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"Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Fea" by Wenlang Xie, Zhixiong Li et al.

In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an essential part of improving machine life, reducing economic losses, and avoiding safety problems caused by machine bearing failures. Most existing bearing fault diagnosis methods face challenges in extracting the fault features from raw bearing fault data. Compared with traditional methods for bearing fault characteristics extraction, deep neural networks can automatically extract intrinsic features without expert knowledge. The convolutional neural network (CNN) was utilized most widely in extracting representative features of bearing faults. Fundamental to this, the hybrid models based on the CNN and individual classifiers were proposed to diagnose bearing faults. However, CNN may not be suitable for all bearing fault classifiers. It is crucial to identify the classifiers which can maximize the CNN feature extraction ability. In this paper, four hybrid models based on CNN were built, and their fault ....

Bearing Fault Classifier , Bearing Fault Diagnosis , Convolutional Neural Network , Deep Learning , Feature Extraction , Machine Learning ,

Artificial Intelligence Can Forecast Damaging Solar Storms

Artificial Intelligence Can Forecast Damaging Solar Storms
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Yuval Reuveni , Atmospheric Administration , Ariel University Department , Convolutional Neural Network , Ariel University , Us National Oceanic , Jordan Rift Rd Center , Vlad Landa , Convolutional Neural , Astrophysical Journal , Geostationary Operational Environmental Satellite , Heliospheric Observatory , Getty Images , Solar Dynamics Observatory ,