Study On Vibration Signal Based Fractal Feature Extraction Methods For Fault Diagnosis

Study on mechanical installation fault diagnosis，fault feature extraction andpattern recognition is key problems，its concerns accuracy and reliability of the faultdiagnosis.at present,monitoring and diagnostics for vibration signals with mechanicalinstallation fault is the most suitable and commonly.formation mechanism revealedthrough study nonlinear characteristics of vibration signals,Then we apply singularvalue decomposition、Self-affine fractal、fractal dimension and so on to extraction faultfeature,failure diagnosis with pattern recognition by using support vector machines.This dissertation includes the following topics:①Nonlinear characteristics of vibration signals are analyzed of two aspectscompletely and objectively.The firstly，nonlinear chaos characteristics of vibrationsignals had been analyzed,the test methods include:recurrence plot,CLY method，powerspectrum and method of calculation Lyapunov exponent spectrum for time series basedon least-squared support vector machine(LS-SVM),the results show that vibrationsignals are chaotic and super chaos characteristics, the bigger is a fault,the super chaoticis it.The secondly,methods of computation hurst index and multi-fractal spectrum areimproved,then,fractal structure of vibration signals analyzed by hurst index andmulti-fractal spectrum.The results show that vibration signals have different fractalcharacteristic values in different conditions,so it have different fractal characteristicstructure indifferent conditions.②A adaptive piecewise self-affine fractal fault feature extraction method wasexplored for data compression of vibration signal based on the analysis of thetheoretical basis on fractal compression and based on nonlinear analysis.themethodology and steps are given in detail,The peculiarity of the adaptive fractalalgorithm is that the length of the subsection decided by error threshold in terms ofproperty of vibration signals，where estimation of the threshold has been analyzed，andthe comparison was made with the piecewise self-affine fractal compressionmethod.Applications of the mothod to actual vibration signals as well as simulationsignals have been given with good results obtained in respect of data compression ratioand signal reconstruction precision,this method are able to extract and save fault featureof vibration signalmore effective.③Considering fault sample of fault diagnosis is lack.a abnormal identification method based on support vector data description(SVDD) in chaos and fractalcharacteristic is presented.we Deals with chaos and fractal characteristic,then thereceiver operating characteristic(ROC) curve for two classifying errors in classificationfield are also synthesized based on SVDD is utilized to select better features, mostlyresearched feature selection for chaos and fractal combined,and kernel functionsparameters to have an effect on classification of fault.Results of experiment showed thatfeature combined can be used for distinguish between normal states and fault states,thismethod is only required normal state samples can identification between normal statesand fault states，and it were able to favorable discrimination unknown fault type.④To solve the vibration signal mixed with interference information detrend，andaffect the accuracy of fault diagnosis.For this defect,the multi-fractal detrendedfluctuation analysis(MF-DFA) is introduced into the field of vibration faultdiagnosis,and vibration signals analysis method based on the parameters of multi-fractalspectrum features is presented.it utilize polynomial fitting method of several section tovibration signals eliminate detrended,four kinds of multifractal spectrum parameterscharacteristics of the vibration signals compared with each other，the0is employedto fault features. Finally, a0and support vector machine is applied to faultdiagnosis.Simulation results proved that the fluctuation of the vibration signals showedsignificant multi-fractal characteristics,the parameters0of singular spectrum andsupport vector machine can distinguish between normal status and fault status with highperformance for vibration fault diagnosis.⑤According to the vibration weak fault frequency characteristic extractionproblem is pretty difficult,a fault feature extraction method is proposed Based onsingular value decomposition(SVD) and morphological filters.This method makes useof the relations between the singular value distribution of the time series track matrix ofattractor and the signal characteristics to select the way of reconstruction of signal bymost potential reflecting singular values. This way can filter smooth information andpartial noise in the signal, and gets impulse information with noise in the signal, thentake the advantage of the feature that morphological filters was used to extract impulsefeature in fault signal to.act in opposition to pick out the extract impulse fault feature insignal and applies it to fault feature extraction of bearing in vibration signal. Results ofexperiment showed that the presented method can be used for the abstraction of theweak impact feature signal that mixed in the strong background noise, which is effectiveto abstract weak impact feature signal. Finally, a summary of the research contents is presented. Moreover, the furtherresearch object and the target are pointed out.