Xintao Jiao

TL;DR: A novel method is proposed to extract fault features from non-stationary vibration signals of gearboxes using the techniques of signal sparse decomposition and order tracking and an improved matching pursuit algorithm on segmental signal is designed to solve sparse coefficients and reconstruct steady- type fault components and impact-type fault components.

TL;DR: In this article, a novel order tracking method based on discrete spectrum correction technique is proposed to analyze wind turbine gearbox vibration for the purposes of health monitoring and fault diagnosis, and the results show that the shaft rotating speed could be accurately identified from the vibration signal together with amplitudes of significant gear meshing components.

TL;DR: In this article, a new method is presented to rectify the distortion and improve the accuracy of wavelet coefficients by using the complementary characteristic of the wavelet filters, which can effectively magnify the amplitude of the fault frequencies in practice.

TL;DR: In this paper, a gear case coupling modulation signal separation method based on inner product transformation and correlation filtering is proposed. But the method is not suitable for the case where the impact can not be easily extracted easily according to the traditional method.

TL;DR: Wang et al. as discussed by the authors proposed a non-intrusive load monitoring model based on graph neural networks, which uses the graph structure to characterize the correlation information between data nodes and combines Long Short-Term Memory to extract the time-domain features of the data.