Research On Rolling Bearing Fault Diagnosis Method Based On Fractal Dimension And Chaotic Oscillator

Rotating machinery is an important part of modern industrial production equipment.Rolling bearings play a key role in all kinds of rotating machines.In order to ensure the normal operation of machinery and equipment,condition monitoring and fault diagnosis of rolling bearings are very necessary.When rolling bearing fails,the vibration signal has nonstationary and nonlinear characteristics.Chaos fractal theory provides a theoretical basis for analyzing the inherent mechanism of nonlinear time series,which is widely used in the research on feature extraction and fault diagnosis of mechanical equipment.In this paper,the rolling bearing is taken as the research object,the fractal dimension and chaotic oscillator are used to analyze and study the vibration data of the fault bearing.The main contents are as follows:Firstly,on the basis of studying the main characteristics and commonly used analysis methods of chaotic fractal,the correlation dimension of the fractal dimension and the traditional G-P algorithm for solving the correlation dimension are researched.After analyzing the problem of the non scale-free region recognition in the traditional G-P algorithm,the principle that Duffing chaotic oscillator identify the measured signal is researched,and the shortcomings of the Duffing chaotic oscillator applying in the detection signal are analyzed.Secondly,in order to solve the problem of scale-free recognition in the calculation of correlation dimension and the lack of the calculating accuracy to characterize fault characteristics,a new method of feature extraction based on correlation dimension and line segment clustering is proposed.After reconstructing the phase space,the logarithmic diagram is calculated and the data points of the second order derivative are processed.Then,by choosing the direction parameter and the distance parameter,the line segment clustering method is used to carry on the two clustering analysis.Finally,the correlation dimension is obtained by linear fitting and is used as feature parameter of fault signal for feature extraction.The experimental results show that the proposed method can accurately calculate the correlation dimension of fault signals and distinguish the digital characteristics of fault signals,so as to realize the feature extraction and fault diagnosis of bearing signals.Finally,aiming at the narrow detection frequency band in the fault diagnosis ofDuffing chaotic oscillator,a new fault diagnosis method for rolling bearings based on improved Duffing chaotic oscillator and VMD is proposed.The VMD method is used to decompose the bearing signal.Then,add the IMF component which containing the fault characteristic frequency to Duffing equation in the critical state as a external driving force.At the same time,by improving the Duffing oscillator equation and widening the range of the detection frequency band,the frequency of the measured signal can be identified by analyzing the change of the phase diagram.The method is applied to deal with the rolling bearing fault signal.Experiments show that the proposed method is of good effect when identifying the fault frequency of rolling bearing.