Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems.

Bearings play a crucial role as key components in electric motors widely used in industrial settings. When defects occur in these bearings, they can lead to serious motor failures, causing significant downtime for the entire system. Therefore, prompt detection of bearing defects is essential to minimize system downtime and improve production efficiency. Previous research has mainly focused on using convolutional neural network (CNN)-based and recurrent neural network (RNN)-based models for bearing fault diagnosis. However, these models often involve high computational complexity for learning and rely on high-dimensional data, such as time series data collected from sensors or image data generated from time series data. Employing these models can result in significant delays in the fault diagnosis process, making them less suitable for real-world industrial environments. In this study, we propose an innovative approach to reduce both training and inference time by utilizing time domain features (TDF) obtained through the transformation of time series current data. The TDF provides a low-dimensional data representation that can be used to facilitate model training and inference. Additionally, the proposed model uses linear layers based on residual structure and dense connectivity structure to improve performance and reduce model complexity. To evaluate the effectiveness of our proposed method, we conducted simulations using the Paderborn University bearing dataset for training and inference. In experimental results, we show that the proposed method outperforms conventional methods in model complexity and fault diagnosis accuracy.

As Climate change has become a major issue worldwide, the importance of building energy management systems (BEMS) is increasing. Because of obligation of public institutions to introduce BEMS and increase in BEMS installation due to the increase in smart buildings, It has become essential to understand and analyze about energy consumption to predict it in order to reduce carbon emissions and strengthen energy-saving policies. Through many previous studies, several analysis models have been proposed that can accurately predict energy consumption, and these models have shown excellent performance in each study. However, to obtain interesting results, a sufficient amount of training data is required. Obtaining a satisfactory amount of data sets generally requires a lot of time and effort, and sometimes it is impossible. This is an obstacle to applying deep learning models to many tasks. In this study, we propose a method for supplementing insufficient performance by transferring knowledge from similar types of data so that deep learning models can perform well even with sparse data sets.