A New Approach to Automated Bearing Fault Severity Assessment Using Deep-Learning

2020 ◽  
pp. 1091-1104
Author(s):  
Lucas Mailey ◽  
Pietro Borghesani ◽  
Wade A. Smith ◽  
Wenyi Wang ◽  
Zhongxiao Peng
Keyword(s):  
Deep Learning ◽  
Severity Assessment ◽  
New Approach ◽  
Bearing Fault ◽  
Fault Severity

Bearing Fault Diagnosis with Deep Learning Models

2020 ◽  
Author(s):  
Chia-An Yi ◽  
Yu-Ling Wang ◽  
Huei-Yang Lai ◽  
Yi-Wei Chen ◽  
Chan-Yun Yang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Models ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

A physics-informed deep learning approach for bearing fault detection

2021 ◽  
Vol 103 ◽  
pp. 104295
Author(s):  
Sheng Shen ◽  
Hao Lu ◽  
Mohammadkazem Sadoughi ◽  
Chao Hu ◽  
Venkat Nemani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Learning Approach ◽  
Bearing Fault ◽  
Bearing Fault Detection

scholarly journals Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Stefan Gerlach ◽  
Christoph Fürweger ◽  
Theresa Hofmann ◽  
Alexander Schlaefer
Keyword(s):  
Deep Learning ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Plan Quality ◽  
New Approach ◽  
Beam Generation ◽  
Radiological Features ◽  
Improve Treatment ◽  
Robotic Radiosurgery ◽  
Treatment Plans

AbstractAlthough robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.

scholarly journals A Deep-Learning-Based Framework for Severity Assessment of COVID-19 with CT Images

2021 ◽  
pp. 115616
Author(s):  
Zhidan Li ◽  
Shixuan Zhao ◽  
Yang Chen ◽  
Fuya Luo ◽  
Zhiqing Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Severity Assessment

A survey on Deep Learning based bearing fault diagnosis

2019 ◽  
Vol 335 ◽  
pp. 327-335 ◽  
Author(s):  
Duy-Tang Hoang ◽  
Hee-Jun Kang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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