Detection Of Falling Vibration Signal Of Magnetic Suspension Rotor Based On Hilbert-Huang Transform

Nowadays,among all kinds of rotating machinery equipment,rolling bearing is a common and vulnerable part.It will affect production practice,so it is necessary to monitor and diagnose it in real time.Fault feature extraction is the key to fault diagnosis and modern signal processing plays an important role in fault diagnosis.Fourier transform is usually used in the process of non-stationary signal processing.This method has some limitations.Therefore,a new transformation method is proposed.The Hilbert-Huang transformation(Hilbert-Huang Transform-HHT)was established by the famous American scholar N.E.Huang in the 1990 s.This kind of transformation mainly involves the empirical mode decomposition of the signal to obtain the corresponding instantaneous frequency signal,which can obtain a good effect of transformation.This paper discusses the theoretical basis of the transformation,and discusses its features and application.Hilbert spectrum,edge spectrum and time-frequency analysis are introduced.Transient frequency,time scale parameters and inherent modal functions are analyzed.The application limitation of this kind of transformation is briefly analyzed.The method of Hilbert Huang transformation in fault feature extraction of rolling bearing is studied.In the fault diagnosis method,the fault and normal vibration signals are analyzed by time and frequency,and the HHT method is applied to the drop impact.On the basis of the theoretical analysis of this transformation,this paper briefly discusses LabVIEW software,establishes the HHT signal analysis module based on this software,and then selects the vibration signal as the object to test the signal processing function of this system.After the system development,the htr-10 electromagnetic bearing drop test bench was selected to establish a test unit to test the bearing fault signal,and then the results were compared with the results of the signal analysis system,and it was found that the results were basically consistent.The results obtained in this paper enrich the content of signal processing and fault diagnosis.