scholarly journals Automotive Brake Part Inspection and Fault Localization using Deep Learning

2021 ◽  
Vol 1059 (1) ◽  
pp. 012062
Author(s):  
Santhiya Rajan ◽  
R. Rameswari ◽  
Suresh Gunasekaran
Keyword(s):  
Deep Learning ◽  
Fault Localization ◽  
Part Inspection ◽  
Automotive Brake

Turn-to-Turn Short Circuit Fault Localization in Transformer Winding via Image Processing and Deep Learning Method

2021 ◽  
pp. 1-1
Author(s):  
Arash Moradzadeh ◽  
Hamed Moayyed ◽  
Behnam Mohammadi-Ivatloo ◽  
Gevork B. Gharehpetian ◽  
A. Pedro Aguiar
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Short Circuit ◽  
Fault Localization ◽  
Learning Method ◽  
Transformer Winding ◽  
Short Circuit Fault

scholarly journals Deep Learning-Based Fault Localization in Video Networks Using Only Client-Side QoE

2020 ◽  
Vol 1 (2) ◽  
pp. 130-138
Author(s):  
Hossein Ebrahimi Dinaki ◽  
Shervin Shirmohammadi ◽  
Emil Janulewicz ◽  
David Cote
Keyword(s):  
Deep Learning ◽  
Fault Localization ◽  
Client Side

Improving deep‐learning‐based fault localization with resampling

2020 ◽  
Author(s):  
Zhuo Zhang ◽  
Yan Lei ◽  
Xiaoguang Mao ◽  
Meng Yan ◽  
Ling Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Localization

Graphical temporal semi-supervised deep learning–based principal fault localization in wind turbine systems

2020 ◽  
Vol 234 (9) ◽  
pp. 985-999 ◽  
Author(s):  
Na Jiang ◽  
Xiangzhi Hu ◽  
Ning Li
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Learning Algorithm ◽  
Fault Localization ◽  
Wind Farms ◽  
Temporal Correlation ◽  
Unlabeled Data ◽  
Machine Learning Techniques ◽  
Data Set ◽  
The Real

Principal fault localization of the faults chain, as a branch of fault diagnosis in wind turbine system, has been an essential problem to ensure the reliability and security in the real wind farms recently. It can be solved by machine learning techniques with historical data labeled with principal faults. However, most real data are unlabeled, since the labeled is expensive to obtain, which increases the difficulty to localize the principal fault if just using unlabeled data and few labeled data. So, in this article, a novel approach using unlabeled data is proposed for principal fault localization of the faults chain in wind turbine systems. First, a deep learning model, stacked sparse autoencoders, is introduced to learn and extract high-level features from data. Then, we present a graphical temporal semi-supervised learning algorithm to develop the pseudo-labeled data set with an unlabeled data set. Considering the temporal correlation of wind power data, we add a time weight vector and apply the cosine-similarity in the proposed algorithm. Finally, based on the pseudo-labeled data set, a classifier model is built and trained for the principal fault localization of the faults chain. The proposed approach is verified by the real buffer data set collected from two wind farms in China, and the experimental results show its effectiveness in practice.

scholarly journals Deep Learning-Based Fault Localization with Contextual Information

2017 ◽  
Vol E100.D (12) ◽  
pp. 3027-3031 ◽  
Author(s):  
Zhuo ZHANG ◽  
Yan LEI ◽  
Qingping TAN ◽  
Xiaoguang MAO ◽  
Ping ZENG ◽  
...  
Keyword(s):  
Deep Learning ◽  
Contextual Information ◽  
Fault Localization

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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