Identification of Overtemperature Disturbances in Industrial Food Refrigeration Processes

Pursche, Thomas; Grabowski, Niclas; Nowitzki, Jan; Claub, Roland; Patryarcha, Lukas; Dreisbach, Heiko; Tibken, Bernd

doi:10.23919/sice.2018.8492663

Cited by 2 publications

(1 citation statement)

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“…Due to advances in digitalization and the growing market in the smart manufacturing sector, most plants and machinery are now equipped with new sensor technology. The possible use cases of sensors can vary greatly: in the aluminum-production industry, sensors are used to monitor the state of reduction cells [1] or to measure individual anode currents [2] while in the food-refrigeration industry sensor data are analyzed to predict over-temperature disturbances [3]. To gain new insights about a system, the sensor data have to be preprocessed, analyzed and visualized in a compact way.…”

Section: Introductionmentioning

confidence: 99%

Information Extraction from Industrial Sensor Data Using Time Series Meta-Features

Grabowski

Kremser²,

Düssel

et al. 2023

Applied Sciences

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In the smart manufacturing sector, analyzing time series data is essential for monitoring plants and machinery to prevent costly failures or shutdowns. In order to gain new insights and make better control decisions, new methods are needed for extracting information and interpreting sensor data from hundreds of systems. In this paper, we present an approach for visualizing and interpreting sensor data from TRIMET Aluminium SE Essen (TAE) using time series meta-features and principal component analysis (PCA). We describe our general approach of generating multiple two-dimensional feature spaces to identify salient and implausible sensor data. Using a set of 20 time series meta-features, we applied our approach to sensor data from TAE which were generated by thermocouples. Each step of the approach was integrated into a dashboard to ensure a user-friendly and approachable interaction in finding salient and implausible sensor data.

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Section: Introductionmentioning

confidence: 99%