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.
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Acknowledgements
The authors would like to thank the anonymous reviewers and the editors for their insightful comments and suggestions, which are greatly helpful for improving the quality of this paper. This work is supported by the National Natural Science Foundation of China, No.: 62002209; the Natural Science Foundation of Shandong Province, No.: ZR2020QF111; the project of CERNET Innovation (NGII20190109); the project of Qingdao Postdoctoral Applied Research.
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Zhan, P., Wang, S., Wang, J. et al. Temporal anomaly detection on IIoT-enabled manufacturing. J Intell Manuf 32, 1669–1678 (2021). https://doi.org/10.1007/s10845-021-01768-1
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DOI: https://doi.org/10.1007/s10845-021-01768-1