Xiaoxi Ding

TL;DR: Comparisons of clustering distribution and classification accuracy with six other features show that the proposed feature mining approach is quite suitable for spindle bearing fault diagnosis with multiclass classification regardless of the load fluctuation.

TL;DR: Wang et al. as mentioned in this paper explored the active role of healthy pattern in fault classification and proposed a new fusion feature extraction method based on locality preserving projections (LPP) to discover the local feature pattern difference between each bearing status and the healthy condition to characterize and discriminate different bearing statuses.

TL;DR: The proposed TFM sparse reconstruction method combines the merits of the TFM in denoising and the atomic decomposition in image sparse reconstruction and makes it possible to express the nonlinear signal processing results explicitly in theory.

TL;DR: In this article, an iterative transient feature extraction approach is proposed based on time-frequency domain sparse representation, where the TF atoms are constructed based on the TF distribution (TFD) of the Morlet wavelet bases and local TF templates are formulated from TF atoms for the matching process.

TL;DR: The proposed FTFM-based reconstruction method indicates attractive prospects in the following two aspects: effective but efficient TFM learning for practical and on-line application, sound and adaptive signal reconstruction with the data-driven F TFM basis.