Research And Application Of Automatic Acquisition Method Of Fault Feature Diagnosis Knowledge Based On Wavelet-SOFM

With the development of the mechanical fault intelligence diagnosis system, Knowledge Acquisition (KA) becomes more and more important. KA is one of the key problems in the domain of Artificial Intelligence (AI) and mechanical fault intelligence diagnosis system. At present, one common defect of intelligence fault diagnosis expert system is that the knowledge which included the system is limited and inaccurate. According these questions, taking the fault feature diagnosis knowledge acquisition as the core, the applications of fault feature knowledge acquisition based on Self-Organizing Feature Map (SOFM) neural network are researched. The main researches and contributions are fourfold:1 How the SOFM network works is researched and the application of SOFM in fault diagnosis knowledge acquisition is discussed. It is very difficult to be understood that the knowledge is hide in the network's weight. So the visualization of the SOFM training results is investigated, U-matrix method and a new visualizing technique is used.2 The theory of Wavelet Analysis is discussed. The Application of Wavelet Packet Transform (WPT) in signal decomposition and restructure is researched. According the theory, a method of energy feature extraction which can be used to reduce the dimensionality of nonlinear high dimensional data space and extract feature, is proposed.3 The modal of KA based on WPT and SOFM, which can be used to acquire knowledge automatically, is proposed. It is more suitable for fault feature knowledge acquisition from the fault signals than time domain feature combining with SOFM.4 The new model is applied to automatic knowledge acquisition of fault diagnosis of roll bearing. Its simulation results indicate that the method based on WPT and SOFM can acquire the knowledge of roll bearing fault classification accurately. Later, taking roll bearing as object, regarding MATLAB as developing instrument, combining with SOM Toolbox2.0, a roll bearing fault diagnosis system based on WPT and SOFM model is developed.Finally, the whole thesis is summarized, and some future research areas of condition monitoring and fault diagnosis technique based on SOFM are highlighted.