Papers by Author: Ming J. Zuo

Abstract: Fractal dimension reflects the complexity of dynamic systems, and contains the health status information of machinery. The experimental vibration signals of a gearbox with different gear crack size are analyzed using correlation dimension, and it is found that the correlation dimension increases monotonically with the increasing crack level. This is consistent with the nature of a nonlinear dynamic system: the more severe the fault, the more complicated the dynamic system, and accordingly the larger the fractal dimension. Correlation dimension has potential to assess gear localized damage.

Abstract: In this paper, a new digital signal processing method for ultrasonic time-of-flight diffraction (TOFD) estimation is presented. This method is based on wavelet analysis using the Morlet mother wavelet and the least mean squares (LMS) filter. It is designed to remove noise and identify the echo starting point of the ultrasonic signal reflected from the tip of a crack. Both simulated data and experimental data obtained from a steel plate with a crack are used to demonstrate the performance of the proposed method. This method is especially useful when the properties of the ultrasonic crack signal are unknown and the noise is heavy.

Abstract: The cyclic autocorrelation function is used with regard to the cyclostationarity of gear vibrations in order to extract the modulation features of gearbox vibration signals, and to detect localized gear damage. The properties of the amplitude and frequency modulated signals in the cyclic frequency domain are summarized in order to investigate the differences between the modulation features of normal and faulty gearbox vibration signals. Gear tooth spalling is detected by the presence of many sidebands in a zero-lag time-slice of the cyclic autocorrelation function, thereby indicating an increase in the degree of modulation effect. The damage source is located by the spacing of the sidebands.

Abstract: The ultrasonic time-of-flight-diffraction (TOFD) detection method has been widely used in crack size assessment. The key issue in TOFD is to determine the arrival time of crack tip diffracted signal. In the traditional cross correlation method, the resulting maximum peak of cross correlation function between two signals indicates the time of flight between them. In practical ultrasonic measurement, the transmission wave may be distorted and phase shift may be introduced. This paper presents a method using cross correlation and phase shift correction to improve the accuracy of crack sizing in the TOFD framework. The resulting maximum peak of the cross correlation function between two signals combining with time delay introduced by phase shift determine the arrival time of diffracted signal. Experimental results are used to demonstrate the advantage of the proposed method.

Abstract: Model-based gear dynamic analysis and simulation has been a promising way for developing e®ective gearbox vibration monitoring approaches. In this paper, based on the dynamic model of a one-stage gearbox with spur gears and one tooth crack, we investigate statistical indicators and the discrete wavelet transform (DWT) technique to identify e®ective and sensitive health indicators for re°ecting the crack propagation level. Our results suggest that the root mean square (RMS) indicator is a good statistical indicator to re°ect the crack propagation in the early stage; DWT can improve the sensitivity of the RMS indicator and the RMS indicator becomes more sensitive with the increase of the DWT level.

Abstract: Machine vibration signal has been used in fault detection and diagnosis. Modulation and non-stationarity existing in the signal generated by a faulty gearbox present challenges to effective fault detection. Hilbert transform has the ability to address the modulation issue. This paper outlines a novel fault detection method called Hilbert & TT-transform (HTT-transform) which combines Hilbert transform and TT-transform obtained from the inverse Fourier transform of the S-transform. The principle of the proposed method is to analyze the modulating signal created by a faulty gear using a time-time representation. The method has the advantage of providing a new way of localizing the time features of the modulating signal around a particular point on the time axis through scaled windows. It is verified with simulated signals and real gearbox vibration signals. The results obtained by CWT, S-transform, TT- transform, and HTT-transform are compared. They show that utilizing the proposed method can improve the effectiveness of gearbox fault detection.