Reliability improvement of the dredging perception system: A sensor fault-tolerant strategy

Bin Wang, Enrico Zio, Xiuhan Chen, Hanhua Zhu, Yunhua Guo, Shidong Fan*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In the dredging industry, the automation and accuracy of the Dredging Perception System (DPS) are vital for operational efficiency and environmental safety. Current DPS implementations face challenges with sensor fault tolerance, leading to system unreliability and increased false alarm rates that can disrupt dredging operations. We propose a Hybrid Redundancy Sensor Fault Tolerance (HRSFT) strategy that integrates matching physical sensors (PS) with two distinct types of virtual sensors (VS) driven by multi-sensor association and time-series prediction models. The HRSFT employs a voting-cold storage strategy to address the false alarm issues commonly associated with single virtual sensor systems. Through experimental validation, the HRSFT strategy has demonstrated its capability to provide accurate replacement information during both single and multi-sensor failure scenarios, effectively managing abnormal sensor data and enhancing the operational reliability of the DPS. The implementation of the HRSFT strategy significantly improves the accuracy and stability of the DPS, suggesting a substantial advancement in sensor fault tolerance that could be applied to similar systems in various industries, leading to safer and more reliable operations.

Original languageEnglish
Article number110134
Number of pages15
JournalReliability Engineering and System Safety
Volume247
DOIs
Publication statusPublished - 2024

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Cutter suction dredger (CSD)
  • Data fusion
  • Dredging perception system
  • Fault tolerant (FT)
  • Reliability
  • Sensor-fault detection, isolation and accommodation (SFDIA)

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