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Cited by 29Anomaly Detection Techniques and ApplicationsFault Detection and Control SystemsIoT and Edge/Fog Computing

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

A TinyML approach to non-repudiable anomaly detection in extreme industrial environments

Antonini, Mattia, Pincheira, Miguel, Vecchio, Massimo, Antonelli, Fabio·2022·2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)
DOI 10.1109/metroind4.0iot54413.2022.9831517
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Abstract

Unforeseen failures of industrial assets may lead to unexpected downtime with a huge impact on critical business processes. Therefore, modern assets usually include several embedded sensors and processing units, allowing to monitor certain operational parameters, i.e., Condition Monitoring. The sensed data can be later analyzed using Machine Learning (ML) approaches to detect anomalies and anticipate failures. Furthermore, the Internet of Things can provide the tools to extend Condition Monitoring to legacy assets that do not have onboard sensing capabilities. In general, these IoT devices offer the opportunity to move ML processing closer to the monitored asset, thus reducing costs and simplifying the anomaly detection system. However, extreme industrial environments present harsh operating conditions and limited resources, further exacerbated by the reduced computation capabilities of most IoT devices. This paper proposes an ML-based anomaly detection system that uses a retrofitting kit based on a constrained and cost-effective IoT device. Despite its limited resources, the latter executes a state-of-the-art unsupervised anomaly detection algorithm locally, autonomously learning the normality behavior of the monitored asset. Furthermore, to improve the transparency of the monitoring process, we propose to leverage blockchain technology as a non-repudiable repository of information, also assessing the impact of such implementation choice in terms of costs and overhead.

Overview

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Registry

Crossref · 10.1109/metroind4.0iot54413.2022.9831517

Cites

14 of 14 references verified0 citation errors · 0 not checkable

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This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Cloud provides an ML model to detect machine's abnormal behavior.

    “Cloud provides an ML model to detect machine's abnormal behavior.”

    From A Comprehensive Survey on TinyML (2023) · cites this paper as [134]

  • The study in [134] used cloud computing to monitor the health condition of machines using vibration signals.

    “The study used cloud computing to monitor the health condition of machines using vibration signals.”

    From A Comprehensive Survey on TinyML (2023) · cites this paper as [134]

  • An example of cloud based anomaly detection is condition monitoring [134].

    “An example of cloud based anomaly detection is condition monitoring [134].”

    From A Comprehensive Survey on TinyML (2023) · cites this paper as [134]

Citations

Cites

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Research works printed but not linked: 1

  1. 2011, Signalling on low-voltage electrical installations in the frequency range 3 kHz to 148,5 kHz - Part 1: General requirements, frequency bands and electromagnetic disturbances, European Committee for Electrotechnical Standardization Standard

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Metadata

Authors
Antonini, M., Pincheira, M., Vecchio, M., & Antonelli, F.
Affiliations
Fondazione Bruno Kessler (IT)
ORCID
Venue
2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)
Publisher
IEEE
Published
2022-06-07
Pages
397-402
DOI
10.1109/metroind4.0iot54413.2022.9831517
OpenAlex
W4286572030
Cited by
28
References
14
Citations by year
2026: 7 · 2025: 7 · 2024: 6 · 2023: 7 · 2022: 1
FWCI
2.90
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) (publishedVersion)
  • IRIS eCampus Telematic University (Università degli Studi eCampus) (submittedVersion)
Funders
European Union
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords
TinyML, anomaly detection, condition monitoring, Industrial Internet of Things, blockchain, unsupervised learning, edge computing, resource-constrained devices, predictive maintenance, Internet of Things

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