Research On Adaptive Time&Frequency Domain Analysis Method In Early Fault Diagnosis Of Rolling Bearings

Rolling bearing can provide stable support for rotating structure,and it is widely used in mechanical equipment.However,it is also one of the most easily damaged parts.From the initial slight damage to the abnormal operation of rolling bearings with the vibration increasing,it will affect the normal operation of other components in mechanical equipment.The whole equipment will produce a series of faults,resulting in serious economic losses.Therefore,it is important to diagnosis timely when rolling bearing has slight damage.This is good for the normal operation of the whole equipment.In this paper,the fault mechanism of rolling bearing and the signal processing method of early fault is studied deeply.The purpose of fault diagnosis is achieved by exploring appropriate processing methods.(1)Through the research on the early fault mechanism of rolling bearing,the key point is to extract the periodic shock signal produced by the damaged bearing during operation.The expressions of periodic shock signal in time domain,frequency domain and envelope spectrum are mastered to support the successful extraction of vibration signal.(2)In order to solve the problem that early failure of rolling bearings information are difficult to identify,a new method of rolling bearing fault diagnosis based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and 1.5 dimension spectrum is proposed.Firstly,the CEEMDAN method is used to decompose the vibration signal,a signal of a finite number of intrinsic mode component(IMF)is obtained.Then,according to the relative kurtosis criterion of each component,an IMF component containing important fault information is extracted.Finally,the extracted IMF component is analyzed in 1.5 dimension spectrum.Fault type of bearing can be determined by analyzing the prominent components in 1.5 dimension spectrum.The method is validated by simulation signals and early fault tests of inner and outer rings of rolling bearings.The results show that the combination of the two methods has great advantages in early fault diagnosis of rolling bearings.(3)In order to solve the problem that the early faults of rolling bearing is very difficult to extract the fault features using the fast spectral kurtosis in the strong noise background.A new method is proposed to find the optimal frequency band according to the fault characteristic frequency intensity coefficient.And the signal is decomposed by Butterworth filter to obtain the fault characteristic.Firstly,the center frequency and bandwidth of the band-pass filter are set.Then the optimal frequency band is found according to the maximum principle of characteristic frequency intensity coefficient.Finally,the bearing fault type is determined by analyzing the envelope spectrum of the optimal frequency band.The method is validated by simulating the early fault tests of inner and outer rings of rolling bearings.The results show that the method can realize the early fault diagnosis of rolling bearings when the fast spectral kurtosis method is invalid.(4)The early fault feature information of rolling bearing is very weak and difficult to extract,and the selection of parameters of the TQWT depend on the user’s experience.The method of early fault diagnosis of rolling bearing based on improved TQWT is proposed to solve the above problems.Firstly,the range of Q-factor was preset.The several components are obtained by decomposing the fault bearing acceleration vibration signal using TQWT.Then,the each component is demodulated using the envelope derivative energy operator.The best decomposition parameters are selected adaptively according to the index of characteristic frequency strength factor in the energy spectrum.The optimal decomposition results of the fault signal can be obtained.Finally,the fault feature information can be accurately extracted by analyzing the envelope derivative energy spectrum of the optimal component.The method is verified by analyzing simulation signal,experiment data and engineering case.The results show that the method can effectively extract the early weak fault characteristics of rolling bearing and accurately judge the type of fault.It has a certain value of engineering application.This paper focuses on the early fault diagnosis methods of rolling bearings,including three adaptive signal processing methods: CEEMDAN and 1.5-dimensional spectrum,improved fast spectral kurtosis and improved TQWT.The validity of the method is verified by theoretical simulation,laboratory experiments and field experiments.The research results have a certain guiding role in the field diagnosis of rolling bearing faults.