Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems Article Swipe
Manuel Goncalves
,
Pedro Sousa
,
Jérôme Mendes
,
Morad Danishvar
,
Alireza Mousavi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3173376
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3173376
Extracting key system parameters and their impact on state transition is a necessity for \nknowledge and data engineering. In Decision Support Systems, the quest for yet more efficient and faster \nmethods of sensitivity analysis (SA) and feature extraction in complex and volatile systems persists. A new \nimproved event tracking methodology, the fastTracker, for real-time SA in large scale complex systems \nis proposed in this paper. The main feature of fastTracker is its high-frequency analytics using meager \ncomputational cost. It is suitable for data processing and prioritization in embedded systems, Internet \nof Things (IoT), distributed computing (e.g. Edge computing) applications. The presented algorithm’s \nunderpinning rationale is event driven; its objective is to correctly and succinctly quantify the sensitivity of \nobservable changes in the system (output) with respect to the input variables. To demonstrate the performance \nof the proposed fastTracker methodology, fastTracker was deployed in the Supervisory control and data \nacquisition (SCADA) system from real cement industry. fastTracker has been verified by system experts in \nreal industrial application. Its performance was compared with other real-time event-based SA techniques. \nThe comparison revealed savings of 98.8% in processing time per sensitivity index and 20% in memory \nusage when compared with EventTracker, its closest rival. The proposed methodology is more accurate and \n80.9% faster than an entropy-based method. Its application is recommended for reinforced learning and/or \nformulating system key performance indicators from raw data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2022.3173376
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09770808.pdf
- OA Status
- gold
- Cited By
- 11
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285274173
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285274173Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2022.3173376Digital Object Identifier
- Title
-
Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA SystemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Manuel Goncalves, Pedro Sousa, Jérôme Mendes, Morad Danishvar, Alireza MousaviList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2022.3173376Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09770808.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09770808.pdfDirect OA link when available
- Concepts
-
SCADA, Computer science, Event (particle physics), Embedded system, Artificial intelligence, Real-time computing, Engineering, Physics, Quantum mechanics, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 5, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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