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.

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.

The impact of the Fourth Industrial Revolution has brought significant attention to Condition-based maintenance (CBM) for autonomous ships. This study aims to apply CBM to the fuel supply pump of a ship. Five major failures were identified through reliability analysis, and structural analysis was conducted to investigate the mechanisms by which one failure induces another, leading to the identification of three compound failure scenarios. Data were collected on a test bed under normal conditions, five single failure conditions, and three compound failure conditions. The acceleration data from the experiments were transformed into 2D arrays corresponding to a single pump rotation, and a method was proposed to compensate for the errors accumulated during the repeated array generation. The data were vectorized using a simplified CNN structure and applied to six multi-label learning methods, which were compared to identify the optimal approach. Among the six methods, the Label Powerset (LP) was found to be the most effective. Multi-label learning captures correlations between labels, similar to the failure-inducing mechanisms learned from structural analysis.

A ship’s automatic mooring system relies on vacuum suction pads with rubber seals to withstand external loads, such as mooring forces. This paper focused on the design requirements and performance testing of vacuum suction pads to develop a high-performance automatic mooring system and evaluated the performance of vacuum suction pads through real-scale testing. The mooring capacity of the target ship, the training ship HANBADA, was estimated based on the port and fishing port design standards of the Ministry of Oceans and Fisheries. Under the most extreme ocean conditions (beafort 6), the estimated longitudinal (surge) and lateral (sway) mooring forces acting on HANBADA were 17.7 and 248 kN, respectively. In the real-scale performance test, stable suction was achieved under both dry and water spraying conditions, with the suction force ranging from 180 to 200 kN, under sway conditions. The vacuum ratio remained satisfactory, indicating stable suction. However, under surge conditions, the vacuum ratio decreased slightly to 0.99. Furthermore, the rubber seals returned to their initial shape after load removal, demonstrating their effectiveness in the automatic mooring system. This study provides valuable insights into the design requirements and performance testing of vacuum suction pads, establishing their suitability for developing high-performance automatic mooring systems.

A PTO gearbox ("gearbox") is designed to be mounted on a construction vehicle to convey power from the engine to multiple pumps. The gearbox uses oil bath lubrication, which often experiences a disruption in the supply oflubricant inside the gearbox. Poor lubrication in any gearing system is a major cause of a drop in its efficiency, which is often displayed in the form of heat or noise. This study describes a technology that can reduce noise by making splash lubricant inside the gearbox fall down along the wall like a curtain and measures its performance through comparison. Measurement of noise was made under no-load condition and load condition. For the former, measurement was made while the servo motor was used for driving to minimize noise from the equipment. For the load test, the noise from the equipment was unavoidable because an electrical dynamometer had to be used to control the load, but a comparison was made against the measured noise to determine where there had been any impact. If any impact was identified, a correction would be applied for the noise measured.