Temporal anomaly detection on IIoT-enabled manufacturing

Peng Zhan, Shaokun Wang, Jun Wang, Leigang Qu, Kun Wang, Yupeng Hu () and Xueqing Li
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Peng Zhan: Shandong University
Shaokun Wang: Shandong University
Jun Wang: Shandong University
Leigang Qu: Shandong University
Kun Wang: Shandong University
Yupeng Hu: Shandong University
Xueqing Li: Shandong University

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 6, No 10, 1669-1678

Abstract: 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.

Keywords: Advanced manufacturing; Temporal representation; Anomaly detection (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-021-01768-1

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