Diagnosis Of Rolling Bearing Failure Based On Wavelet Theory And Cyclic Statistics Analysis

Rolling is the most commonly used in rotating equipment and key components, Its workingcondition has influence on working safety in industry directly. So it is very significant toresearch on bearing condition monitoring and fault diagnosis techniques, especially for bearingearly fault.By the symmetry of the structure and operation mode of rotation and reciprocating, So thatwhen the injury occurs due to the impact of modulation phenomena, resulting in the vibrationcharacteristics of cyclostationary signals presented to in-depth study of the circulation statisticsbased on the analysis of rolling bearing vibration signal feature extraction method. Followingmajor elements:1) Detailed discussion of the theory of circulation statistics, and their places demodulationperformance. Demodulation performance by comparing the Hilbert transform, envelopedemodulation and CAF, show that the CAF for the rolling bearing vibration signals obtainedwith a better analysis of results, and by calculating the cycle frequency equal to the faultcharacteristic frequency of the CAF, to simplify calculations. Its modulus, FFT transform toextract feature information.2) Early damage of rotating machinery can cause a very weak impact modulation,information is often submerged in strong noise, which, the application of wavelet theory toenhance the information on the early weak, that is, to strengthen the early signals of weakcyclostationary properties, but also to continue spectral analysis of the signal cycle to dosufficient time to prepare.3) Finally, several experimental studies, by different types of bearing failure, respectively,made a power spectrum, envelope spectrum and the cyclic spectrum analysis, comparativeanalysis, theoretical research to verify the correctness and validity.