Cheon-Hong Min

An unsupervised anomaly-detection framework is proposed and field validated for thrust-bearing monitoring in the impulse turbine of a shoreline oscillating water-column (OWC) wave energy converter (WEC) off Jeju Island, Korea. Operational monitoring is constrained by nonstationary sea states, scarce fault labels, and low-rate supervisory logging at 20 Hz. To address these conditions, a 24 h period of normal operation was median-filtered to suppress outliers, and six physically motivated time-domain features were computed from triaxial vibration at 10 s intervals: absolute mean; standard deviation (STD); root mean square (RMS); skewness; shape factor (SF); and crest factor (CF, peak divided by RMS). A feature-based autoencoder was trained to reconstruct the feature vectors, and reconstruction error was evaluated with an adaptive threshold derived from the moving mean and moving standard deviation to accommodate baseline drift. Performance was assessed on a 2 h test segment that includes a 40 min simulated fault window created by doubling the triaxial vibration amplitudes prior to preprocessing and feature extraction. The detector achieved accuracy of 0.99, precision of 1.00, recall of 0.98, and F1 score of 0.99, with no false positives and five false negatives. These results indicate dependable detection at low sampling rates with modest computational cost. The chosen feature set provides physical interpretability under the 20 Hz constraint, and denoising stabilizes indicators against marine transients, supporting applicability in operational settings. Limitations associated with simulated faults are acknowledged. Future work will incorporate long-term field observations with verified fault progressions, cross-site validation, and integration with digital-twin-enabled maintenance.

This research article focuses on developing a baseline digital twin model for a wave power generator structure located in Yongsu-ri, Jeju-do, South Korea. First, this study performs a cause analysis on the discrepancy of the dynamic properties from the real structure and an existing simulation model and finds the necessity of modeling the non-structural masses and the environmental factors. The large amounts of the ballast are modeled in the finite element model to enhance the accuracy of the digital twin. Considering the influence of environmental factors such as tide level and wave direction, the added mass effect of structural members, one of the hydrodynamic effects, depending on the change of the ocean environments is calculated based on the rule of Det Norske Veritas and applied. The results indicate that non-structural mass components significantly impact the dynamic characteristics of the structure. Additionally, environmental factors have a greater effect on the dynamic behavior of the box-type structure compared to lightweight offshore structures.

The key to coping with global warming is reconstructing energy governance from carbon-based to sustainable resources. Offshore energy sources, such as offshore wind turbines, are promising alternatives. However, the abnormal climate is a potential threat to the safety of offshore structures because construction guidelines cannot embrace climate outliers. A cosine similarity-based maintenance strategy may be a possible solution for managing and mitigating these risks. However, a study reporting its application to an actual field structure has not yet been reported. Thus, as an initial study, this study investigated whether the technique is applicable or whether it has limitations in the real field using an actual example, the Gageocho Ocean Research Station. Consequently, it was found that damage can only be detected correctly if the damage states are very similar to the comparison target database. Therefore, the high accuracy of natural frequencies, including environmental effects, should be ensured. Specifically, damage scenarios must be carefully designed, and an alternative is to devise more efficient techniques that can compensate for the present procedure.

In order to utilize wave energy, various wave power systems are being actively researched and developed and interest in them is increasing. To maximize the operational efficiency, it is very important to monitor and maintain the fault of components of the system. In recent years, interest in the management cost, high reliability and facility utilization of such systems has increased. In this regard, fault diagnosis technology including fault factor analysis and fault reproduction is drawing attention as an important main technology. Therefore, in this study, to reproduce and monitor the faults of a wave power system, firstly, the failure mode of the system was analyzed using FMEA analysis. Secondly, according to the derived failure mode and effect, the thrust bearing was selected as a target for fault reproduction and a test equipment bench was constructed. Finally, with the vibration data obtained by conducting the tests, the vibration spectrum was analyzed to extract the features of the data for each operating status; the data was classified by applying the three machine learning algorithms: naïve Bayes (NB), k-nearest neighbor (k-NN), and multi-layer perceptron (MLP). The criteria for determining the fault were derived. It is estimated that a more efficient fault diagnosis is possible by using the standard and fault monitoring method of this study.

There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

A Dynamic Analysis of 150 ton Winch using Ocean Environment Data analysis model of winch;ocean environment data;subsystem modeling;integrated framework for winch analysis; This paper seeks to provide a dynamic analysis of a 150 ton winch based on ocean environmental data. The winch model that was subjected to analysis was modeled from CAD to each subsystem by the commercial software DAFUL. The winch model has tree brake systems (disk brake, band brake and ratchet brake). The rotation motion of the motor and contact elements of the brake are applied to the winch model in order to analyze its dynamic characteristics. In addition, a crane-barge was modeled to apply ocean environmental data. The motion data of the crane-barge was produced by means of the RAO(Response Amplitude Operator) of the barge and wave spectrum. The reaction force of the translational joint was measured instead of the tension of the cable. The brake performance of the winch was produced and assessed based on the operating motion of the crane-barge.