Vibration Signal Analysis Of Bearings In The Rotating Machinery

Bearings are one of the most widely used elements in the rotating machinery andtheir performance of great necessity to the operation of the mechanical equipment. Inorder to timely identify the deterioration in the condition of the bearings, it is significantto carry out the vibration based condition monitoring and fault diagnosis. In this thesis,the kurtogram based method is firstly developed and improved for the de-noising andfault characterization of the vibration signals buried in strong noise for the rollingelement bearings. Subsequently, the directional cyclostationarity analysis basedalgorithm is proposed to reveal significant features of the vibration signature for thejournal bearings with oil film instability faults. Finally, the integrated and virtualmeasurement and diagnosis system for the bearings of rotary machines is developed tofacilitate the practical application of the achievement in this thesis. The main researchesare listed as follows:Firstly, the mathematical basis including the kurtosis statistics and spectral kurtosiscoefficients of the kurtogram is reviewed. Subsequently, the specific algorithm of theconventional kurtogram as well as the COT based order-kurtogram is introduced. It isstated that the kurtogram is able to de-noise and identify the optimal demodulation bandfor the following analysis of the vibration signal of the rolling element bearings. Sincethe traditional spectral kurtosis coefficient is easy to be influenced by the singularvalues in the original signal, the robust spectral kurtosis coefficients including theMoors spectral kurtosis, Hogg spectral kurtosis and the Crow-Siddiqui spectral kurtosisare defined. The robust kurtogram is also proposed to eliminate the interference of thesingular values. The simulation test and practical application has verified that theproposed robust kurtogram is able to improve the filtering effect and enhance theimpulsiveness of the fault signal.Since the filtered signals will be inputted to the distribution of the spectral kurtosis,it is necessary to select the optimal filter in order to improve the ability of the kurtogram.The STFT suffers from the trade-off between the time resolution and frequencyresolution according to the Heisenberg uncertainty principle. The FIR filter-bank suffersfrom the low operation efficiency and WPT from the limit of the wavelet functionlibrary. Therefore, an improved kurtogram is proposed based on the scale-adaptiveredundant lifting wavelet packet transform which can construct the updater and predictor according to the signal itself as well as to adaptively decompose the originalsignal. The results have proved that the proposed method is able to improve theadaptivity of the conventional kurtogram and ensure the accuracy of the signal filteredby the kurtogram. Moreover, the intrisic time-scale decomposition based Hilbertspectrum has been proposed to supplement the envelope analysis. Compared with theHilbert-Huang transform, practical test has verified the superiority of the ITD basedHilbert spectrum when characterizing the distribution of the time-frequency energy ofthe fault signal.Cyclostationarity defined to represent the correlation features of periodicphenomenon has been widely used to characterizing the fault vibration signal obtainedfrom the rotating machinery. Based on the conventional cyclic statistics, the directionalcyclic parameters including the directional cyclic mean, directional cyclic correlation,and directional spectral correlation density are defined to represent the complex-valuesignal integrated from double channel. It is found that the directional cyclic statistics areable to extract the periodically varying characteristics as well as the information relatedto the vibrating condition of the journal bearing supported rotor under oil film instabilityfault. In addition, the relationship between the conventional full spectrum, thedirectional Wigner distribution and the directional cyclic statistics is revealed. The fullspectrum can be interpreted as the first order cyclic statistics, namely the cyclic mean.The directional Wigner distribution belongs to the second order cyclic parameters(directional cyclic correlation function). Moreover, the acceleration signal basedcondition monitoring is compared with the displacement signal based conditionmonitoring when describing the system vibration.According to the requirements analysis of the vibration based condition monitoringon bearings, the NI Labview based virtual measurement and diagnosis system for thebearings of rotary machines is developed. The system which is able to conduct dataacquisition, signal analysis and fault diagnosis has facilitated the practical application ofthe achievement in this thesis.