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선박의 기관시스템 보조기기 펌프의 상태기반정비를 위한 HHT 및 CNN을 활용한 고장 분류
유병문,이종직,김용진,이태현,김영기,장화섭,박재철,박지혁 한국신뢰성학회 2023 신뢰성응용연구 Vol.23 No.4
Purpose: This study focuses on test bed construction for condition-based maintenance of auxiliary pump systems in autonomous ships and fault classification using machine learning. Methods: Experiments were conducted on a test bed using failure simulation conditions. Vibration data underwent preprocessing and were converted into images by Hilbert-Huang transform (HHT). The convolutional neural networks (CNN) performed feature extraction and learning to classify faults. Results: The study confirmed a high percentage classification accuracy of 96% for six operational states (normal and abnormal). Increasing the convolutional layers improved training accuracy but caused overfitting, as indicated by lower validation accuracy. Simplifying the structure and regularization techniques, such as dropout, enhanced the model’s predictive performance. Conclusion: This study developed a 2D CNN-based algorithm that successfully classified faults with 96% acc
대칭정보를 이용한 다품종 I.C Lead Frame 검사
유병문,황치정 충남대학교 자연과학연구소 1992 忠南科學硏究誌 Vol.19 No.1
In this paper, a feature point(e.g. reference hole) and symmetry-based approach is introduced which can carry out the precise and flexible I.C lead frames inspection. Extracting symmetry from a given industrial image, four folds of a lead frame are compared one another, one is the reference and another is the comparative data. And then we can make a decision whether the lead frame is good or bad (including defect type) by analyzing the image difference. The algorithm is characterized by its short processing time and robustness to the image transfer and gray-data processing.