Wavelet Analysis In Fault Diagnosis Of Rotating Machinery Application Research

Wavelet analysis is a theory developing rapidly in recent years. It has been increasingly used in image processing, data compression, speech recognition, signal to noise separation, fractal and other fields. In this thesis, wavelet analysis theory to the theory applied to machinery fault diagnosis, wavelet analysis through theoretical study and real data analysis of wavelet analysis in fault diagnosis in the field of application prospect is very broad.This paper studies the fault diagnosis technology status and development trend, introduces the traditional method and the rotating machinery fault diagnosis of several common faults, indicating the wavelet analysis in fault diagnosis of. Focuses on the wavelet analysis theory and application, introduced in the wavelet denoising, signal filtering and detection of mutations in the application, illustrates the fault diagnosis of machinery using wavelet analysis has obvious advantages and necessity. In this paper, collecting a factory turbine compressor of bearing vibration signal, the collected vibration signals using wavelet analysis. Through the use of wavelet multiresolution analysis and the nature of Mallat algorithm, using Matlab software, to extract a single frequency to determine the original signal is not fault signal, a method of judging fault signal. At the same time the idea of the wavelet analysis, design constructed a new rotating machinery fault diagnosis device, which is a wavelet theory based fault diagnosis system using the system extracts the fault signal is very clear, makes the mechanical fault diagnosis more accurate.Wavelet analysis in time-frequency localization has good properties, dealing with non-stable signals with very prominent advantages. The wavelet analysis theory is applied to the rotating machinery fault diagnosis is a worthy application of new technology, application is very wide.