Abstract
With the development of industrial Internet technology, more and more devices are brought into the industrial big data platform. To improve the efficiency of device maintenance, the industrial big data platform needs to monitor the abnormal data of the device. However, most of the current anomaly detection algorithms are offline and they can’t be updated in real-time. To solve this problem, this paper proposes an anomaly detection model for the industrial stream. The model realizes anomaly detection by cooperatively calling 3 σ and DBSCAN algorithm. The model has the advantages of low cost, fast speed, and easy to use. On this basis, this paper presents a real-time update strategy for this model, which further improves the accuracy of the model. The experimental results of water pump equipment monitoring data show the effectiveness of this method.
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References
Lee, J.: Industrial Big Data. Mechanical Industry Press (2015)
Hu, X., Tang, X., Hu, R.: Modern Detection Technology and System. China Machine Press, Beijing (2015). (in Chinese)
Zhou, D., Ding, X.: Theory and applications of fault tolerant control. Acta Autom. Sin. 26(6), 788–797 (2000)
Ji, C., Shao, Q., Sun, J., et al.: Device data ingestion for industrial big data platforms with a case study. Sensors 16(3), 279 (2016)
Ester, M., Kriegel, H.P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226–231 (1996)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)
Wu, J.F., Jin, W., Tang, P.: Survey on monitoring techniques for data abnormalities. Comput. Sci. 44(S2), 24–28 (2017)
Toledano, M., Cohen, I., Ben-Simhon, Y., et al.: Real-time anomaly detection system for time series at scale. In: KDD 2017 Workshop on Anomaly Detection in Finance, pp. 56–65 (2018)
Liu, Y., Yao, K.T., Liu, S., et al.: System and method for failure prediction for rod pump artificial lift systems. U.S. Patent 8,988,236, 24 March 2015
Rençberoğlu, E.: Fundamental techniques of feature engineering for machine learning. Towards Data Sci. (2019)
Gupta, M., Gao, J., Aggarwal, C., et al.: Outlier detection for temporal data. Synth. Lect. Data Min. Knowl. Discov. 5(1), 1–129 (2014)
Gao, C.: Network traffic anomaly detection based on industrial control network. Beijing University of Technology (2014)
Xu, C., Lin, T.: Improved K-means algorithm based on anomaly detection. Softw. Guide 18(4), 74–78 (2019)
Fujun, J., Min, C., Lei, W., et al.: SVM based energy consumption abnormality detection in ARM system. Electr. Measur. Instrum. 51(6), 64–69 (2014)
Chen, Y.: Research on time series anomaly detection based on similarity analysis. Southwest Jiaotong University (2011). (in Chinese)
Ding, G., Sun, S.: Research on outlier detection algorithm based on dynamic rule constraint. Technol. Inf. (008), 41–42 (2019). (in Chinese)
Wickham, H.: Tidy data. J. Stat. Softw. 59(10), 1–23 (2014)
Ji, C., Zou, X., Liu, S., et al.: ADARC: an anomaly detection algorithm based on relative outlier distance and biseries correlation. Softw. Pract. Experience 50(11), 2065–2081 (2019). https://doi.org/10.1002/spe.2756
Ding, X.O., Yu, S.J., Wang, M.X., Wang, H.Z., Gao, H., Yang, D.H.: Anomaly detection on industrial time series based on correlation analysis. J. Softw. 31(3), 726–747 (2020). (in Chinese)
McMahan, B.: Follow-the-regularized-leader and mirror descent: equivalence theorems and L1 regularization. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 525–533 (2011)
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Wang, T., Yuan, P., Ji, C., Liu, S. (2021). Cooperative Anomaly Detection Model and Real-Time Update Strategy for Industrial Stream Data. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_23
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DOI: https://doi.org/10.1007/978-981-16-2540-4_23
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