Automatic Classification of Rotating Machinery Defects Using Machine Learning (ML) Algorithms

2020 ◽  
pp. 193-203
Author(s):  
Wend-Benedo Zoungrana ◽  
Abdellah Chehri ◽  
Alfred Zimmermann
Keyword(s):  
Machine Learning ◽  
Automatic Classification ◽  
Rotating Machinery

scholarly journals Classification of Daily Irradiance Profiles and the Behaviour of Photovoltaic Plant Elements: The Effects of Cloud Enhancement

2021 ◽  
Vol 11 (11) ◽  
pp. 5230
Author(s):  
Isabel Santiago ◽  
Jorge Luis Esquivel-Martin ◽  
David Trillo-Montero ◽  
Rafael Jesús Real-Calvo ◽  
Víctor Pallarés-López
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Automatic Classification ◽  
Sampling Frequency ◽  
Machine Learning Algorithms ◽  
Unsupervised Machine Learning ◽  
Average Efficiency ◽  
Clear Sky ◽  
Photovoltaic Plant

In this work, the automatic classification of daily irradiance profiles registered in a photovoltaic installation located in the south of Spain was carried out for a period of nine years, with a sampling frequency of 5 min, and the subsequent analysis of the operation of the elements of the installation on each type of day was also performed. The classification was based on the total daily irradiance values and the fluctuations of this parameter throughout the day. The irradiance profiles were grouped into nine different categories using unsupervised machine learning algorithms for clustering, implemented in Python. It was found that the behaviour of the modules and the inverter of the installation was influenced by the type of day obtained, such that the latter worked with a better average efficiency on days with higher irradiance and lower fluctuations. However, the modules worked with better average efficiency on days with irradiance fluctuations than on clear sky days. This behaviour of the modules may be due to the presence, on days with passing clouds, of the phenomenon known as cloud enhancement, in which, due to reflections of radiation on the edges of the clouds, irradiance values can be higher at certain moments than those that occur on clear sky days, without passing clouds. This is due to the higher energy generated during these irradiance peaks and to the lower temperatures that the module reaches due to the shaded areas created by the clouds, resulting in a reduction in its temperature losses.

scholarly journals A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

2020 ◽  
pp. 107151
Author(s):  
Alexis Falcin ◽  
Jean-Philippe Métaxian ◽  
Jérôme Mars ◽  
Éléonore Stutzmann ◽  
Jean-Christophe Komorowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Classification ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Volcanic Seismicity

scholarly journals Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

Sensors ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 75 ◽  
Author(s):  
Ole Rindal ◽  
Trine Seeberg ◽  
Johannes Tjønnås ◽  
Pål Haugnes ◽  
Øyvind Sandbakk
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Automatic Classification ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Micro Sensor ◽  
Cross Country Skiing ◽  
Cross Country ◽  
Classical Cross

STUDY ON AUTOMATIC CLASSIFICATION OF GRAVEL BEACH SEDIMENTS USING MACHINE LEARNING

2021 ◽  
Vol 77 (2) ◽  
pp. I_673-I_678
Author(s):  
Yuta INOUE ◽  
Masami KIKU ◽  
Tomoaki NAKAMURA ◽  
Norimi MIZUTANI
Keyword(s):  
Machine Learning ◽  
Automatic Classification ◽  
Beach Sediments ◽  
Gravel Beach

scholarly journals Machine learning for automatic classification of volcano-seismic signatures

2017 ◽  
Author(s):  
Marielle Malfante ◽  
Mauro Dalla Mura ◽  
Jerome I. Mars ◽  
Jean-Philippe Metaxian
Keyword(s):  
Machine Learning ◽  
Automatic Classification
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