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An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components

Ellefsen, Andre; Bjørlykhaug, Emil Dale; Æsøy, Vilmar; Zhang, Houxiang
Journal article, Peer reviewed
Published version
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URI
http://hdl.handle.net/11250/2595603
Date
2019
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  • Institutt for havromsoperasjoner og byggteknikk [452]
  • Publikasjoner fra CRIStin - NTNU [21809]
Original version
IEEE Access. 2019, 7 1-9.   10.1109/ACCESS.2019.2895394
Abstract
In recent years, the reliability and safety requirements of ship systems have increased drastically. This has prompted a paradigm shift toward the development of prognostics and health management (PHM) approaches for these systems' critical maritime components. In light of harsh environmental conditions with varying operational loads, and a lack of fault labels in the maritime industry generally, any PHM solution for maritime components should include independent and intelligent fault detection algorithms that can report faults automatically. In this paper, we propose an unsupervised reconstruction-based fault detection algorithm for maritime components. The advantages of the proposed algorithm are verified on five different data sets of real operational run-to-failure data provided by a highly regarded industrial company. Each data set is subject to a fault at an unknown time step. In addition, different magnitudes of random white Gaussian noise are applied to each data set in order to create several real-life situations. The results suggest that the algorithm is highly suitable to be included as part of a pure data-driven diagnostics approach in future end-to-end PHM system solutions.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal
IEEE Access

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