In modern manufacturing, each stage of industrial processes is accurately measured via multiple sensors and, consequently, a large amount of data is made available for analytics, monitoring and control purposes. A possible use of such data is to detect anomalies in order to prevent potential damages and hazards. In this paper, we will consider a sensor setup returning distributed time series measurements that can be used for failure identification. In particular, an anomaly detection strategy based on Vector Autoregressive (VAR) modeling for multivariate time series will be presented and analyzed in detail. The effectiveness of the proposed methodology will be assessed on experimental data from a real industrial case study.

Anomaly detection via distributed sensing: A VAR modeling approach

Abbracciavento F.;Formentin S.;Savaresi S. M.
2021

Abstract

In modern manufacturing, each stage of industrial processes is accurately measured via multiple sensors and, consequently, a large amount of data is made available for analytics, monitoring and control purposes. A possible use of such data is to detect anomalies in order to prevent potential damages and hazards. In this paper, we will consider a sensor setup returning distributed time series measurements that can be used for failure identification. In particular, an anomaly detection strategy based on Vector Autoregressive (VAR) modeling for multivariate time series will be presented and analyzed in detail. The effectiveness of the proposed methodology will be assessed on experimental data from a real industrial case study.
IFAC-PapersOnLine
Applications of FDI and FTC
Fault detection and diagnosis
Modeling of manufacturing operations
Parameter estimation based methods for FDI
Time series modeling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1209178
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