A series of material tests were performed on cryogenic metallic materials meant for liquid hydrogen storage tanks using a 20 K tensile cryostat and an electrochemical hydrogen-charging apparatus. Mechanical evaluation of the electrochemically hydrogen-charged specimens was performed in a tensile cryostat using helium gas at ambient temperature and cryogenic temperature (20 K). The tensile cryostat was equipped with a vacuum jacket and a G-M cryocooler with gaseous helium. Furthermore, the cathodic electrolysis cell used for charging the specimens was adopted for internal hydrogen conditions with a reflux condenser and heating mantle to increase hydrogen diffusivity. The target materials were austenite stainless steel and aluminum alloy, which are suitable for liquefied natural gas and gaseous hydrogen environments. No significant change in the yield strength and flow stress of the hydrogen-charged specimen up to 20% strain was observed. However, changes in tensile strength and elongation were observed thereafter. Electrochemical hydrogen charging of stainless steel leads to a high concentration of hydrogen on the surface of the specimen. The resulting surface cracks reduced the flow stress. The 20 K tensile test showed discontinuous yielding in the austenitic stainless steel with an abrupt increase in temperature. The mechanical performance of the aluminum alloys improved in terms of strength and elongation. Changes in the mechanical performance and relative area reduction were observed for all the metallic materials at 300 K and 20 K.

As the urgency for carbon-neutral fuels grows in response to global warming and environmental pollution, liquid hydrogen, with its high energy density, emerges as a promising candidate. Stored at temperatures below 20 K, liquid hydrogen’s containment system requires materials resilient to such cryogenic temperatures. Austenitic stainless steel, including 304L grade, has been widely used due to its favorable properties. However, designing pressure vessels for these systems necessitates a deep understanding of fracture mechanics and accurate assessments of the material’s fracture toughness at cryogenic temperatures. The mechanical behavior at these temperatures differs significantly from that at room temperature, making testing at 20 K a complex procedure that requires stringent facilities. This study examines the tensile behavior and fracture toughness of 304L stainless steel at cryogenic temperatures, comparing and analyzing the characteristics observed at 20 K with those at room temperature. The phenomenon of discontinuous yield, with abrupt stress drops and stepwise deformation at low temperatures, has been identified, resulting in more complex stress–strain curves. Limitations were found in the calculation of the crack length during the assessment of fracture toughness in stainless steel under extremely low-temperature environments through the J-integral compliance method. To address these constraints, a comparative analysis was carried out to determine potential corrective measures.

Maritime autonomous surface ships (MASS) are proposed as a future technology of the maritime industry. One of the key technologies for the development of MASS is condition-based maintenance (CBM) based on prognostics and health management (PHM). The CBM technology can be used for early detection of abnormalities based on the database and for a prediction of the fault occurring in the future. However, this technology has a problem that requires a high-quality database that reproduces the operation state of the actual ships and quantitatively and systematically indicates the characteristics for the various fault state of the device. To solve this problem, this paper presents a study on the development method of the fault database based on the reliability. Firstly, the reliability analysis of the target device was performed to select five types of the core fault modes. After that, a fault simulation scenario that defined the fault simulation test methodology was drawn. A land-based testbed was built for the fault simulation test. The fault simulation database was developed with a total of 109 sets through the fault simulation test. Additionally, a fault classification algorithm based on deep learning is proposed. The classification performance was evaluated with a confusion matrix. The developed database will be expected to serve as the basis for the development CBM technology of MASS in the future.

With the development of the Internet of things, big data, and AI leading the 4th industrial revolution, it has become possible to acquire, manage, and analyze vast and diverse condition signals from various industrial machinery facilities. In addition, it has been revealed that various and large amounts of signals acquired from the facilities can be utilized for fault diagnosis. Currently, while data-driven fault diagnosis techniques applicable to the facilities are being developed, it has been tried to apply the techniques for the development of fully autonomous ships in the shipbuilding and shipping industry. Since the autonomous ships must be able to detect and diagnose the failures on their own in real time, the overall research is required on how to acquire signals from the ship facilities and use them to diagnose their failures. In this study, a fault diagnosis framework was proposed for condition-based maintenance (CBM) of ship oil purifiers, which are an auxiliary facility in the engine system of a ship. First, an oil purifier test-bed for simulating faults was built to obtain data on the state of the equipment. After extracting features using discrete wavelet decomposition from the data, the features were visualized by using t-distributed stochastic neighbor embedding, and were used to train support vector machine-based diagnostic models. Finally, the trained models were evaluated with $Accuracy$ and $F_1 score$, and some models scored 0.99 or higher, confirming high diagnostic performance. This study can be used as a reference for establishing CBM system and fault diagnosis system. Furthermore, this study is expected to improve the safety and reliability of oil purifiers in Degree 4 MASS.

In MIL-STD-810, the environmental engineering consideration and laboratory tests by US military, the DP (damage potential) formula is used to calculate the fatigue damage by vibration environments which is a simplified version of a FDS (fatigue damage spectrum) formula. DP, however, was originally made for comparison between different test standards and is not an optimized formula for the synthesis of vibration environments. This paper presents the GRS (Gaussian Random Synthesis) formula for only vibration test synthesis featuring simplified DP formula and produce the same result as DP. Although the GRS provides insight into the synthesis of vibration tests, it must be used with care because it inherits the constraints of the original FDS formula.

Pumps, as core pieces of equipment in ships, are installed in the engine room to supply refined oil to the engine. Pump failure causes critical problems for ship operations. Therefore, failure-monitoring-based diagnosis technology is an essential requirement in the shipbuilding industry. For this purpose, a database containing information about the failure states depending on the main cause of the failure cases of the pump needs to be developed. In the present study, failure causes of pumps based on actual accident records were quantitatively analyzed. Then, failure modes for the bearing, coupling, sealing, and screw, which are the core parts of the oil pump, were determined. Test infrastructures for the oil pump were developed to obtain normal and abnormal data considering diverse operating conditions. Based on the vibration data from the accelerometer installed on the test infrastructures, the frequency of failure was analyzed through Fast Fourier Transform (FFT). In addition, more precise results were obtained by performing Short-Time Fourier Transform (STFT) for the FFT results that indicated severe failure. Finally, over 200 data entries were accumulated on the core parts of the oil pump, considering normal as well as abnormal operating conditions. The database constructed in this study is expected to help in investigating failure diagnosis and prediction of algorithm models for ship management.