Weiguo Huang

TL;DR: Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection in this paper, and the proposed method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period.

TL;DR: A coarse-to-fine decomposing strategy is proposed for weak fault detection of rotating machines and can well-detect the weak repetitive transients in the signals with heavy noise and overcome the drawbacks of the original VMD.

TL;DR: The proposed adaptive spectral kurtosis filtering technique is applied in the extraction of the signal transients that shows the gear fault, which proves the effectiveness of the proposed technique in extracting the signaltransients in the practical application.

TL;DR: A joint time-frequency (TF) squeezing method and generalized demodulation (GD) to realize variable speed bearing fault diagnosis and has better performance than those methods based on conventional TF analysis and resampling.

TL;DR: In this article, a new transient feature extraction technique is proposed for gearbox fault diagnosis based on sparse representation in wavelet basis, which can extract both the impulse time and the period of transients.