Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/103294
Title: | Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems | Authors: | Goncalves, Manuel Sousa, Pedro Mendes, Jérôme Amaro Pires Danishvar, Morad Mousavi, Alireza |
Keywords: | Event Tracking; sensitivity analysis (SA); discrete event systems; input variable selection; real-time systems; distributed computing | Issue Date: | 2022 | Project: | CENTRO-01-0247-FEDER-069730 UIDB/00048/2020 |
Serial title, monograph or event: | IEEE Access | Volume: | 10 | Abstract: | Extracting key system parameters and their impact on state transition is a necessity for knowledge and data engineering. In Decision Support Systems, the quest for yet more efficient and faster methods of sensitivity analysis (SA) and feature extraction in complex and volatile systems persists. A new improved event tracking methodology, the fastTracker, for real-time SA in large scale complex systems is proposed in this paper. The main feature of fastTracker is its high-frequency analytics using meager computational cost. It is suitable for data processing and prioritization in embedded systems, Internet of Things (IoT), distributed computing (e.g. Edge computing) applications. The presented algorithm’s underpinning rationale is event driven; its objective is to correctly and succinctly quantify the sensitivity of observable changes in the system (output) with respect to the input variables. To demonstrate the performance of the proposed fastTracker methodology, fastTracker was deployed in the Supervisory control and data acquisition (SCADA) system from real cement industry. fastTracker has been verified by system experts in real industrial application. Its performance was compared with other real-time event-based SA techniques. The comparison revealed savings of 98.8% in processing time per sensitivity index and 20% in memory usage when compared with EventTracker, its closest rival. The proposed methodology is more accurate and 80.9% faster than an entropy-based method. Its application is recommended for reinforced learning and/or formulating system key performance indicators from raw data. | URI: | https://hdl.handle.net/10316/103294 | ISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2022.3173376 | Rights: | openAccess |
Appears in Collections: | I&D ISR - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Real-Time_Event-Driven_Learning_in_Highly_Volatile_Systems_A_Case_for_Embedded_Machine_Learning_for_SCADA_Systems.pdf | 6.71 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
9
checked on Aug 12, 2024
WEB OF SCIENCETM
Citations
6
checked on Aug 2, 2024
Page view(s)
107
checked on Aug 13, 2024
Download(s)
67
checked on Aug 13, 2024
Google ScholarTM
Check
Altmetric
Altmetric
This item is licensed under a Creative Commons License