Pengxin Wang

TL;DR: An enhanced intelligent diagnosis method for rotary equipment based on multi-sensor data-fusion and an improved deep convolutional neural network (CNN) models shows higher prediction accuracy and more obvious visualization clustering effects.

TL;DR: Compared with the typical ODL method, the ICM-ODL algorithm can not only improves the anti-noise performance of the dictionary atoms, but also removes the noise compositions of the reconstructed signal significantly.

TL;DR: An adaptive data fusion strategy based on deep learning called the convolutional neural network with atrous convolution for the adaptive fusion of multiple source data (FAC-CNN) is presented, found to exhibit superior performance.

TL;DR: In this article, a recursive sparse representation (RSR) algorithm is proposed to solve the fault diagnosis of bearings from sparse representation in the time and frequency domains, where the tunable Q-factor wavelet transform (TQWT) filtering strategy is used to adaptively obtain the best wavelet with the signal vibration features.

TL;DR: In this paper, a multiscale feature fusion convolutional neural network (MFF-CNN) is proposed for the diagnosis of rotating machines based on deep learning models, which extracts, modulates, and fuses the input samples' multi-scale features to focus more on the health state difference rather than the noise disturbance and workload difference.