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Correlation Coefficient Based Cluster Data Preprocessing and LSTM Prediction Model for Time Series Data in Large Aircraft Test Flights

  • Hanlin Zhu
  • Yongxin Zhu
  • Di Wu
  • Hui Wang
  • Li Tian
  • Wei Mao
  • Can Feng
  • Xiaowen Zha
  • Guobao Deng
  • Jiayi Chen
  • Tao Liu
  • Xinyu Niu
  • Kuen Hung Tsoi
  • Wayne Luk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)

Abstract

The Long Short-Term Memory (LSTM) model has been applied in recent years to handle time series data in multiple application domains, such as speech recognition and financial prediction. While the LSTM prediction model has shown promise in anomaly detection in previous research, uncorrelated features can lead to unsatisfactory analysis result and can complicate the prediction model due to the curse of dimensionality. This paper proposes a novel method of clustering and predicting multidimensional aircraft time series. The purpose is to detect anomalies in flight vibration in the form of high dimensional data series, which are collected by dozens of sensors during test flights of large aircraft. The new method is based on calculating the Spearman’s rank correlation coefficient between two series, and on a hierarchical clustering method to cluster related time series. Monotonically similar series are gathered together and each cluster of series is trained to predict independently. Thus series which are uncorrelated or of low relevance do not influence each other in the LSTM prediction model. The experimental results on COMAC’s (Commercial Aircraft Corporation of China Ltd) C919 flight test data show that our method of combining clustering and LSTM model significantly reduces the root mean square error of predicted results.

Keywords

Cluster Time series Correlation coefficient LSTM 

Notes

Acknowledgment

This work is partially supported by National Key Research & Development Program of China (2017YFA0206104), Shanghai Municipal Science and Technology Commission and Commercial Aircraft Corporation of China, Ltd. (COMAC) (175111105000), Shanghai Municipal Science and Technology Commission (18511111302, 18511103502), Key Foreign Cooperation Projects of Bureau of International Co-operation Chinese Academy of Sciences (184131KYSB20160018) and UK EPSRC (EP/L016796/1, EP/N031768/1 and EP/P010040/1).

References

  1. 1.
    Cao, Z., Zhu, Y., et al.: Improving prediction accuracy in LSTM network model for aircraft testing flight data. In: IEEE International Conference on Smart Cloud (2018)Google Scholar
  2. 2.
    Hsu, H., Hsieh, C.: Feature selection via correlation coefficient clustering. J. Softw. 5(12), 1371–1377 (2010)CrossRefGoogle Scholar
  3. 3.
    Gauthier, T.: Detecting trends using spearman’s rank correlation coefficient. Environ. Forensics 2, 359–362 (2001)CrossRefGoogle Scholar
  4. 4.
    Nanduri, A., Sherry, L.: Anomaly detection in aircraft data using recurrent neural networks. In: Integrated Communications Navigation and Surveillance (ICNS) Conference (2016)Google Scholar
  5. 5.
    Grabusts, P., Borisov, A.: Clustering methodology for time series mining. Sci. J. Riga Tech. Univ. 40(1), 81–86 (2009)Google Scholar
  6. 6.
    Singhal, A., Seborg, D.: Clustering multivariate time-series data. J. Chemom. 19, 427–438 (2005)CrossRefGoogle Scholar
  7. 7.
    Funie, A.-I., Grigoras, P., Burovskiy, P., Luk, W., Salmon, M.: Run-time reconfigurable acceleration for genetic programming fitness evaluation in trading strategies. J. Signal Process. Sys. 90(1), 39–52 (2018)CrossRefGoogle Scholar
  8. 8.
    Gai, K., Qiu, M., Zhao, H., et al.: Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. J. Netw. Comput. Appl. 59, 46–54 (2016)CrossRefGoogle Scholar
  9. 9.
    Bara, A., Niu, X., Luk, W.: A dataflow system for anomaly detection analysis. In: International Conference on Field Programmable Technology (2014)Google Scholar
  10. 10.
    Graves, A.: Generating sequences with recurrent neural networks. https://arxiv.org/abs/1308.0850
  11. 11.
    Cui, L., Luo, Y., Li, G., Lu, N.: Artificial bee colony algorithm with hierarchical groups for global numerical optimization. In: Qiu, M. (ed.) SmartCom 2016. LNCS, vol. 10135, pp. 72–85. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-52015-5_8CrossRefGoogle Scholar
  12. 12.
    Gai, K., Qiu, M., Liu, M., Zhao, H.: Smart resource allocation using reinforcement learning in content-centric cyber-physical systems. In: Qiu, M. (ed.) SmartCom 2017. LNCS, vol. 10699, pp. 39–52. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-73830-7_5CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hanlin Zhu
    • 1
  • Yongxin Zhu
    • 1
  • Di Wu
    • 1
  • Hui Wang
    • 1
  • Li Tian
    • 1
  • Wei Mao
    • 2
  • Can Feng
    • 2
  • Xiaowen Zha
    • 2
  • Guobao Deng
    • 2
  • Jiayi Chen
    • 2
  • Tao Liu
    • 2
  • Xinyu Niu
    • 3
  • Kuen Hung Tsoi
    • 3
  • Wayne Luk
    • 4
  1. 1.Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChina
  2. 2.Commercial Aircraft Corporation of China Ltd.ShanghaiChina
  3. 3.Shenzhen Corerain Technologies Co. Ltd.ShenzhenChina
  4. 4.Imperial College LondonLondonUK

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