Study Of Test System Of Performance Parameters Based On Virtual Instrument

With the development of the Machinery, The machinery fault diagnosis technology becomes more and more important. This article has mainly studied the structure of Wavelet Neural Network (WNN) and we apply it to the field of the machinery fault diagnosis. The feasibility that WNN was used for fault diagnosis was verified by a test of bearing.The optimal wavelet bases that are used for wavelet denoise and feature extraction is decided by studying different wavelet bases. Given that there exit some improved BP algorithms, The Resilient back propagation algorithm is finally proposed by accurate experiment data.This paper adopts LabVIEW as the development flat, achieving the function of signal simulation, signal analysis and fault diagnosis. In the module of fault diagnosis, the design of wavelet denoise, feature extraction and neural network by the methods of LabVIEW and MATLAB mixed programming.