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Alexander Prosvirin
Alexander Prosvirin
Research Scientist at University of Ulsan, Ulsan, South Korea
Подтвержден адрес электронной почты в домене liveuou.kr - Главная страница
Название
Процитировано
Процитировано
Год
Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers
RN Toma, AE Prosvirin, JM Kim
Sensors 20 (7), 1884, 2020
1092020
Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks
DK Appana, A Prosvirin, JM Kim
Soft Computing 22 (20), 6719-6729, 2018
642018
Rolling-element bearing fault diagnosis using advanced machine learning-based observer
F Piltan, AE Prosvirin, I Jeong, K Im, JM Kim
Applied Sciences 9 (24), 5404, 2019
282019
Bearing fault diagnosis based on convolutional neural networks with kurtogram representation of acoustic emission signals
A Prosvirin, JY Kim, JM Kim
Advances in Computer Science and Ubiquitous Computing, 21-26, 2017
242017
Towards bearing health prognosis using generative adversarial networks: Modeling bearing degradation
SA Khan, AE Prosvirin, JM Kim
2018 International Conference on Advancements in Computational Sciences …, 2018
222018
Rub-impact fault diagnosis using an effective IMF selection technique in ensemble empirical mode decomposition and hybrid feature models
AE Prosvirin, M Islam, J Kim, JM Kim
Sensors 18 (7), 2040, 2018
212018
Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines
MMM Islam, AE Prosvirin, JM Kim
Mechanical Systems and Signal Processing 160, 107853, 2021
202021
An improved algorithm for selecting IMF components in ensemble empirical mode decomposition for domain of rub-impact fault diagnosis
AE Prosvirin, MMM Islam, JM Kim
IEEE Access 7, 121728-121741, 2019
162019
Construction of a sensitive and speed invariant gearbox fault diagnosis model using an incorporated utilizing adaptive noise control and a stacked sparse autoencoder-based deep …
CD Nguyen, AE Prosvirin, CH Kim, JM Kim
Sensors 21 (1), 18, 2020
152020
An SVM-based neural adaptive variable structure observer for fault diagnosis and fault-tolerant control of a robot manipulator
F Piltan, AE Prosvirin, M Sohaib, B Saldivar, JM Kim
Applied Sciences 10 (4), 1344, 2020
132020
Novel bearing fault diagnosis using gaussian mixture model-based fault band selection
AS Maliuk, AE Prosvirin, Z Ahmad, CH Kim, JM Kim
Sensors 21 (19), 6579, 2021
122021
A reliable fault diagnosis method for a gearbox system with varying rotational speeds
CD Nguyen, A Prosvirin, JM Kim
Sensors 20 (11), 3105, 2020
112020
Multistage centrifugal pump fault diagnosis by selecting fault characteristic modes of vibration and using Pearson linear discriminant analysis
Z Ahmad, AE Prosvirin, J Kim, JM Kim
IEEE Access 8, 223030-223040, 2020
102020
Efficient rub-impact fault diagnosis scheme based on hybrid feature extraction and SVM
A Prosvirin, J Kim, JM Kim
Advances in Computer Communication and Computational Sciences, 405-415, 2019
102019
Global and local feature extraction using a convolutional autoencoder and neural networks for diagnosing centrifugal pump mechanical faults
AE Prosvirin, Z Ahmad, JM Kim
IEEE Access 9, 65838-65854, 2021
62021
Blade rub-impact fault identification using autoencoder-based nonlinear function approximation and a deep neural network
AE Prosvirin, F Piltan, JM Kim
Sensors 20 (21), 6265, 2020
62020
Hybrid Rubbing Fault Identification Using a Deep Learning-Based Observation Technique
AE Prosvirin, F Piltan, JM Kim
IEEE Transactions on Neural Networks and Learning Systems 32 (11), 5144-5155, 2020
52020
Intelligent rubbing fault identification using multivariate signals and a multivariate one-dimensional convolutional neural network
AE Prosvirin, AS Maliuk, JM Kim
Expert Systems with Applications 198, 116868, 2022
42022
SVM hyperparameter optimization using a genetic algorithm for rub-impact fault diagnosis
A Prosvirin, BP Duong, JM Kim
Advances in Computer Communication and Computational Sciences, 155-165, 2019
32019
Fault prediction of rolling element bearings using one class least squares SVM
A Prosvirin, MMM Islam, C Kim, JM Kim
The Engineering and Arts Society in Korea, EASKO, 2017
32017
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