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

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

scholarly journals Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

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

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.

scholarly journals Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

2020 ◽  
Vol 10 (14) ◽  
pp. 4965
Author(s):  
Yordanos Dametw Mamuya ◽  
Yih-Der Lee ◽  
Jing-Wen Shen ◽  
Md Shafiullah ◽  
Cheng-Chien Kuo
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Radial Distribution ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Efficiency Coefficient ◽  
Distribution Grid ◽  
Phase Current ◽  
Three Phase ◽  
Types Of Faults

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.

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