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Novelty class detection in machine learning-based condition diagnosis
유현탁,Dong-Hee Park,이정준,Hyeon Sik Kim,최병근 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.3
Industrial plant machines have a significantly lower frequency of defective data than the frequency of normal data. For this reason, machine learning is often applied using only some obtained state data. However, the low frequency of defect data does not guarantee that novel data occur, which is why detection of novelty class is required. This paper studies the novelty class detection method in multi-classification. Multi-class support vector machine was used for multi-classification. Cluster-based local outlier factor, histogram-based outlier score, outlier detection with minimum covariance determinant, isolation forest, and one-class support vector machine applied novelty class detection. Anomaly detection algorithms used the hard voting ensemble method. A feature engineering method that is advantageous for novelty class detection was confirmed by comparing the genetic algorithm (GA)-based feature selection and principal component analysis (PCA). Findings show that creating a model using GA-based feature selection for multi-classification and independent PCA for each class for novelty class detection is advantageous. With the use of an independent PCA, the problem was simplified to perform detection on a novelty class with a condition similar to the trained class.
Classification of rotary machine fault considering signal differences
유현탁,Hyoung Jin Kim,Seong Hun Park,Min Ho Kim,전이슬,Byeong-Keun Choi 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.2
Machine learning for the diagnosis of rotary machines takes priority in generating a training data set through the machine’s past data. The training data set uses features that have physical and statistical meaning of vibration signals. A training data is formed on the assumption that the normal condition of the facility is almost similar over time. However, many industrial power plants perform regular O/H (overhaul), and the vibration level of the machine's normal condition is likely to change depending on the O/H results. The vibration level is one of the important factors representing the condition change of rotating machines and is difficult to ignore easily. This paper is a study on a method that can be used for feature-based machine learning with training data formed from past data whose vibration level of the rotating machine has changed due to the influence of maintenance. Data acquisition was made through labscale defect simulation test devices, and experimental equipment was simulated before and after O/H with several faults that could occur in rotating machines. The signal named “delta signal” refers to a signal that sets each normal data as a reference signal, matches the fault signal through phase synchronization and resampling, and subtracts it to leave only a difference. The algorithms used in machine learning used genetic algorithm (GA) based feature selection and support vector machines (SVM) for learning and classification. According to the experiment, it was confirmed that in raw signal learning, the similarity by the learned condition (label) decreased due to the influence of maintenance, but the method using delta signal decreased the effect by maintenance, increasing the similarity within the same learned condition.
RK4를 이용한 삼점지지 Shaft Balancing 실험
유현탁(Hyeon-Tak Yu),이종명(Jong-Myeong Lee),김용석(Yong-Seok Kim),김학은(Hack-Eun Kim),최병근(Byeong-Keun Choi) 한국소음진동공학회 2015 한국소음진동공학회 학술대회논문집 Vol.2015 No.10
The subject of this paper is a high-pressure LNG secondary pump. This pump has a long shaft and integral type of rotor. Also, the shaft has three support points. This shape has difficulty correcting the unbalance using the existing two-plane balancing. we simulated the shaft having three supports by using the Rotor kit (RK4). By carrying out balancing at each rpm, we compared the amplitude, drew the graph and compared every aspect resulted from balancing at each rpm. Then, we checked which rpm the most advantageous balancing occurs at.
관형 철탑 용접 결함 진단을 위한 초음파 신호의 특징 분석
민태홍,유현탁,김형진,최병근,김현식,이기승,강석근 한국정보통신학회 2021 한국정보통신학회논문지 Vol.25 No.4
본 논문에서는 관형 철탑의 용접부 결함을 상시적으로 감시하기 위하여 초음파 탐상 신호에 대한 기계학습 알고리즘의 적용 방법을 제시하고 분석하였다. 기계학습 방법으로는 유전자 알고리즘에 의한 특징 선택과 서포트 벡터 머신을 이용한 탐상 신호 분류 방법을 사용하였다. 특징 선택에서는 30개의 후보 특징들 가운데 피크, 히스토그램 하한 경계, 정규 음로그우도가 선택되었으며, 이들은 결함의 깊이에 따른 신호의 차이를 명확하게 나타내었다. 또한, 선택된 특징들을 서포트 벡터 머신에 적용한 결과 정상 부위와 결함 부위를 완벽하게 분류할 수 있는 것으로 나타났다. 따라서 본 연구의 결과는 향후 초음파 신호 기반 결함 성장 조기 감지시스템의 개발과 이를 통한 에너지 송전 관련 산업에 유용하게 사용될 수 있을 것으로 기대된다. In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.