Bayesian regularized artificial neural network for fault detection and isolation in wind turbine

Bakri, Ayoub El; Berrada, Youssef; Boumhidi, Ismail

doi:10.1109/isacv.2017.8054977

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In this work, we investigated the ability of a neural network trained using the Bayesian regularization technique to forecast PV power, since this method has not seen many applications in the field of solar energy prediction. The Bayesian technique has a variety of practical benefits, including the ability to solve the over-fitting problem which occurs in conventional neural networks [18].…”

Section: Bayesian Regularized Neural Networkmentioning

confidence: 99%

Principal Component Analysis and Machine Learning Approaches for Photovoltaic Power Prediction: A Comparative Study

Chahboun

Maâroufi

2021

Applied Sciences

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Nowadays, in the context of the industrial revolution 4.0, considerable volumes of data are being generated continuously from intelligent sensors and connected objects. The proper understanding and use of these amounts of data are crucial levers of performance and innovation. Machine learning is the technology that allows the full potential of big datasets to be exploited. As a branch of artificial intelligence, it enables us to discover patterns and make predictions from data based on statistics, data mining, and predictive analysis. The key goal of this study was to use machine learning approaches to forecast the hourly power produced by photovoltaic panels. A comparison analysis of various predictive models including elastic net, support vector regression, random forest, and Bayesian regularized neural networks was carried out to identify the models providing the best predicting results. The principal components analysis used to reduce the dimensionality of the input data revealed six main factor components that could explain up to 91.95% of the variation in all variables. Finally, performance metrics demonstrated that Bayesian regularized neural networks achieved the best results, giving an accuracy of R2 = 99.99% and RMSE = 0.002 kW.

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Section: Bayesian Regularized Neural Networkmentioning

confidence: 99%

Principal Component Analysis and Machine Learning Approaches for Photovoltaic Power Prediction: A Comparative Study

Chahboun

Maâroufi

2021

Applied Sciences

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“…Moreover, most of the datasets are considered high-dimensional datasets, as they have a high number of attributes/dimensions. The obtained results are compared with nine other algorithms such as Bayesian regularization, 19 BFGS quasi-Newton, 20 resilient backpropagation, 21 scaled conjugate gradient, 22 Fletcher-Powell conjugate gradient, 23 Polak-Ribire conjugate gradient, 23 one-step secant, 23 gradient descent with momentum, 22 and gradient descent 24 in terms of convergence speed, MSE, R, and CPU running time. To make the comparisons fair and minimize the random effects, the experiments are repeated 10 times/ runs.…”

Section: Comparative Algorithmsmentioning

confidence: 99%

Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems

Djelloul

Sari

Sidibe³

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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Fault diagnosis is becoming an important issue in industrial environment, and the accurate diagnosis is the most significant part in fault handling. This article discusses a fault detection and diagnosis problem for manufacturing systems taking into account rapid detection and speed performances of fault isolation with minimum ambiguity. However, in many complex real plants, it may not be possible to discover accurately the causes of probable faults. The accuracy of fault or fault detection by the traditional approaches is not adequate. Considering the quality effect of the learning algorithm, a new hybrid neural network approach is developed using the integration of a regression task for classification accuracy. Two models of neural networks: gradient descent and momentum & adaptive learning rate and Levenberg–Marquardt are investigated and compared. The performance of the proposed approach is evaluated using mean square error, convergence speed, and classification accuracy. The case study and experimental results are presented and discussed. A comparison with the Levenberg–Marquardt regression approach shows the importance of considering the proposed learning algorithm quality in the fault detection and diagnosis problem compared with those reported in the literature.

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Comparison of Internal Fault Classification of Wind Turbine Benchmark Model

Behera

Pattanayak

Mohapatra

et al. 2019

2019 Global Conference for Advancement in Technology (GCAT)

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Bayesian regularized artificial neural network for fault detection and isolation in wind turbine

Cited by 5 publications

References 9 publications

Principal Component Analysis and Machine Learning Approaches for Photovoltaic Power Prediction: A Comparative Study

Principal Component Analysis and Machine Learning Approaches for Photovoltaic Power Prediction: A Comparative Study

Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems

Comparison of Internal Fault Classification of Wind Turbine Benchmark Model

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