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Mobile and Ubiquitous Systems: Computing, Networking and Services. 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 14–17, 2023, Proceedings, Part I

Research Article

FaultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-63989-0_5,
        author={Koustabh Dolui and Ashok Samraj Thangarajan and Sergii Morshchavka and Zhaoyi Liu and Sam Michiels and Danny Hughes},
        title={FaultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 14--17, 2023, Proceedings, Part I},
        proceedings_a={MOBIQUITOUS},
        year={2024},
        month={7},
        keywords={Internet of Things Wireless Sensor Networks Machine Learning Signal Processing Energy awareness},
        doi={10.1007/978-3-031-63989-0_5}
    }
    
  • Koustabh Dolui
    Ashok Samraj Thangarajan
    Sergii Morshchavka
    Zhaoyi Liu
    Sam Michiels
    Danny Hughes
    Year: 2024
    FaultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-031-63989-0_5
Koustabh Dolui1,*, Ashok Samraj Thangarajan1, Sergii Morshchavka1, Zhaoyi Liu1, Sam Michiels1, Danny Hughes1
  • 1: imec-Distrinet, Department of Computer Science
*Contact email: koustabh.dolui@kuleuven.be

Abstract

The timely monitoring and maintenance of industrial machines is critical to prevent expensive disruptions and down-time. The Internet of Things (IoT) offers a solution to instrument production processes at lower cost and complexity. However, wireless IoT networks are bandwidth constrained, which precludes the transmission of high frequency signals such as vibration or electrical current. Prior approaches to tackling this problem require a high degree of application-specific re-engineering. In this paper, we argue for a new approach to fault detection that is accurate, efficient and applicable to a large class of fault detection problems. We propose FaultBit, an application independent toolkit for fault classification that can gather, compress and classify data using IoT networks. We evaluate FaultBit in two representative scenarios using current and vibration data. In both cases, FaultBit offers classification accuracy 99%, which is very close to application-specific classifiers, while requiring 512(\times )less bandwidth, enabling a battery life of several years.

Keywords
Internet of Things Wireless Sensor Networks Machine Learning Signal Processing Energy awareness
Published
2024-07-19
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-63989-0_5
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