Machine Learning for Reaction Wheel Fault Detection using Simulated Telemetry Data

2022 ◽  
Author(s):  
João Vaz Carneiro ◽  
Hanspeter Schaub ◽  
Morteza Lahijanian ◽  
Kendra Lang ◽  
Konstantin Borozdin
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Telemetry Data ◽  
Reaction Wheel

scholarly journals Fault Detection in the Wind Farm Turbine Using Machine Learning Based On SVM Algorithm

2021 ◽  
Vol 1964 (5) ◽  
pp. 052015
Author(s):  
S Muthukrishnan ◽  
Arun Kumar Pallekonda ◽  
R Saravanan ◽  
B Meenakshi
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Wind Farm ◽  
Svm Algorithm

Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

2021 ◽  
Author(s):  
Yang Meng ◽  
Xinyun Wu ◽  
Jumoke Oladejo ◽  
Xinyue Dong ◽  
Zhiqian Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Diagnosis And Prognosis ◽  
Industrial Boilers

Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

2021 ◽  
Vol 194 ◽  
pp. 107106
Author(s):  
M.S. Coutinho ◽  
L.R.G.S. Lourenço Novo ◽  
M.T. de Melo ◽  
L.H.A. de Medeiros ◽  
D.C.P. Barbosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power Transmission ◽  
Transmission Towers

Big Telemetry Data Analytics for Vehicle Fault Detection

2021 ◽  
Author(s):  
Jana Zdravkovic ◽  
Nikola Ilic ◽  
Dragan Stojanovic
Keyword(s):  
Fault Detection ◽  
Data Analytics ◽  
Telemetry Data

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

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