2021
Coupling of dynamic reaction forces of a heavy load crane and ship motion responses in waves
Ships and Offshore Structures (2021), Vol. 16, pp. 58–67
Chu, Yingguang; Li, Guoyuan; Hatledal, Lars Ivar; Holmeset, Finn Tore; Zhang, Houxiang
Links crane-induced reaction forces with vessel motions in waves for safer heavy-lift offshore operations.
Fault Prognostics Using LSTM Networks: Application to Marine Diesel Engine
IEEE Sensors Journal (2021)
Han, Peihua; Ellefsen, Andre; Li, Guoyuan; Æsøy, Vilmar; Zhang, Houxiang
Long short-term memory networks for prognostics of marine diesel engine degradation.
An Uncertainty-aware Hybrid Approach for Sea State Estimation Using Ship Motion Responses
IEEE Transactions on Industrial Informatics (2021)
Han, Peihua; Li, Guoyuan; Cheng, Xu; Skjong, Stian; Zhang, Houxiang
Combines model- and data-driven cues with explicit uncertainty for sea state inference from onboard motions.
Data-driven sea state estimation for vessels using multi-domain features from motion responses
2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 2120–2126
Han, Peihua; Li, Guoyuan; Skjong, Stian; Wu, Baiheng; Zhang, Houxiang
Uses features from multiple motion domains to estimate sea state without dedicated wave sensors.
Vico: An entity-component-system based co-simulation framework
Simulation Modelling Practice and Theory (2021), Vol. 108
Hatledal, Lars Ivar; Chu, Yingguang; Styve, Arne; Zhang, Houxiang
ECS-based framework for modular maritime and engineering co-simulation.
Open Simulation Platform - towards a maritime ecosystem for efficient co-simulation
14th International Modelica Conference (2021)
Kyllingstad, Lars Tandle
Vision for an open platform enabling interoperable maritime co-simulation across tools and partners.
A Co-operative Hybrid Model For Ship Motion Prediction
Modeling, Identification and Control (2021), Vol. 42(1), pp. 17–26
Skulstad, Robert; Li, Guoyuan; Fossen, Thor I.; Wang, Tongtong; Zhang, Houxiang
Hybrid model blending physics and learning for short-horizon ship motion forecasting.
Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction
IEEE Transactions on Industrial Informatics (2021)
Wang, Tongtong; Li, Guoyuan; Hatledal, Lars Ivar; Skulstad, Robert; Æsøy, Vilmar; Zhang, Houxiang
Calibrates maneuvering models by embedding approximate dynamics into a data-driven correction layer.
Parameter Identification of Ship Manoeuvring Model Under Disturbance Using Support Vector Machine Method
Ships and Offshore Structures (2021), Vol. 16, pp. 13–21
Wang, Tongtong; Li, Guoyuan; Wu, Baiheng; Æsøy, Vilmar; Zhang, Houxiang
SVM-based identification of maneuvering parameters in the presence of environmental disturbance.
Sailing status recognition to enhance safety awareness and path routing for a commuter ferry
Ships and Offshore Structures (2021), Vol. 16, pp. 1–12
Wu, Baiheng; Li, Guoyuan; Wang, Tongtong; Hildre, Hans Petter; Zhang, Houxiang
Recognizes operational sailing modes from data to support safety and routing for ferries.
2020
A Novel Densely Connected Convolutional Neural Network for Sea State Estimation Using Ship Motion Data
IEEE Transactions on Instrumentation and Measurement (2020), Vol. 69(9), pp. 5984–5993
Cheng, Xu; Li, Guoyuan; Ellefsen, Andre; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang
DenseNet-style CNN for estimating sea state from vessel motion time series.
SpectralSeaNet: Spectrogram and Convolutional Network-based Sea State Estimation
IECON 2020, pp. 5069–5074
Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Zhang, Houxiang; Chen, Shengyong
Spectrogram plus CNN pipeline for sea state estimation from motion recordings.
Incorporation of ship motion prediction into active heave compensation for offshore crane operation
ICIEA 2020, pp. 1444–1449
Chu, Yingguang; Li, Guoyuan; Zhang, Houxiang
Feeds predicted ship motions into active heave compensation to improve crane control.
Online Fault Detection in Autonomous Ferries: Using fault-type independent spectral anomaly detection
IEEE Transactions on Instrumentation and Measurement (2020), Vol. 69(10), pp. 8216–8225
Ellefsen, Andre; Han, Peihua; Cheng, Xu; Holmeset, Finn Tore; Æsøy, Vilmar; Zhang, Houxiang
Spectral anomaly detection for online faults without prior fault-type labels.
A Data-Driven Prognostics and Health Management System for Autonomous and Semi-Autonomous Ships
NTNU Open (2020), 173 pages
Ellefsen, Andre
Doctoral monograph on PHM architectures and methods for crew-reduced vessel operation.
Co-simulation as a Fundamental Technology for Twin Ships
Modeling, Identification and Control (2020), Vol. 41(4), pp. 297–311
Hatledal, Lars Ivar; Skulstad, Robert; Li, Guoyuan; Styve, Arne; Zhang, Houxiang
Positions FMI-based co-simulation as a core enabler of ship digital twins.
Visual Attention Assessment for Expert-in-the-loop Training in a Maritime Operation Simulator
IEEE Transactions on Industrial Informatics (2020), Vol. 16(1), pp. 522–531
Li, Guoyuan; Mao, Runze; Hildre, Hans Petter; Zhang, Houxiang
Measures operator visual attention in simulators to support expert-in-the-loop maritime training.
