Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

2021 ◽  
Vol 173 ◽  
pp. 114598
Author(s):  
Serkan Ayvaz ◽  
Koray Alpay
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Shara I. Feld ◽  
Daniel S. Hippe ◽  
Ljubomir Miljacic ◽  
Nayak L. Polissar ◽  
Shu-Fang Newman ◽  
...  

2021 ◽  
pp. 116073
Author(s):  
Paulo Augusto de Lima Medeiros ◽  
Gabriel Vinícius Souza da Silva ◽  
Felipe Ricardo dos Santos Fernandes ◽  
Ignacio Sánchez-Gendriz ◽  
Hertz Wilton Castro Lins ◽  
...  

Author(s):  
Sai Kumar Chilukuri ◽  
Nagendra Panini Challa ◽  
J. S. Shyam Mohan ◽  
S. Gokulakrishnan ◽  
R. Vasanth Kumar Mehta ◽  
...  

2020 ◽  
Author(s):  
Claudia Corradino ◽  
Gaetana Ganci ◽  
Giuseppe Bilotta ◽  
Annalisa Cappello ◽  
Ciro Del Negro

<p>Detect, locate and characterize eruptions in real-time is fundamental to monitor volcanic activity. Here we present an automatic system able to discover and identify the main types of eruptive activities by exploiting infrared images acquired by the thermal cameras installed around Mount Etna volcano. The system, which employs the machine learning approach, is based on a decision tree tool and a bag of words-based classifier. The decision tree provides information on the visibility level of the monitored area, while the bag of words-based classifiers detects the onset of the eruptive activity and recognize the eruption type among either explosion and/or lava flow or plume. Thus, applied to each image of all thermal cameras over Etna in real-time, the proposed system provides two outputs, namely the visibility level and the recognized activity status. By merging the outcomes coming from each thermal camera, the monitored phenomena can be fully described from different perspectives getting deeper information in real-time and in an automatic way.   </p>


2021 ◽  
Vol 54 (1) ◽  
pp. 1187-1192
Author(s):  
G. Aiello ◽  
A. Certa ◽  
Islam Abusohyon ◽  
Francesco Longo ◽  
Antonio Padovano

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