Anomaly Detection for Industrial Big Data

Neil Caithness, David Wallom

Abstract

As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing ‘volume, velocity and variety’ (i.e. Industrial Big Data. An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by Senseye.io, Prognostics - The Future of Condition Monitoring, and output of the NASA Prognostics Center of Excellence (PCoE)). However, as noted by others, our ability to collect “big data” has greatly surpassed our capability to analyze it. In order to fully utilize the potential of Industrial Big Data we need data-driven techniques that operate at scales that process models cannot. Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale. The method generalizes to application with almost any multivariate data set based on independent ordinations of repeated (bootstrapped) partitions of the data set and inspection of the joint distribution of ordinal distances.

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Paper Citation


in Harvard Style

Caithness N. and Wallom D. (2018). Anomaly Detection for Industrial Big Data.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 285-293. DOI: 10.5220/0006835502850293


in Bibtex Style

@conference{data18,
author={Neil Caithness and David Wallom},
title={Anomaly Detection for Industrial Big Data},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={285-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006835502850293},
isbn={978-989-758-318-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Anomaly Detection for Industrial Big Data
SN - 978-989-758-318-6
AU - Caithness N.
AU - Wallom D.
PY - 2018
SP - 285
EP - 293
DO - 10.5220/0006835502850293