An effective ship control strategy for collision-free maneuver toward a dock
IEEE Access (2020), Vol. 8, pp. 110140–110152
Shuai, Yonghui; Li, Guoyuan; Xu, Jinshan; Zhang, Houxiang
Control strategy for approach and docking with collision avoidance guarantees.
A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model into the Ship Dynamic Model
IEEE Transactions on Instrumentation and Measurement (2020), Vol. 70
Skulstad, Robert; Li, Guoyuan; Fossen, Thor I.; Vik, Bjørnar; Zhang, Houxiang
Embeds a neural network inside the ship dynamic model for docking-phase motion prediction.
An effective model-based thruster failure detection method for dynamically positioned ships
IEEE ICMA 2020, pp. 898–904
Wang, Tongtong; Li, Guoyuan; Skulstad, Robert; Æsøy, Vilmar; Zhang, Houxiang
Model-based residual methods to detect thruster faults on DP vessels.
A human-expertise based statistical method for analysis of log data from a commuter ferry
ICIEA 2020, pp. 1471–1477
Wu, Baiheng; Li, Guoyuan; Zhao, Luman; Hildre, Hans Petter; Zhang, Houxiang
Statistical analysis of operational logs guided by maritime domain expertise.
A Novel Channel and Temporal-wise Attention in Convolutional Networks for Multivariate Time Series Classification
IEEE Access (2020), Vol. 8, pp. 212247–212257
Xu, Cheng; Han, Peihua; Li, Guoyuan; Chen, Shengyong; Zhang, Houxiang
Dual attention in CNNs for classifying multivariate ship and machinery time series.
Development of Onboard Decision Supporting System for Ship Docking Operations
ICIEA 2020, pp. 1456–1462
Zhao, Luman; Li, Guoyuan; Remøy, Knut Endre Græsdal; Wu, Baiheng; Zhang, Houxiang
Onboard decision support to assist crews during ship docking.
2019
Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation
ICRA 2019, pp. 6644–6650
Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang
Models DP vessel motions to extract sea state information for monitoring and control.
Data-driven uncertainty and sensitivity analysis for ship motion modeling in offshore operations
Ocean Engineering (2019), Vol. 179, pp. 261–272
Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Major, Pierre Yann; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang
Quantifies uncertainty and sensitivities in data-driven ship motion models for offshore work.
An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components
IEEE Access (2019), Vol. 7, pp. 16101–16109
Ellefsen, Andre; Bjørlykhaug, Emil Dale; Æsøy, Vilmar; Zhang, Houxiang
Autoencoder-style reconstruction errors for unsupervised fault detection on ship systems.
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
Reliability Engineering & System Safety (2019), Vol. 183, pp. 240–251
Ellefsen, Andre; Bjørlykhaug, Emil; Æsøy, Vilmar; Ushakov, Sergey; Zhang, Houxiang
Semi-supervised deep learning for RUL with limited labeled degradation data.
Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation
IEEE ICMA 2019, pp. 2195–2200
Ellefsen, Andre; Cheng, Xu; Holmeset, Finn Tore; Æsøy, Vilmar; Zhang, Houxiang; Ushakov, Sergey
Detects diesel engine degradation during autonomous ferry transits.
Validation of Data-Driven Labeling Approaches Using a Novel Deep Network Structure for Remaining Useful Life Predictions
IEEE Access (2019), Vol. 7, pp. 71563–71575
Ellefsen, Andre; Ushakov, Sergey; Æsøy, Vilmar; Zhang, Houxiang
Validates weak- and self-labeling strategies for RUL via a tailored deep architecture.
A comprehensive survey of prognostics and health management based on deep learning for autonomous ships
IEEE Transactions on Reliability (2019), Vol. 68(2), pp. 720–740
Ellefsen, Andre; Æsøy, Vilmar; Ushakov, Sergey; Zhang, Houxiang
Survey of deep learning methods for PHM in maritime autonomy contexts.
Toward Time-Optimal Trajectory Planning for Autonomous Ship Maneuvering in Close-Range Encounters
IEEE Journal of Oceanic Engineering (2019)
Li, Guoyuan; Hildre, Hans Petter; Zhang, Houxiang
Time-optimal paths for autonomous ships in close-quarters situations.
An efficient neural-network based approach to automatic ship docking
Ocean Engineering (2019), Vol. 191
Shuai, Yonghui; Li, Guoyuan; Cheng, Xu; Skulstad, Robert; Xu, Jinshan; Liu, Honghai; Zhang, Houxiang
Neural network controller for automatic docking with efficiency constraints.
Dead reckoning of dynamically positioned ships: Using an efficient recurrent neural network
IEEE Robotics & Automation Magazine (2019), Vol. 26(3), pp. 39–51
Skulstad, Robert; Li, Guoyuan; Fossen, Thor I.; Vik, Bjørnar; Zhang, Houxiang
RNN-based motion dead reckoning to bridge GNSS outages on DP vessels.
COLREGs-compliant multi-ship collision avoidance via deep reinforcement learning
ICCAS 2019, pp. 85–88
Zhao, Luman; Zhang, Houxiang; Roh, Myung-Il; Lee, Hye-Won; Chun, Do-Hyun; Lee, Sung-Jun; Lee, June-Beom
Deep RL policy for multi-vessel avoidance respecting COLREGs.