Industrial Internet of Things embedded devices fault detection and classification. A case study

Citation Author(s):
Alberto
Garcés-Jiménez
Department of Computer Science, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
André
Rodrigues
Polytechnic of Coimbra, Coimbra Business School Research Centre, ISCAC, Coimbra, 3040-316, Portugal
José M.
Gómez-Pulido
Department of Computer Science, Universidad de Alcalá, Alcalá de Henares, Spain
Duarte
Raposo
Instituto de Telecomunicações, Aveiro, 3810-193, Portugal
Juan A.
Gómez-Pulido
School of Technology, Universidad de Extremadura, 10003 Cáceres
Jorge
Sá Silva
Institute for Systems Engineering and Computers, INESC, Coimbra, 3030-290, Portugal
Fernando
Boavida
Centre of Informatics and Systems of the University of Coimbra, CISUC, Coimbra, 3030-790, Portugal
Submitted by:
Juan A. Gomez-Pulido
Last updated:
Sat, 12/23/2023 - 14:00
DOI:
10.21227/x2xm-xh92
Data Format:
License:
0
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Abstract 

Industries transition to the Industry 4.0 paradigm requires solutions based on devices attached to machines that allow monitoring and control of industrial equipment. Monitoring is essential to ensure devices' proper operation against different aggressions. We propose a novel approach to detect and classify faults, that are typical in these devices, based on machine learning techniques that use as features the energy, the processing, and the time consumed by device main application functionality. The proposal was validated using a dataset collected from a testbed executing a typical equipment monitoring application. The proposal machine learning pipeline uses a decision tree-based model for fault detection (99.4% accuracy, 99.7% precision, 99.6% recall, 75.2% specificity, and 99.7% F1) followed by a Semi-Supervised Graph-Based model (99.3% accuracy, 96.4% precision, 96.1% recall, 99.6% specificity, and 96.2% F1) for further fault classification. Those results demonstrate that machine learning techniques, based on easily obtainable metrics, help coping with common device faults.

Instructions: 

Instructions for dataset use.

Comments

This is a very rich dataset

Submitted by Akinyemi OYELAKIN on Fri, 03/24/2023 - 04:30