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Articles

Anomaly detection of industrial state quantity time-Series data based on correlation and long short-term memory

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Pages 2048-2065 | Received 21 Mar 2022, Accepted 16 Jun 2022, Published online: 30 Jun 2022

References

  • Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
  • Chaovalit, P., Gangopadhyay, A., Karabatis, G., & Chen, Z. (2011). Discrete wavelet transform-based time series analysis and mining. ACM Computing Surveys, 43(2), 1–37. https://doi.org/10.1145/1883612.1883613
  • Ding, X. O., Yu, S. J., Wang, M. X., Wang, H. Z., Gao, H., & Yang, D. H. (2020). Anomaly detection on industrial time series based on correlation analysis. Ruan Jian Xue Bao/Journal of Software, 31(3), 726–747 (in Chinese). https://doi.org/10.13328/j.cnki.jos.005907
  • Dwivedi, Y., & Subba Rao, S. (2011). A test for second-order stationarity of a time series based on the discrete Fourier transform. Journal of Time Series Analysis, 32(1), 68–91. https://doi.org/10.1111/j.1467-9892.2010.00685.x
  • Enders, W. (2008). Applied econometric time series. John Wiley & Sons.
  • Gao, B., Ma, H. Y., & Yang, Y. H. (2002). Hmms (hidden Markov models) based on anomaly intrusion detection method. In Bo Gao, Hui-Ye Ma, & Yu-Hang Yang (Eds.), Proceedings of the international conference on machine learning and cybernetics, 1 (pp. 381–385).
  • Gao, K., Chang, C.-C., & Liu, Y. (2021). Predicting missing data for data integrity based on the linear regression model. International Journal of Embedded Systems, 14(4), 355–362. https://doi.org/10.1504/IJES.2021.117946
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Huang, H., Mehrotra, K., & Mohan, C. K. (2012). Algorithms for detecting outliers via clustering and ranks. Lecture Notes in Computer science, Proceedings of the International Conference on industrial, engineering, and other Applications of Applied intelligent systems (pp. 20–29). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-31087-4_3.
  • Izakian, H., Pedrycz, W., & Jamal, I. (2015). Fuzzy clustering of time series data using dynamic time warping distance. Engineering Applications of Artificial Intelligence, 39, 235–244. https://doi.org/10.1016/j.engappai.2014.12.015
  • Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2018). LSTM fully convolutional networks for time series classification. IEEE Access, 6(99), 1662–1669. https://doi.org/10.1109/ACCESS.2017.2779939
  • Kolozali, S., Puschmann, D., Bermudez-Edo, M., & Barnaghi, P. (2016). On the effect of adaptive and nonadaptive analysis of time-series sensory data. IEEE Internet of Things Journal, 3(6), 1084–1098. https://doi.org/10.1109/JIOT.2016.2553080
  • Lee, J. (2015). Industrial big data: The revolutionary transformation and value creation in INDUSTRY 4.0 era (B. H. Qiu, Trans.). China Machine Press. (in Chinese).
  • Li, J., Ni, J., & Wang, A. Z. (2017). From big data to intelligent manufacturing. Shanghai Jiao Tong University Press. (in Chinese).
  • Li, J., Pedrycz, W., & Jamal, I. (2017). Multivariate time series anomaly detection: A framework of hidden Markov models. Applied Soft Computing, 60, 229–240. https://doi.org/10.1016/j.asoc.2017.06.035
  • Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., & Hossain, M. S. (2020). Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach. IEEE Internet of Things Journal, 8(8), 6348–6358. https://doi.org/10.1109/JIOT.2020.3011726
  • Liu, Y., Kumar, N., Xiong, Z., Bryan Lim, W. Y., Kang, J., & Niyato, D. (2020). Communication-efficient federated learning for anomaly detection in industrial internet of things. Globecom 2020–2020 IEEE Global Communications Conference, Taipei, Taiwan.
  • Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2015). Long short-term memory networks for anomaly detection in time series. Proceedings of the European symposium on artificial neural networks, Computational intelligence, and machine learning (pp. 89–94).
  • Mehrotra, K. G., Mohan, C. K., & Huang, H. (2017). Anomaly detection principles and algorithms. Springer International Publishing.
  • Münz, G., Li, S., & Carle, G. (2007). Traffic anomaly detection using k-means clustering. Proceedings of the GI/ITG workshop MMBnet (pp. 13–14).
  • Nakamura, T., Taki, K., Nomiya, H., Seki, K., & Uehara, K. (2013). A shape-based similarity measure for time series data with ensemble learning. Pattern Analysis and Applications, 16(4), 535–548. https://doi.org/10.1007/s10044-011-0262-6
  • National Manufacturing Strategy Advisory Committee. (2015). Made in China 2025. Technology road map for key areas (in Chinese).
  • Park, J. W., & Kim, D. Y. (2017). Standard time estimation of manual tasks via similarity measure of unequal scale time series. IEEE Transactions on Human-Machine Systems, 48(3), 241–251. https://doi.org/10.1109/THMS.2017.2759809
  • Qiao, Y., Xin, X. W., Bin, Y., & Ge, S. (2002). Anomaly intrusion detection method based on HMM. Electronics Letters, 38(13), 663–664. https://doi.org/10.1049/el:20020467
  • Ren, H., Ye, Z., & Li, Z. (2017). Anomaly detection based on a dynamic Markov model. Information Sciences, 411, 52–65. https://doi.org/10.1016/j.ins.2017.05.021
  • Shang, F. H., & Sun, D. C. (2010). PLR based on time series tendency turning point. Application Research of Computers, 6, 27.
  • Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics, 14(11), 4724–4734. https://doi.org/10.1109/TII.2018.2852491
  • Van Der Voort, M., Dougherty, M., & Watson, S. (1996). Combining Kohonen maps with Arima time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies, 4(5), 307–318. https://doi.org/10.1016/S0968-090X(97)82903-8
  • Varasteh Yazdi, S. V., & Douzal-Chouakria, A. (2018). Time warp invariant kSVD: Sparse coding and dictionary learning for time series under time warp. Pattern Recognition Letters, 112, 1–8. https://doi.org/10.1016/j.patrec.2018.05.017.
  • Wang, J. M. (2017a). White paper on big data technology and application in China’s industry. Alliance of Industrial Internet (in Chinese).
  • Wang, J. M. (2017b). Summary of industrial big data technology. Big Data Research, 6, 3–14. https://doi.org/10.11959/j.issn.2096-0271.2017057. (in Chinese with English abstract ).
  • Wang, M., Zhang, C., & Yu, J. (2006). Native API based windows anomaly intrusion detection method using SVM. Proceedings of the IEEE International Conference on Sensor Networks 1:6.
  • Wu, S., Liu, Y., Zou, Z., & Weng, T. (2022). S_I_LSTM: Stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 34(1), 44–62. https://doi.org/10.1080/09540091.2021.1940101
  • Zhang, J., Qin, W., Bao, J. S., et al. (2016). Big data in manufacturing industry. Shanghai Science and Technology Press (in Chinese).
  • Zhang, S., Zhang, Z., Chen, Z., Lin, S., & Xie, Z. (2021). A novel method of mental fatigue detection based on CNN and LSTM. International Journal of Computational Science and Engineering, 24(3), 290–300. https://doi.org/10.1504/IJCSE.2021.115656
  • Zhang, X., Fan, P., & Zhu, Z. (2003). A new anomaly detection method based on hierarchical HMM. Proceedings of the 4th International Conference on Parallel and Distributed Computing, Applications, and Technologies (pp. 249–252).