Framework for the Identification of Rare Events via Machine Learning and IoT Networks
Nardelli, Pedro; Papadias, Constantinos; Kalalas, Charalamps; Alves, Hirley; Christou, Ioanns T.; Macaluso, Irene; Marchetti, Nicola; Palacios, Raul; Alonso-Zarate, Jesus (2019-10-21)
Post-print / Final draft
Nardelli, Pedro
Papadias, Constantinos
Kalalas, Charalamps
Alves, Hirley
Christou, Ioanns T.
Macaluso, Irene
Marchetti, Nicola
Palacios, Raul
Alonso-Zarate, Jesus
21.10.2019
International Symposium on Wireless Communication Systems
IEEE
School of Energy Systems
Kaikki oikeudet pidätetään.
© Copyright 2019 IEEE
© Copyright 2019 IEEE
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019111337968
https://urn.fi/URN:NBN:fi-fe2019111337968
Tiivistelmä
This paper introduces an industrial cyber-physical system (CPS) based on the Internet of Things (IoT) that is designed to detect rare events based on machine learning. The framework follows the following three generic steps: (1) Large data acquisition / dissemination: A physical process is monitored by sensors that pre-process the (assumed large) collected data and send the processed information to an intelligent node (e.g., aggregator, central controller); (2) Big data fusion: The intelligent node uses machine learning techniques (e.g., data clustering, neural networks) to convert the received ("big") data to useful information to guide short-term operational decisions related to the physical process; (3) Big data analytics: The physical process together with the acquisition and fusion steps can be virtualized, building then a cyber-physical process, whose dynamic performance can be analyzed and optimized through visualization (if human intervention is available) or artificial intelligence (if the decisions are automatic) or a combination thereof. Our proposed general framework, which relies on an IoT network, aims at an ultra-reliable detection/prevention of rare events related to a pre-determined industrial physical process (modelled by a particular signal). The framework will be process- independent, however, our demonstrated solution will be designed case-by-case. This paper is an introduction to the solution to be developed by the FIREMAN consortium.
Lähdeviite
Nardelli P., Papadias C., Kalalas C., Alves H., Christou I. T., Macaluso I., Marchetti N., Palacios R., Alonso-Zarate J. (2019). Framework for the Identification of Rare Events via Machine Learning and IoT Networks. Published in: 2019 16th International Symposium on Wireless Communication Systems (ISWCS), Oulu, Finland, 27-30 Aug. 2019. DOI: 10.1109/ISWCS.2019.8877287
Kokoelmat
- Tieteelliset julkaisut [1212]