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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References
Abdulla, S., & Hashimy, A. S. A. (2018). Tisefe: Time series evolving fuzzy engine for network traffic classification. International Journal of Communication Networks and Information Security, 10, 116–124.
Breunig, MM., Kriegel, H., Ng, RT., & Sander, J. (2000). LOF: identifying density-based local outliers. In Proceedings of the ACM international conference on management of data (pp. 93–104).
Cheng, Y., Bi, L., Tao, F., & Ji, P. (2020). Hypernetwork-based manufacturing service scheduling for distributed and collaborative manufacturing operations towards smart manufacturing. Journal of Intelligent Manufacturing, 31, 1707–1720.
Dong, L., Wang, P., & Yan, F. (2019). Damage forecasting based on multi-factor fuzzy time series and cloud model. Journal of Intelligent Manufacturing, 30, 521–538.
Guerrero, J. L., Berlanga, A., García, J., & Molina, J. M. (2010). Piecewise linear representation segmentation as a multiobjective optimization problem. In Distributed computing and artificial intelligence (pp. 267–274).
Gupta, M., Gao, J., Aggarwal, CC., & Han, J. (2014). Outlier detection for temporal data. Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool Publishers.
Helman, P., & Bhangoo, J. (1997). A statistically based system for prioritizing information exploration under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics, 27, 449–466.
Hsu, C., & Liu, W. (2021). Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing. Journal of Intelligent Manufacturing, 32, 823–836.
Huang, S., Guo, Y., Yang, N., Zha, S., Liu, D., & Fang, W. (2020). A weighted fuzzy c-means clustering method with density peak for anomaly detection in iot-enabled manufacturing process. Journal of Intelligent Manufacturing, 1–17.
Hu, Y., Ji, C., Zhang, Q., Chen, L., Zhan, P., & Li, X. (2020a). A novel multi-resolution representation for time series sensor data analysis. Soft Computing, 24, 10535–10560.
Hu, Y., Ren, P., Luo, W., Zhan, P., & Li, X. (2019). Multi-resolution representation with recurrent neural networks application for streaming time series in iot. Computer Networks, 152, 114–132.
Hu, Y., Zhan, P., Xu, Y., Zhao, J., Li, Y., & Li, X. (2020b). Temporal representation learning for time series classification. Neural Computing and Applications, 32, 1–14.
Keogh, E., & Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. In Proceedings of the international conference on knowledge discovery and data mining (pp. 24–30).
Keogh, E., Lin, J., & Fu, AW. (2005). HOT SAX: Efficiently finding the most unusual time series subsequence. In Proceedings of the international conference on data mining (pp. 226–233).
Keogh, E., Chakrabarti, K., Pazzani, M. J., & Mehrotra, S. (2001). Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 3, 263–286.
Keogh, E., Lin, J., Fu, A. W., & Herle, H. V. (2006). Finding unusual medical time-series subsequences: Algorithms and applications. IEEE Transactions Information Technology in Biomedicine, 10, 429–439.
Kha, N. H., & Anh, D. T. (2015). From cluster-based outlier detection to time series discord discovery. In Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (pp. 16–28).
Leng, M., Yu, W., Wu, S., & Hu, H. (2013). Anomaly detection algorithm based on pattern density in time series. In Proceedings of the emerging technologies for information systems, computing, and management (pp. 305–311).
Luo, W., Li, Y., Yao, F., Wang, S., Li, Z., Zhan, P., & Li, X. (2020). Multi-resolution representation for streaming time series retrieval. International Journal of Pattern Recognition and Artificial Intelligence, 1–18.
Pang, J., Liu, D., Peng, Y., & Peng, X. (2018). Intelligent pattern analysis and anomaly detection of satellite telemetry series with improved time series representation. Journal of Intelligent and Fuzzy Systems, 34, 3785–3798.
Ren, H., Liao, X., Li, Z., & Al-Ahmari, A. (2018). Anomaly detection using piecewise aggregate approximation in the amplitude domain. Applied Intelligence, 48, 1097–1110.
Sellami, C., Miranda, C., Samet, A., Tobji, M. A. B., & Beuvron, Fd Bd. (2020). On mining frequent chronicles for machine failure prediction. Journal of Intelligent Manufacturing, 31, 1019–1035.
Tsay, R. S., Pena, D., & Pankratz, A. E. (2000). Outliers in multivariate time series. Biometrika, 87, 789–804.
Wei, L., Keogh, E., & Xi, X. (2006). Saxually explicit images: Finding unusual shapes. In Proceedings of the IEEE international conference on data mining (pp. 711–720).
Xue, J., Zhou, S., Liu, Q., Liu, X., & Yin, J. (2018). Financial time series prediction using rf-elm. Neurocomputing, 277, 176–186.
Yamanishi, K., Takeuchi, J., Williams, G., & Milne, P. (2004). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery, 8, 275–300.
Yang, H., Kumara, S., Bukkapatnam, S., & Fg, Tsung. (2019). The internet of things for smart manufacturing: A review. Institute of Industrial and Systems Engineers Transactions, 51, 1190–1216.
Zhan, P., Sun, C., Hu, Y., Luo, W., Zheng, J., & Li, X. (2020). Feature-based online representation algorithm for streaming time series similarity search. International Journal of Pattern Recognition and Artificial Intelligence, 34, 1–25.
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