Fanrang Kong

TL;DR: A new intelligent fault diagnosis scheme based on the extraction of statistical parameters from the paving of a wavelet packet transform (WPT), a distance evaluation technique (DET) and a support vector regression (SVR)-based generic multi-class solver is proposed.

TL;DR: In this paper, an improved stochastic resonance (SR) approach with parameter tuning for identifying the defect-induced rotating machine faults is proposed. But the proposed approach requires small parameters, which is not suited for rotating machine fault diagnosis as the defect induced fault characteristic frequency is usually much higher than 1 Hz.

TL;DR: Experimental analysis with a fatigue test of an automobile transmission gearbox shows that the KPCA features outperform PCA features in terms of clustering capability, and both the two K PCA-based subspace methods can be effectively applied to gearbox condition monitoring.

TL;DR: An underdamped step-varying second-order SR (USSSR) method is proposed to further improve the output signal-to-noise ratio (SNR) and has three distinct merits as good anti-noises capability in detecting weak signal being submerged in heavy background noise.

TL;DR: In this paper, the low-dimensional principal component (PC) representations from the statistical features of the measured signals to characterize and hence, monitor machine conditions are automatically extracted using the principal component analysis (PCA) technique from the time and frequency-domains statistical features.