Extracted Fault Feature Of Rolling Element Bearing Based On Separated Vibration Of Gearbox

As a result of high transmission power,stable and reliable running etc,gearbox transmission are commonly used in all kinds of transmission device.Gear and rolling element bearing is the key components in the gearbox,but it is also one of the easiest components of failure,and it is also one of the key points in the field of fault diagnosis;Therefore,the effective fault monitoring of the gearbox possess high practical application value.Researches shows that the accessible technology has been relatively mature for single fault monitoring in the gearbox.For example,synchronous average can achieve gear fault monitoring and feature extraction,and amplitude demodulation analysis can effectively achieve fault detection and feature extraction about rolling element bearing.For the multi-fault in the gearbox,although it can achieve multi-fault feature extraction based on multi-sensor ICA method,the application of multi-sensor due to constraints such as installation.In order to improve the efficiency fault diagnosis of rolling element bearing and improving value of signal separation technology in the practical application,the self-adaptive noise cancellation(Discrete random separation)are researched in this study.Taking the parallel shaft gearbox and the planetary gearbox as the research object.The gear signal and the rolling element bearing signal is separated in the gearbox,and the fault feature extraction of the rolling element bearing is realized after separating.When the study's object is parallel shaft gearbox,the steps as follows will be carried out,first of all,using the order tracking technique to eliminate the influence of speed fluctuation of gearbox in the actual operation of the process;and then using the Fast-Kurtogram algorithm to determine the optimal resonance amplitude demodulation analysis with parameters;and then using the self-adaptive noise cancellation technology to separate the gear signal and the bearing signal.Finally,the spectrum analysis achieve which kinds of fault feature extraction respectively.The test results show that this technology can effectively improve the extraction of weakly fault feature in the gearbox,efficiency improve fault diagnosis of gearbox.This studies research the application of signal separation technology in planetary gearbox.Compared with parallel shaft gearbox,the planetary gearbox structure and operation modes is so different.So,implementation fault monitoring of the planetary gearbox is so difficult,especially extracted the planet bearing fault feature is most onerous.The study research self-adaptive noise cancellation technology in the application of fault diagnosis of planet bearing in detailed,and test result found out that the self-adaptive noise cancellation(Discrete random separation)in fault feature extraction of planet bearing of the effectiveness and feasibility.