detailedly the analysis methods of acquisition data and design method of hardware and soft ware of the whole state monitoring and fault diagnosis system.