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Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings

Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, Rüdiger and Stökler, Steffen and Allgaier, Johannes and Reichert, Manfred (2019) Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings. Sensors, 19 (24). ISSN 1424-8220

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Official URL: https://www.mdpi.com/1424-8220/19/24/5370

Abstract

To build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.

Item Type:Article
Subjects:DBIS Research > Publications
ID Code:1840
Deposited By: Klaus Kammerer
BibTex Export:BibTeX
Deposited On:06 Dec 2019 14:17
Last Modified:06 Dec 2019 14:17

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