Byeng D. Youn

dblp:62/11333 · also Byeng Dong Youn · DBLP profile ↗
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14ranked-venue papers
0as first author
13since 2021 · last 2026
0000-0003-0135-3660ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 11 · 11 since 2021Databases, data management, data science and information retrieval · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1
YearPublicationVenuePosition
2026 An adapter-enhanced, Fourier feature deep operator network for fault severity estimation of stator inter-turn short circuits in induction motors
Minseok Chae, Hyeongmin Kim, Sang Kyung Lee, Joonho Yang, Heonjun Yoon, Byeng D. Youn
Eng. Appl. Artif. Intell.7
2026 Frequency-band graph-based sensor fusion with sensitivity-aware energy assist network for machinery system fault diagnosis
Sang Kyung Lee, Hyeongmin Kim, Minseok Chae, Joonho Yang, Heonjun Yoon, Byeng D. Youn
Eng. Appl. Artif. Intell.7
2025 Optimized relative entropy for robust fault detection in excavator traveling gearboxes via smeared envelope spectrum analysis of cyclo-non-stationary signals
Kyumin Na, Keon Kim, Jinoh Yoo, Jinwook Lee, Byeng D. Youn
Expert Syst. Appl.5
2025 Spectral kurtosis attention network (SKAN): Synergizing signal processing and deep learning for fault diagnosis of rolling element bearings
Jinoh Yoo, Jong Moon Ha, Byeng D. Youn
Expert Syst. Appl.6
2024 Spectrum-guided GAN with density-directionality sampling: Diverse high-fidelity signal generation for fault diagnosis of rotating machinery
Jin Uk Ko, Jinwook Lee, Yong Chae Kim, Joon Ha Jung, Byeng D. Youn
Adv. Eng. Informatics6
2024 Latent space alignment based domain adaptation (LSADA) for fault diagnosis of rotating machinery
Yong Chae Kim, Jin Uk Ko, Jinwook Lee, Joon Ha Jung, Byeng D. Youn
Adv. Eng. Informatics6
2024 Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance
Jinwook Lee, Jin Uk Ko, Yong Chae Kim, Joon Ha Jung, Byeng D. Youn
Expert Syst. Appl.6
2024 Revolution and peak discrepancy-based domain alignment method for bearing fault diagnosis under very low-speed conditions
Seungyun Lee, Sungjong Kim, Su J. Kim, Heonjun Yoon, Byeng D. Youn
Expert Syst. Appl.6
2024 FASER: Fault-affected signal energy ratio for fault diagnosis of gearboxes under repetitive operating conditions
Kyumin Na, Yunhan Kim, Heonjun Yoon, Byeng D. Youn
Expert Syst. Appl.4
2024 Self-supervised feature learning for motor fault diagnosis under various torque conditions
Sang Kyung Lee, Hyeongmin Kim, Minseok Chae, Hye Jun Oh, Heonjun Yoon, Byeng D. Youn
Knowl. Based Syst.6
2023 Opt-TCAE: Optimal temporal convolutional auto-encoder for boiler tube leakage detection in a thermal power plant using multi-sensor data
Hyeongmin Kim, Jin Uk Ko, Kyumin Na, Hyeonchan Lee, Jong-Duk Son, Heonjun Yoon, Byeng D. Youn
Expert Syst. Appl.8
2023 Frequency-learning generative network (FLGN) to generate vibration signals of variable lengths
Jin Uk Ko, Jinwook Lee, Yong Chae Kim, Byeng D. Youn
Expert Syst. Appl.5
2022 A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam turbines
Jin Uk Ko, Kyumin Na, Joon-Seok Oh, Jaedong Kim, Byeng D. Youn
Expert Syst. Appl.5
2016 Model-Based Fault Diagnosis of a Planetary Gear: A Novel Approach Using Transmission Error
abstract
Extensive prior studies aimed at the development of diagnostic methods for planetary gearboxes have mainly examined acceleration and acoustic emission signals. Recently, due to the relationship between gear mesh stiffness and transmission error (TE), TE-based techniques have emerged as a promising way to analyze dynamic behavior of spur and helical gears. However, to date, TE has not been used as a measure to detect faults in planetary gears. In this paper, we propose a new methodology for model-based fault diagnostics of planetary gears using TE signals. A lumped parametric model of planetary gear dynamics was built to extract simulated TE signals, while accounting for the planet phasing effect, which is a peculiar characteristic of the planetary gear. Next, gear dynamic analysis was performed using TE signals, and TE-based damage features were calculated from the processed TE signals to quantitatively represent health conditions. The procedures described aforesaid were then applied to a case study of a planetary gear in a wind turbine gear train. From the results, we conclude that TE signals can be used to detect the faults, while enhancing understanding of the complex dynamic behaviors of planetary gears.
Jungho Park, Jong Moon Ha, Hyun-Seok Oh, Byeng D. Youn, Joo Ho Choi, Nam Ho Kim
IEEE Trans. Reliab.4