Deformable Surface Registration with Extreme Learning Machines

Gritsenko, A. N.; Sun, Zhiyu; Baek, Stephen; Miché, Yoan; Hu, Renjie; Lendasse, Amaury

doi:10.1007/978-3-030-01520-6_28

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…(2012) Deep learning is a recent addition to the modeling suite with promising applications in multiple domains (Ouyang et al, 2017;Sun et al, 2020a;Sun et al, 2020b;Shen et al, 2021a;He et al, 2018;Li et al, 2018). The deep learning algorithms are capable of extracting in-depth features and patterns within the training dataset (Gritsenko et al, 2017;Ouyang et al, 2019;Li et al, 2020;Shen et al, 2021b;Shen and Raksincharoensak 2021). Within the wind energy sector, it has been applied in the prediction tasks of wind speed (Hu et al, 2016), wind power (Wang et al, 2017), and wind direction (Wang et al, 2016a;Li et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

et al. 2021

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Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.

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Section: Introductionmentioning

confidence: 99%

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

et al. 2021

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“…shown that ELM has the universal approximation and classification properties. [61], ELM has good generalized performance in most cases and the learning speed is thousands of times faster than conventional neural networks [42,52].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, reducing the computational time for machine learning algorithms is evermore desirable. Extreme Learning Machines (ELMs) [4,42,61,82,104] is a type of Randomized Neural Networks (RNNs) that is known for its fast training speed and good accuracy.…”

Section: Motivationmentioning

confidence: 99%

Random neural networks for dimensionality reduction and regularized supervised learning

Hu¹

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, for serving as my committee members. Thank you for letting my defense be an enjoyable moment. Your brilliant comments and constructive suggestions are much appreciated. Lastly, I would like to give my family, especially my parents, a special thanks. I am appreciative beyond the words for the love and encouragement you give me, which makes me have the courage to pursuit what I really love. Thank you for all the long-lasting companionship, unconditional supports, and constant guidance.

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ELM Feature Selection and SOM Data Visualization for Nursing Survey Datasets

Farag

Björk

et al. 2020

Proceedings in Adaptation, Learning and Optimization

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Deformable Surface Registration with Extreme Learning Machines

Cited by 4 publications

References 35 publications

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

Random neural networks for dimensionality reduction and regularized supervised learning

ELM Feature Selection and SOM Data Visualization for Nursing Survey Datasets

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