Fault Feature Extraction Of Rolling Bearing Based On Chirplet Path Pursuit

Although rolling bearing is widely used in mechanical equipment,it is prone to failure because of the variable speed and harsh working environment.The whole mechanical system is directly related to the operation state of rolling bearing,so it is significant to study the condition monitoring and diagnosis technology.Resonance demodulation is the most effective method to obtain the fault information,however,when the speed changes,the frequency spectrum will be fuzzy.What is more,the resonance frequency band of the bearing is difficult to obtain,when the bearing signal is in the strong background noise,such as the gear noise.Therefore,it is necessary to study the fault diagnosis technology.In this paper,the method of extracting the fault characteristics of rolling bearing under the condition of variable speed and gear noise is studied:The instantaneous frequency of bearing vibration signal under gear noise is complex and difficult to extract,but instantaneous frequency is an important parameter of the vibration signal in non-stationary state.To solve this problem,the chirplet path pursuit(CPP)algorithm is studied.The method has stronger anti noise ability than the commonly used algorithm based on time frequency representation.Therefore,it can be applied to estimate the instantaneous frequency of the bearing vibration signal under the influence of the gear noise.The angle domain resampling algorithm based on the instantaneous fault characteristic frequency(IFCF)is studied.The algorithm is based on the order spectrum of the abscissa of 1 and its multiples of the peak and the frequency of the parameters as a reference to determine the fault of the bearing.But when the vibration signal is in the strong background noise,the small modulation frequency is very easy to be submerged by the noise.Moreover,if the bearings are in the background noise such as gear noise,the gear instantaneous meshing frequency(GIMF)also affects the extraction of IFCF and the judgment of bearing state.The method is proposed in this paper based on AR model filtering in angle domain to solve this problem.To estimate the rotational speed,the GIMF is extracted from the down sampling mixed signal using CPP algorithm.The mixed signal is re-sampled by a constant angular interval based on the estimated rotational speed.The gear noise is removed in the angle domain signal used AR model.Finally,the fault diagnosis is completed by observing the order spectrum gotten by Hilbert transformation and FFT.The method of fault diagnosis of rolling element bearing under a variable rotational speed and gear vibration noise based on CPP method is proposed in this paper.First,the envelope signal is got by the Hilbert transform of the original vibration signal and reduced under the condition of satisfying the sampling theorem.Then,the IFCF curve of the fault bearing is extracted by the CPP method and resampled along with the rotation speed.Finally,the fault diagnosis is completed by the comparison of the calculating instantaneous fault characteristic coefficient(CIFCC)with the fault characteristic coefficient(FCC)of the outer ring,inner ring and rolling element.It is proved that the method can not only detect the baring fault,but also determine the fault location by processing the simulation signal and experimental signal.