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Ship design engineering refers to the development and design of shipbuilding architectures in a drawing which reflects all relevant manufacturing processes. This paper provides analysis methods for model-information interfaces between hull structure design and outfitting design, and a technical application for manufacturing phases reflecting the pipe support pad and angle item automatically. The existing information procedure of pipe support pad and angle system processes information using drawing without model specification. Outfitting design team directly distributes drawings to the shop floor then manual-based marking and installation work are conducted refer to the distributed drawings. As a result, this process has become time consuming and causes problems in the productivity and quality improvement due to the rework caused by omitted or incorrect marking. The pipe support pad and angle marking is a method that automatically updates model information to hull structure design using sets of data that analyse the generated model in outfitting design processes. Therefore, this approach provides an efficient solution through design references without manual activities such as a reflection of hull structure design, cutting process, numerical control work, and dimension measurement and marking. The conversion of a method from the existing procedure based on manual marking to the reflective and automatic approach would have enabled to proceed installation work without manual activities for the measurement. Therefore, this research study proposes an efficient approach using pre-data analysis of model information interfaces between design and manufacturing phases to improve productivity during construction for shipbuilding.

The environmental regulations on vessels being strengthened by the International Maritime Organization (IMO) has led to a steady growth in the eco-friendly ship market. Related research is being actively conducted, including many studies on the maintenance and predictive maintenance of propulsion systems (including electric motors, rotating bodies) in electric propulsion vessels. The present study intends to enhance the artificial intelligence-based failure diagnosis rate for electric propulsion vessel propulsion systems. To verify the proposed AI-based failure diagnosis algorithm for electric motors, this study utilized the vibration data of mechanical equipment (electric motors) in an urban railway station. Securing and preprocessing high-quality data is crucial for improving the failure diagnosis rate, in addition to the performance of the diagnostic algorithm. However, the conventional wavelet transform method, which is generally used for machine signal processing, has a disadvantage of data loss when the data distribution is abnormal or skewed. This study, to overcome this shortcoming, proposes an AI-based DAE method that can remove noise while maintaining data characteristics for signal processing of mechanical equipment. This study preprocessed vibration data by using the DAE method, and extracted significant features from the data through the feature extraction method. The extracted features were utilized to train the one-class support vector machine model and to allow the model to diagnose the failure. Finally, the F-1 score was calculated by using the failure diagnosis results, and the most meaningful feature extraction method was determined for the vibration data. In addition, this study compared and evaluated the preprocessing performance based on the DAE and the wavelet transform methods.

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