Xingxing Jiang

TL;DR: Wang et al. as discussed by the authors proposed a multi-perspective DTL model including the perspectives of space, channel and sequence for bearing fault diagnosis under varying working conditions, and the proposed model consists of three following parts: a feature enhancement network (FENet), a residual block attention model with the space and channel attention mechanisms, and a bidirectional long short term memory (BiLSTM) network is further introduced to extract the high-level discriminative features from the output of FENet under the perspective of sequence.

TL;DR: A novel adaptive cross-domain feature extraction (ACFE) method which can automatically extract similar features between different feature spaces and the intelligent fault diagnosis method for dealing with the imbalanced target dataset is described.

TL;DR: In this article, a nonlinear vibration isolator with high-static-low-Dynamic-Stiffness (HSLDS) characteristic comprised of vertical spring and horizontal spring is presented, the dynamic motion can be approximately described by the classic Duffing equation.

TL;DR: The classification results show that the hybrid method of sparse filtering and t-SNE can well extract discriminative information from the raw vibration signal and can clearly distinguish different fault types.

TL;DR: In this article, the effect of offset displacement mainly caused by overloading on the primary resonance and displacement transmissibility is investigated, and the results indicate that the system exhibits a softening characteristic under certain conditions.