粗糙集神經(jīng)網(wǎng)絡(luò)在注水泵機組故障診斷中的研究
[Abstract]:At present, computer intelligence has been widely used in fault diagnosis. Rough set theory is a mathematical tool for studying imprecise knowledge and incomplete data representation, learning and induction, which is proposed by Z.Pawlak, a Polish professor. Artificial neural network (Ann) is a nonlinear dynamic system that simulates human thinking. It has the ability of parallel cooperative processing and learning. It can realize the functions of recognition and classification, optimization calculation, associative memory, knowledge processing and so on. The vibration data of the water injection pump unit contain a lot of information about the working state of the water injection pump unit, and it is an important data for the fault diagnosis of the water injection pump unit. In this paper, the ability of attribute reduction, knowledge processing and neural network for learning, classification and parallel processing of rough set is used to establish rough set neural network to complete fault diagnosis of water injection pump unit. In this paper, the research results of rough set and neural network are combined. Firstly, rough set theory is used to effectively reduce the dimension of the sample feature, then the network is constructed by using the reduced sample to reduce the learning and running time of the neural network. Finally, the fault diagnosis system of water injection pump unit is built by MATLAB software. The results of network diagnosis show that the simulation results are in agreement with the actual situation for the sample knowledge that has been learned, which shows that the network can correctly diagnose the faults. The research of rough set neural network in fault diagnosis of water injection pump unit has certain theoretical significance and practical value.
【學(xué)位授予單位】:西安石油大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2012
【分類號】:TH165.3;TP183
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