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Journal of Intelligent Manufacturing

Erschienen in:

15.04.2021

Temporal anomaly detection on IIoT-enabled manufacturing

verfasst von: Peng Zhan, Shaokun Wang, Jun Wang, Leigang Qu, Kun Wang, Yupeng Hu, Xueqing Li

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 6/2021

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Abstract

Along with the coming of industry 4.0 era, industrial internet of things (IIoT) plays a vital role in advanced manufacturing. It can not only connect all equipment and applications in manufacturing processes closely, but also provide oceans of sensor data for real-time work-in-process monitoring. Considering the corresponding abnormalities existing in these sensor data sequences, how to effectively implement temporal anomaly detection is of great significance for smart manufacturing. Therefore, in this paper, we proposed a novel time series anomaly detection method, which can effectively recognize corresponding abnormalities within the given time series sequences by standing on the hierarchical temporal representation. Extensive comparison experiments on the benchmark datasets have been conducted to demonstrate the superiority of our method in term of detection accuracy and efficiency on IIOT-enabled manufacturing.

Vorheriger Artikel Multi-product scheduling through process mining: bridging optimization and machine process intelligence

Nächster Artikel Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption

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Titel: Temporal anomaly detection on IIoT-enabled manufacturing
verfasst von: Peng Zhan
Shaokun Wang
Jun Wang
Leigang Qu
Kun Wang
Yupeng Hu
Xueqing Li
Publikationsdatum: 15.04.2021
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 6/2021
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-021-01768-1

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