Papers by Author: Gui Cai Zhang

Abstract: Impulse response provides important information about flaws in mechanical system. Deconvolution is one system identification technique for fault detection when signals captured from bearings with and without flaw are both available. However effects of measurement systems and noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving average (ARMA), is used to estimate vibration pattern of rolling element bearings for fault detection. The frequently used ARMA estimator cannot characterize non-Gaussian noise completely. Aimed at circumventing the inefficiency of the second-order statistics-based ARMA estimator, higher-order statistics (HOS) was introduced to ARMA estimator, which eliminates the effect of noise greatly and, therefore, offers more accurate estimation of the system. Furthermore, bispectrums of the estimated HOS-based ARMA models were subsequently applied to get clearer information. Impulse responses of signals captured from the test bearings without and with flaws and their bispectra were compared for the purpose of fault detection. The results demonstrated the excellent capability of this method in vibration signal processing and fault detection.

Abstract: In this paper, a simplified finite element model of the cracked crankshaft is proposed, and a new method for simulating the nonlinear vibration of operating crankshaft with several cracks is presented. For crankshaft, cracks occur frequently in the parts of crankpin-web fillet region and the edge of oil aperture because of fatigue or damage. According to the characteristic of those cracks, the cracked parts are modeled by the corresponding cracked spatial finite elements respectively, and two cracked elements are discussed in this study. The other, un-cracked, crankshaft parts are modeled by spatial Timoshenko beam elements. Flywheel and front pulley are simplified as lumped mass elements, and main bearings are simulated by equivalent linear springs and dashpots. In order to find the dynamic response of crankshaft-bearing system, a right-handed rotating coordinate system attached to crankshaft is applied. Based on the proposed finite element model, the breathing behavior of cracks in operating crankshaft is studied, and the nonlinear motion equation with variational stiffness is formed. Finally, a four-in-line crankshaft is taken as an example, and its vibration response corresponding to different kinds of crack are calculated and analyzed. Some conclusions are drawn, and a foundation is laid for diagnosing crack fault of crankshaft.

Abstract: Minor and random slip between rolling elements and races in rolling element bearings makes vibration signals have periodically time-varying ensemble statistics, which is known as cyclostationarity. Two second-order cyclostationary methods, the spectral correlation density (SCD) and the degree of cyclostationarity (DCS), are talked about in this paper based on a statistical model of rolling element bearings. The SCD provides redundant information in bi-frequency plane and cyclic frequency domain embodies the majority of it, which is a series of non-zero discrete cyclic frequencies completely reflecting the fault characters of rolling element bearings. The DCS has virtues of less computation and clearer representation, at the same time keeps the same characters with SCD in cyclic frequency domain. And the DCS is also proved to be resistant to the additive and multiplicative stationary noise. Simulation and experiential results from three rolling element bearing faults: outer race defect, inner race defect and rolling element defect, indicate practicability of the DCS analysis in rolling element bearing condition monitoring and fault diagnosis.

Abstract: Bispectrum is a powerful tool for non-Gaussian signal processing and nonlinearity detection. However, it is difficult to use in practical applications due to that it is a 2-dimensional function. Bispectral slices are widely used reduction methods, and they can only represent a small part of the whole bispectral information. Integrated bispectrum contains more signal features than that of the bispectral slices, whereas the integration will lose the focus of some signal features. To overcome these problems, a new approach called maximal bispectrum is proposed to extract signal features. Maximal bispectrum is obtained by selecting the maximal values of every row of the magnitude bispectrum in the whole bispectral plane and it is a 1-dimensional function. Feature extraction based on maximal bispectrum is investigated and the maximal bispectrum is used to extract features of gear fault. Experimental results indicate that the maximal bispectrum is effective for diagnosing gear crack fault.

Abstract: Noise is the biggest obstacle that makes the incipient fault prognosis results uncorrected. According to the theories of correlation analysis and threshold de-noising by wavelets, wavelet transform domain filter (WTDF) is constructed. WTDF is an iterative process. By selecting the process parameters adaptively, WTDF can de-noise signal efficiently. More important, the faint component in the signal will become stronger. WTDF method is used to analyze the signals collected from a bearing that has incipient unbalance and misalignment faults. Results show that WTDF is effective for bearing incipient fault prognosis.