Oscillation Detection in Process Industries by a Machine Learning-Based Approach

2019 ◽  
Vol 58 (31) ◽  
pp. 14180-14192 ◽  
Author(s):  
Jônathan W. V. Dambros ◽  
Jorge O. Trierweiler ◽  
Marcelo Farenzena ◽  
Marius Kloft
Keyword(s):  
Machine Learning ◽  
Process Industries ◽  
Oscillation Detection

scholarly journals Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

2021 ◽  
Vol 12 (2) ◽  
pp. 1205
Author(s):  
Jyothi R ◽  
Tejas Holla ◽  
Uma Rao K ◽  
Jayapal R
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Induction Motor ◽  
Diagnostic Tool ◽  
Voltage Source ◽  
Power Electronic ◽  
Real Time Monitoring ◽  
Process Industries ◽  
Ac Drives ◽  
Wide Range

AC drives are employed in process industries for varying applications resulting in a wide range of ratings. The entire process industry has seen a paradigm shift from manual to automated systems. The major factor contributing to this is the advanced power electronics technology enabling power electronic drives for smooth control of electric motors. Induction motors are most commonly used in industries. Faults in the power electronic circuits may occur periodically. These faults often go unnoticed as they rarely cause a complete shutdown and the fault levels may not be large enough to lead to a breakdown of the drive. An early detection of these faults is required to prevent their escalation into major faults. The diagnostic tool for detection of faults requires real time monitoring of the entire drive. In this work, detailed investigation of different faults that can occur in the power electronic circuit of an industrial drive is carried out. Analysis and impact of faults on the performance of the induction motor is presented. A real time monitoring platform is proposed to detect and classify the fault accurately using machine learning. A diagnostic tool also is developed to display the severity and location of the fault to the operator to take corrective measures.

Oscillation detection in process industries – Part II: Industrial application

2019 ◽  
Vol 78 ◽  
pp. 139-154 ◽  
Author(s):  
Jônathan W.V. Dambros ◽  
Jorge O. Trierweiler ◽  
Marcelo Farenzena ◽  
Ariel Kempf ◽  
Luís G.S. Longhi ◽  
...  
Keyword(s):  
Industrial Application ◽  
Process Industries ◽  
Oscillation Detection

Oscillation detection in process industries – Part I: Review of the detection methods

2019 ◽  
Vol 78 ◽  
pp. 108-123 ◽  
Author(s):  
Jônathan W.V. Dambros ◽  
Jorge O. Trierweiler ◽  
Marcelo Farenzena
Keyword(s):  
Detection Methods ◽  
Process Industries ◽  
Oscillation Detection

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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