Feature fusion and distillation embedded sparse Bayesian learning model for in-situ foreknowledge of robotic machining errors
S Zhao, H Sun, F Peng, R Yan, X Tang, Y Shan… - Journal of Manufacturing …, 2023 - Elsevier
In recent years, robotic machining of complex surface has become a research hotspot for
intelligent manufacture. Considering the weak rigidity characteristics of industrial robot …
intelligent manufacture. Considering the weak rigidity characteristics of industrial robot …
Deep discriminative sparse representation learning for machinery fault diagnosis
R Yao, H Jiang, W Jiang, Y Liu, Y Dong - Engineering Applications of …, 2024 - Elsevier
The high complexity of actual machinery vibration environments introduces various
interferences into vibration signals, making it challenging to eliminate redundant information …
interferences into vibration signals, making it challenging to eliminate redundant information …
Intelligent faults diagnostics of turbine vibration's via Fourier transform and neuro-fuzzy systems with wavelets exploitation
Gas turbines play a vital role in gas transportation and power generation, but they are prone
to instability phenomena that can lead to vibrations, shorten equipment lifespan, and result …
to instability phenomena that can lead to vibrations, shorten equipment lifespan, and result …
Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven
Z Jiao, Z Zhang, Y Li, Y Wu, L Liu… - … Science and Technology, 2024 - iopscience.iop.org
Currently, the diagnostic performance of many deep learning algorithms may drop
dramatically when the distribution of training data is significantly different from that of the test …
dramatically when the distribution of training data is significantly different from that of the test …