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In: Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL, SCITEPRESS

Towards Gaussian Processesfor Automatic and Interpretable Anomaly Detection in Industry 4.0

Fabian Berns¹, Markus Lange-Hegermann, Christian Beecks²
Dec 2020

Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the cur rent challenges are when applying these probabilistic models to large-scale production data.

Literatur Beschaffung: Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL, SCITEPRESS
@inproceedings{2377,
author= {Berns, Fabian and Lange-Hegermann, Markus and Beecks, Christian},
title= {Towards Gaussian Processesfor Automatic and Interpretable Anomaly Detection in Industry 4.0},
abstract= {Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the cur rent challenges are when applying these probabilistic models to large-scale production data.},
booktitle= {Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL},
year= {2020},
month= {Dec},
publisher= {SCITEPRESS},
address= {},
editor= {},
pages= {},
organisation= {},
}

¹ Erstautoren
² Letztautoren