Study On Fault Diagnosis Method For Rotor-bearing

The application of fault diagnosis technic with rotating machine is encouraged tomonitor facility status, ensure production line working regularly, and prevent from majoraccident. Rotor-bearing system is one of the most widely used elements in rotatorymachines. The running status of the rotor-bearing system is important to the performanceof the rotatory machinery. The fault diagnosis of rotor-bearing system has greatsignificance for development of contemporary industry.Firstly, a fault diagnosis study based on wavelet packet energy and Hilbert transformis put forward. That is relative to the modulation and energy concentration of faulted ballbearing vibration signal. The vibration signal of ball bearing is decomposed andreconstructed using wavelet packet transform. And energy calculation of every frequencyband is also done. Selects the frequency band with maximal energy. Then, analyses thesignal of the frequency band applying Hilbert transform. Finally, extracts the characteristicfrequency of fault signal. At present, the computation of fault features is accomplishedartificially. In this paper, a new method which can select fault features automatically ispresented. Through processing and analyzing the practical ball bearing experiment data, itis shown that the fault diagnosis study can diagnose different running states of ballbearings due to surface damage timely and exactly.Secondly, a method based on harmonic wavelet packet is proposed to extract featuresof rotor faults. Due to the distribution of rotating frequency doubling in every node relyingon rotor rotation rate, so there is no unified physical meaning of them in different rotatingrates. To eliminate the effect of rotor rotating rate to doubling frequency features, applingthe scale transform theory resamples the original signal firstly. And then decomposes thesignals with harmonic wavelet packet and computers energy of each node. Throughprocessing and analyzing the practical oil-whirl experiment data, it is shown that the studycan extract rotor faults features in diverse rotation rates intelligently. This providesaccurate data surpporting for fault diagnosis.Thirdly, put forward a method of rotor fault disgnosis based on support vector machine and fuzzy c mean clustering. Sample datas to train SVM are pre-selected withthe fuzzy c means clustering. It is useful to reduce time consuming in computation, andensure the classification accuracy. The grid search method based on cross–validation ischosen to determine model parameters. The model is optimal and efficent due tocaculating parameter C andγmeanwhile. Unbalance experiment of rotor,misalignment experiment of rotor, rubbing experiment of rotor, and whirling experimentof rotor are carried out on ZT-3 exprimental instrument. Through analysing the vibrationsignal of rotor fault, it is proved that the feature extraction method based on scaletransform and the fault diagnosis method based on SVM+FCM are efficient.Finally, fault diagnosis platform of rotor-bearing system based on LabVIEW andMATLAB is developed. MATLAB script is applied in the fault diagnosis platform. Theuser interface is frendly. And the engineering caculation capbility of the platform is strong.The platform provides function of time domain analysis, frequency domain analysis,intelligent fault diagnosis, result saving, and historical data inquirying. The fault diagnosisplatform is real-time and accurate.