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Authors: Nadeem Iftikhar and Finn Nordbjerg

Affiliation: Centre for Industrial Digital Transformation, University College of Northern Denmark, Aalborg 9200, Denmark

Keyword(s): Equipment Health Indication, Real-Time Monitoring, Sensor Data, Unsupervised Learning, Anomaly Detection.

Abstract: Reducing unplanned downtime requires monitoring of equipment health. This may not be possible in many cases as traditional health monitoring systems often rely on the use of historical data and maintenance information which is not always available, especially for small and medium-sized enterprises. This paper presents a practical approach that uses sensor data for real-time equipment health indication. The methodology proposed consists of a set of steps. It starts with feature engineering which may include feature extraction to transform raw sensor data into a format more suitable for analysis. Anomaly detection follows next, where various techniques are employed to find any deviations in the engineered features indicating potential equipment deterioration or abrupt failures. Then comes the most important stages equipment health indication and alert generation. These stages provide timely information about the equipment’s condition and any necessary interventions. These steps make it possible for such an approach to be effective even when there is little or no historical data available. The applicability of this approach is validated through a lab-based case study. (More)

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Paper citation in several formats:
Iftikhar, N. and Nordbjerg, F. (2024). Real-Time Equipment Health Monitoring Using Unsupervised Learning Techniques. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-707-8; ISSN 2184-285X, SciTePress, pages 401-408. DOI: 10.5220/0012785500003756

@conference{data24,
author={Nadeem Iftikhar and Finn Nordbjerg},
title={Real-Time Equipment Health Monitoring Using Unsupervised Learning Techniques},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA},
year={2024},
pages={401-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012785500003756},
isbn={978-989-758-707-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA
TI - Real-Time Equipment Health Monitoring Using Unsupervised Learning Techniques
SN - 978-989-758-707-8
IS - 2184-285X
AU - Iftikhar, N.
AU - Nordbjerg, F.
PY - 2024
SP - 401
EP - 408
DO - 10.5220/0012785500003756
PB - SciTePress

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