Xiange Tian

Diagnosis of combination faults in a planetary gearbox using a modulation signal bispectrum based sideband estimator

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme.

A study of diagnostic signatures of a deep groove ball bearing based on a nonlinear dynamic model

For accurate fault detection and diagnosis, this paper focuses on the study of bearing vibration responses under increasing radial clearances due to investable wear and different bearing grades. A nonlinear dynamic model incorporating with local defects and clearance increments is developed for a deep groove ball bearing. The model treats the inner race-shaft and outer race-housing as two lumped masses which are coupled by a nonlinear spring formalized by the Hertzian contact deformation between the balls and races. The solution of the nonlinear equation is obtained by a Runge-Kutta method in Matlab. The results show that the vibrations at fault characteristic frequencies exhibit significant changes with increasing clearances. However, an increased vibration is found for the outer race fault whereas a decreased vibration is found for inner race fault. Therefore, it is necessary to take into account these changes in determining the size of faults.

A Novel Method to Improve the Resolution of Envelope Spectrum for Bearing Fault Diagnosis Based on a Wireless Sensor Node

In this paper, an accurate envelope analysis algorithm is developed for a wireless sensor node. Since envelope signals employed in condition monitoring often have narrow frequency bandwidth, the proposed algorithm down-samples and cascades the analyzed envelope signals to construct a relatively long one. Thus, a relatively higher frequency resolution can be obtained by calculating the spectrum of the cascaded signal. In addition, a 50 % overlapping scheme is applied to avoid the distortions caused by Hilbert transform based envelope calculation. The proposed method is implemented on a wireless sensor node and tested successfully for detecting an outer race fault of a rolling bearing. The results show that the frequency resolution of the envelope spectrum is improved by 8 times while the data transmission remains at a low rate.