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A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data

Abstract

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 data-driven methods to optimize operation also increases. Considering ship motion data reflects the dynamic positioning performance of the vessels and thruster failure might cause drift-offs, it is possible to detect and isolate potential thruster failure using motion data. In this paper, thruster failure detection and isolation are considered as a time series classification problem. A convolutional neural network (CNN) is introduced to learn the mapping from the logged motion sequence to the status of the thruster. CNN is expected to generate task-specific features from the original time series sensors data and then perform the classification. The dataset is collected from a professional simulator in the Offshore Simulation Centre AS. Experiments show that the proposed method can detect and isolate failed thrusters with up to 95% accuracy. The proposed model is further extended to deal with thruster failure in a realtime manner.
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Category

Academic article

Language

English

Author(s)

  • Peihua Han
  • Guoyuan Li
  • Robert Skulstad
  • Stian Skjong
  • Houxiang Zhang

Affiliation

  • SINTEF Ocean / Fisheries and New Biomarine Industry
  • Norwegian University of Science and Technology

Year

2021

Published in

IEEE Transactions on Instrumentation and Measurement

ISSN

0018-9456

Volume

70

View this publication at Norwegian Research Information Repository