• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.979-984
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• 2004

The ball bearing having faults generally shows, nonlinear vibration characteristics. For the effective method of fault diagnosis on bail bearing, non-linear diagnostic methods can be used. In this paper, the correlation dimension analysis based on nonlinear timeseries was applied to diagnose the faults of ball bearing. The correlation dimension analysis shows some Intrinsic information of underlying dynamical systems, and clear the classification of the fault of ball bearing.

• Journal of Advanced Marine Engineering and Technology
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• 제40권2호
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Smart Structures and Systems
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• 제34권6호
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• pp.415-429
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• 2024

Dynamic properties extracted from bridge acceleration responses are critical for assessing safety, particularly in the context of long-span cable-supported bridges with main spans exceeding one kilometer. However, the abundance of acceleration sensors in their Structural Health Monitoring (SHM) systems is compromised by frequent failures in harsh operational environments, leading to significant issues of missing or erroneous vibration monitoring data. Recent advancements in deep learning offer promising solutions to diagnose the monitored abnormal bridge vibration data. Existing methods often rely on single-bridge vibration monitoring data, posing challenges in applying models across different bridges. To address these challenges, this study proposes a novel ResNet-based feature extraction method for bridge vibration data anomaly detection, emphasizing time-efficient classification and transfer learning. The timeseries bridge vibration responses are transformed into images to enhance computation efficiency. The proposed methodology leverages a pre-trained ResNet50 network for feature extraction, feeding extracted feature vectors into a k-means clustering algorithm for classification. Transfer learning with labelled training datasets ensures detection performance across different bridges, minimizing the required training data. Validation utilizes long-term vibration monitoring data from the SHM system of Sutong Bridge. The results aim to provide reliable technical support for data-driven condition assessment and maintenance of long-span bridges, addressing challenges in SHM systems and contributing to the safety and sustainability of critical infrastructure.

• 한국ITS학회 논문지
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• 제23권5호
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• pp.261-273
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• 2024

자동차는 주요 대기 오염원 중 하나로 작용하고 있으며 자동차가 주 오염원인 대기오염물질 데이터의 분석을 통해 전기자동차, 교통량 등과 실제 대기오염의 상관관계를 분석할 수 있으며, 이러한 분석을 위해선 대기오염물질 데이터의 신뢰성 확보가 중요하다. 본 논문은 딥러닝 모델과 동적 시간 와핑, 변화점 탐지 등의 알고리즘을 복합적으로 이용하여 전국 각지의 대기오염물질 측정소에서 측정되는 데이터 중 '베이스라인 이상' 증상이 나타나는 구간을 탐지하는 방법을 제시한다. 기존 연구들은 이전에 없던 패턴이 나타나는 데이터를 탐지하여 이상으로 정의하지만 이는 베이스라인 이상 탐지에는 적합하지 않았다. 본 논문에서는 주로 이미지 분할(Segmentation)에 사용되는 Unet모델을 시계열 데이터에 적합하도록 변형하여 사용하고 있으며 또한 동적 시간 와핑과 변화점 탐지 알고리즘을 적용하여 주변 측정소와 적절한 비교를 진행하고 이를 통해 오탐지를 최소화하였다.