Extraction Method Research Of Quantification Feature For Rotor Fault Signal

Fault diagnosis consists of three parts of the content, that is, signal processing, feature extraction, fault identification.Feature extraction, is a dynamic signal preprocessing system analysis of the information obtained by extraction with the system state of the data, then analyze these data, extract the system state-dependent with greater sensitivity Feature, is the key of fault diagnosis and one of the focuses of theoretical research.Whether feather is extracted completely and correctly or not, directly affects the success of diagnosis and diagnostic accuracy. Signal feature extraction method commonly used the following two shortcomings:(1)Difficult to accurately describe the nonlinear and non stationary signal characteristics;(2)Difficult to solve the signal characteristics of the quantitative evaluation.In this paper, I use entropy analysis method to study the mechanical signal feature extraction and quantitative evaluation methods.Rotor test bed for the data model,using information entropy for the fault type, degree are given an objective quantitative evaluation.Main contents and conclusions of the study were as follows:1))Based on searching a lot of domestic and foreign literature current situation and existing problems of information entropy measure in fault diagnosis are analyzed.The nature of information entropy and spectral entropy of the singular time domain, frequency domain power spectral entropy and time-frequency domain of wavelet energy spectrum entropy are studied and disscussed systematically.2))Its common failure mechanism is studied based on bearing-Rotor System Dynamics model.A finite impulse response low-pass filter is designed,which filters the collected signals,to provide raw data for the feature extraction.While the AR model based on maximum entropy method of power spectrum algorithm is applied to fault diagnosis categories,which has higher resolution than traditional methods,smooth spectrum,strong ability of noise immunity,and fits bearing-Rotor data processing very much.3)Fast-ICA and AMUSE algorithms which are dealed with the blind signal are studied.Based on the simulation analysis,the data separated from Fast-ICA algorithm is selected as singular spectrum entropy of the sample data because of smaller separation performance Its own merits.Singular spectrum entropy algorithm based on Fast-ICA algorithm is proposed.Through the analysis of experimental signals,the algorithm can effectively evaluate the state of rotor vibration characteristics of indicators.4) In the view of information fusion,application methods of three kinds of information entropy measures,which describe vibration signal energy, such as the singular spectrum entropy in the time domain, power spectrum entropy in the frequency domain and wavelet energy spectrum entropy in the time-frequency domain, In accordance with Generalized sets concept by the feature information fusion about the above-mentioned three kinds of information entropy measures,a new fault identification method in the bearing-rotor system which base on Generalized sets concept of time spatial of the information entropy is proposed.The analysis shows that the method has the Function of three-dimensional feature space graphical representation of fault state domain, demonstrates significant differences between different fault types,and enhances the accuracy of identification fault with a reference value.Entropy theory in the field of fault diagnosis of fever brought in the quantitative extraction of features worthy of further study.