Browsing NTNU Open by Author "Han, Peihua"
Now showing items 1-15 of 15
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Data-driven Methods for Decision Support in Smart Ship Operations
Han, Peihua (Doctoral theses at NTNU;2022:163, Doctoral thesis, 2022)Vessels operating on the surface of the ocean today are now increasingly equipped with sensors. This includes GPS, MRU, IMU that monitor the vessel’s motion behavior, and power, RPM, temperature sensors that monitor the ... -
Data-driven sea state estimation for vessels using multi-domain features from motion responses
Han, Peihua; Li, Guoyuan; Skjong, Stian; Wu, Baiheng; Zhang, Houxiang (Chapter, 2021)Situation awareness is of great importance for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion ... -
A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data
Han, Peihua; Li, Guoyuan; Skulstad, Robert; Skjong, Stian; Zhang, Houxiang (Peer reviewed; Journal article, 2020)Vessels today are being fully monitored, thanks to the advance of sensor technology. The availability of data brings ship intelligence into great attention. As part of ship intelligence, the desire of using advanced ... -
A Digital Twin of the Research Vessel Gunnerus for Lifecycle Services: Outlining Key Technologies
Zhang, Houxiang; Li, Guoyuan; Hatledal, Lars Ivar; Chu, Yingguang; Ellefsen, André Listou; Han, Peihua; Major, Pierre Yann; Skulstad, Robert; Wang, Tongtong; Hildre, Hans Petter (Journal article; Peer reviewed, 2022) -
Directional wave spectrum estimation with ship motion responses using adversarial networks
Han, Peihua; Li, Guoyuan; Skjong, Stian; Zhang, Houxiang (Peer reviewed; Journal article, 2022)The external environmental conditions around a vessel are essential for efficient and safe ship operation, among which the sea state is of key importance. Considering the ship as a large wave buoy, the sea state can be ... -
Fault Detection with LSTM-Based Variational Autoencoder for Maritime Components
Han, Peihua; Ellefsen, Andre; Li, Guoyuan; Holmeset, Finn Tore; Zhang, Houxiang (Journal article; Peer reviewed, 2021)Maintenance routines on ships today follow either a reactive maintenance (RM) or preventive maintenance (PvM) approach. RM can be regarded as post-failure repair, which might create large costs. PvM uses predetermined ... -
Fault Prognostics Using LSTM Networks: Application to Marine Diesel Engine
Han, Peihua; Ellefsen, Andre; Li, Guoyuan; Æsøy, Vilmar; Zhang, Houxiang (Peer reviewed; Journal article, 2021)Maintenance is the key to ensuring the safe and efficient operation of marine vessels. Currently, reactive maintenance and preventive maintenance are two main approaches used onboard. These approaches are either cost-intensive ... -
Interaction-Aware Short-Term Marine Vessel Trajectory Prediction With Deep Generative Models
Han, Peihua; Zhu, Mingda; Zhang, Houxiang (Peer reviewed; Journal article, 2023)Navigation safety is of paramount importance in areas with heavy and complex maritime traffic. Any ship navigating such a scenario should be able to foresee the future positions of other ships and adjust its path accordingly ... -
Interpretable Fault Detection Approach With Deep Neural Networks to Industrial Applications
Kakavandi, Fatemeh; Han, Peihua; Reus, Roger de; Larsen, Peter Gorm; Zhang, Houxiang (Chapter, 2023)Different explainable techniques have been introduced to overcome the challenges in complex machine learning models, such as uncertainty and lack of interpretability in sensitive processes. This paper presents an interpretable ... -
Leveraging Past Experience for Path planning of Marine Vessel: A Docking Example
Han, Peihua; Li, Guoyuan; Zhang, Houxiang (Peer reviewed; Journal article, 2022)Path planning before maneuvering is crucial for the safe and efficient operations of marine vessels. The past successful human maneuvering experience can be leveraged to enable the safe and efficient path planning of ... -
Local Ocean Wave Field Estimation Using A Deep Generative Model of Wave Buoys
Han, Peihua; Hildre, Hans Petter; Zhang, Houxiang (Journal article, 2023)Estimating oceanic wave fields from sparse observations has been a long-standing challenge in oceanography and an important environmental metric desired for maritime operations. The requirement for frequent real-time updates ... -
A Novel Channel and Temporal-wise Attention in Convolutional Networks for Multivariate Time Series Classification
Cheng, Xu; Han, Peihua; Li, Guoyuan; Chen, Shengyong; Zhang, Houxiang (Peer reviewed; Journal article, 2020)Multivariate time series classification (MTSC) is a fundamental and essential research problem in the domain of time series data mining. Recently deep neural networks emerged as an end-to-end solution for MTSC and achieve ... -
Online Fault Detection in Autonomous Ferries: Using fault-type in-dependent spectral anomaly detection
Ellefsen, Andre; Han, Peihua; Cheng, Xu; Holmeset, Finn Tore; Æsøy, Vilmar; Zhang, Houxiang (Peer reviewed; Journal article, 2020)Enthusiasm for ship autonomy is flourishing in the maritime industry. In this context, data-driven Prognostics and Health Management (PHM) systems have emerged as the optimal way to improve operational reliability and ... -
Real-time prediction of fish cage behaviors under varying currents using deep neural network
Gao, Sihan; Han, Peihua; Gansel, Lars Christian; Li, Guoyuan; Zhang, Houxiang (Chapter, 2023)This paper presents a Deep Neural Network (DNN) model for rapid and low-cost prediction of fish cage behavior under varying currents. We employ a numerical model of the fish cage created in Orcaflex and a set of current ... -
An Uncertainty-aware Hybrid Approach for Sea State Estimation Using Ship Motion Responses
Han, Peihua; Li, Guoyuan; Cheng, Xu; Skjong, Stian; Zhang, Houxiang (Peer reviewed; Journal article, 2021)Situation awareness is essential for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses ...