D. Gutierrez-Rojas et al., "Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning," 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 2020, pp. 250-256, doi: 10.1109/ICPS48405.2020.9274780
Three-layer approach to detect anomalies in industrial environments based on machine learning
|Author:||Gutierrez-Rojas, Daniel1; Ullah, Mehar1; Christou, Ioannis T.2;|
1LUT University, Finland
2Athens Information Technology, Greece
3Federal University of Minas Gerais, Brazil
46G Flagship, University of Oulu, Finland
5Trinity College Dublin, Ireland
6Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202102195378
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-02-19
This paper introduces a general approach to design a tailored solution to detect rare events in different industrial applications based on Internet of Things (IoT) networks and machine learning algorithms. We propose a general framework based on three layers (physical, data and decision) that defines the possible designing options so that the rare events/anomalies can be detected ultra-reliably. This general framework is then applied in a well-known benchmark scenario, namely Tennessee Eastman Process. We then analyze this benchmark under three threads related to data processes: acquisition, fusion and analytics. Our numerical results indicate that: (i) event-driven data acquisition can significantly decrease the number of samples while filtering measurement noise, (ii) mutual information data fusion method can significantly decrease the variable spaces and (iii) quantitative association rule mining method for data analytics is effective for the rare event detection, identification and diagnosis. These results indicates the benefits of an integrated solution that jointly considers the different levels of data processing following the proposed general three layer framework, including details of the communication network and computing platform to be employed.
|Pages:||250 - 256|
2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)
IEEE Conference on Industrial Cyberphysical Systems
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported by CHIST-ERA (call 2017) via FIREMAN consortium, which is funded by the following national foundations: Academy of Finland (n. 326270, n. 326301), Irish Research Council, and the Spanish Government under grant PCI2019-103780. This work is partially funded by Academy of Finland 6Genesis Flagship (n. 318927), ee-IoT (n.319009) and EnergyNet Research Fellowship (n.321265/n.328869). and was supported in part by the Research Grant from Science Foundation Ireland and the European Regional Development Fund under Grant 13/RC/2077 and the Catalan Government under grant 2017-SGR-891.
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