Tacholess Order Analysis Of Rolling Bearing On The Basis Of FrFT Filter

Rolling bearings are the most common and basic parts in many kinds of rotating machinery,many of the defaults and problems of those machines may be caused by bearing defects.Studies show that machinery problems caused by bearing faults rate up to 30%.Therefore,fault detection and diagnosis of rolling bearing has been the subject of intensive research.Under the background of Industrial manufacture 4.0 and Internet +,requirements for monitoring and diagnosis of machinery have not be limited in accuracy and usefulness any more,requirements to efficiency and reliability also valued.Many methods of fault diagnosis have been developed,however,these methods are usually based on the assumption of stationary vibration signal.In reality,the rotating machines are more likely being operated under variable speed.Therefore,an effective method is needed to analyze vibration for bearing fault under run-up or run-down processes.This paper presents a novel method to diagnosis bearing fault under varied speed condition using tacholess order analysis based on Fractional Fourier Transform(FrFT)filter.Firstly,determine the optimal order of Fractional Fourier Transform based on the relation between Gabor transform and FrFT.Then,estimate the rotating speed of bearing based on the result of FrFT filter.After that,the re-sampling signal in anger domain can be obtained by re-sampling of vibration signal with the estimation speed.At last,detect the bearing fault by using order power spectrum and order cepstrum.The order power spectrum and order cepstrum analysis can be used to identify the bearing fault features accurately.This paper designed and developed a system including acquisition and analysis of the vibration signal based on Lab VIEW and MATLAB.In order to meet the requirement of remote monitoring,developing a mobile terminal at Android mobile phone based on Data Dashboard.Experimental examples are conducted to evaluate the stability of this system and the effectiveness of the proposed approach.The results show that the system is stable operation and the proposed approach is an effective way to diagnosis the bearing fault.