A Neural-Network-Based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion

Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang

doi:10.1109/joe.2018.2882276

Cited by 25 publications

(9 citation statements)

References 38 publications

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“…In order to remove redundant information among the input features and reduce the computational complexity of variance-based sensitivity analysis, a Pearson Rectified linear unit [21] correlation analysis is conducted. Additionally, a surrogate model is adopted since conventional variance-based sensitivity analysis cannot be applied to the data sets directly [17], [18]. This selection process results in 12 input features.…”

Section: A Data Setsmentioning

confidence: 99%

Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation

Ellefsen

Cheng

Holmeset

et al. 2019

2019 IEEE International Conference on Mechatronics and Automation (ICMA)

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The maritime industry generally anticipates having semi-autonomous ferries in commercial use on the west coast of Norway by the end of this decade. In order to schedule maintenance operations of critical components in a secure and costeffective manner, a reliable prognostics and health management system is essential during autonomous operations. Any remaining useful life prediction obtained from such system should depend on an automatic fault detection algorithm. In this study, an unsupervised reconstruction-based fault detection algorithm is used to predict faults automatically in a simulated autonomous ferry crossing operation. The benefits of the algorithm are confirmed on data sets of real-operational data from a marine diesel engine collected from a hybrid power lab. During the ferry crossing operation, the engine is subjected to drastic changes in operational loads. This increases the difficulty of the algorithm to detect faults with high accuracy. Thus, to support the algorithm, three different feature selection processes on the input data is compared. The results suggest that the algorithm achieves the highest prediction accuracy when the input data is subjected to feature selection based on sensitivity analysis.

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Section: A Data Setsmentioning

confidence: 99%

Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation

Ellefsen

Cheng

Holmeset

et al. 2019

2019 IEEE International Conference on Mechatronics and Automation (ICMA)

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“…However, accurate prediction of ship motions is difficult. Ship motions are non-linear [7], chaotic [8], time-varying [9], and susceptible to environmental factors [10], such as waves [11], currents [12], and winds [13]. The different DoFs are highly coupled [14] [15], which adds to the difficulty of modeling ship dynamics.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

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This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.<div><br></div><div>A major revision is done, compared to the previous version.</div><div><br></div><div>Abstract: Neural networks (NNs) have been applied to predict complex ship motions for stable ship maneuvering and operations at sea, but the coupling effects of different DoFs are still not effectively modeled in black-box NNs. To this end, we propose a novel deep NN framework that explicitly models the coupling effects through factorization machines, and significantly improves the prediction accuracy without introducing much extra complexity. The proposed framework is also lightweight and frequency-aware, which serves real-time and high-frequency prediction with high accuracy. We experimentally demonstrated that the framework is not only friendly to real-time prediction and online learning, but also interpretable and with great potential to be complemented by common machine learning tricks and go deeper.<br></div><div><br></div>

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“…S HIP intelligence aims to make the marine and offshore industries more efficient, innovative, and adaptable to future operations. In fact, ship intelligence has been listed as an important part of the digital agenda, one of the pillars of the European growth strategy [1]. In recent years, interest in development and employment of autonomous ships has increased.…”

Section: Introductionmentioning

confidence: 99%

A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data

Cheng

Ellefsen

et al. 2020

IEEE Trans. Instrum. Meas.

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Sea state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds and costs, respectively. Model-based methods are prone to incorrect estimations due to their high dependency on mathematical models of ships. As previous data-driven studies for sea state estimation only consider wave height and use the motion data from dynamic positioning vessels, this paper introduces a new, deep neural network (SSENET) to estimate sea state in light of both wave height and wave direction, and extends the generality of sensor data from ship motion with forward speed. SSENET is built on the basis of stacked convolutional neural network blocks with dense connections between different blocks, channel attention modules and a feature attention module. The dense connections build shortcut paths between input and all subsequent convolutional blocks, which can make full use of all the hierarchical features from the original time series sensor data. The channel attention modules aim to enhance the features extracted by each convolution block. The feature attention module focuses on combining the feature fusion of hierarchical features in an adaptive manner. Benchmark experiments show the competitive performance against state-ofthe-art approaches. Applying the SSENET on two datasets of zigzag motion for comparative studies shows the effectiveness of the proposed method.

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A Neural-Network-Based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion

Cited by 25 publications

References 38 publications

Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation

Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data

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