Skip to main content
SpringerLink
Log in

A Real-Time Detection Method of System Failure Based on Kalman Filtering

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The quality of information communication is the key guarantee for the normal operation of a system. Information filtering techniques and methods are important for system failure detection. Kalman filtering is a widely used method of optimal estimation as it is easy to computer programming and data can be updated and processed in real time. In this paper, the financial information system is taken for application of Kalman filtering in detecting the failure. Dynamic detecting models are established as the integration of a process model and a discriminant model. The process model is used to describe a dynamic process of financial failure and the discriminant model is used to describe discriminant rules. A real-time detection method is developed based on Kalman filtering and a general n-step-ahead forecasting algorithm is improved in order for the prospective forecast. The dynamic forecasting and detecting process is implemented through computer programming using the data of China’s communications industries. The results have proved the accuracy and advance of detecting financial failure in such case.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Sun, J., & Li, H. (2009). Financial failure prediction using grey case-based reasoning optimized by genetic algorithm. Science Research Management, 30(2), 119–125.

    Google Scholar 

  2. Li, H., Li, C. J., Wu, X. J., et al. (2014). Statistics-based wrapper for feature selection: An implementation on financial failure identification with support vector machine. Applied Soft Computing, 19, 57–67.

    Article  Google Scholar 

  3. Lin, F. Y., Liang, D., Yeh, C. C., et al. (2014). Novel feature selection methods to financial failure prediction. Expert Systems with Applications, 41, 2472–2483.

    Article  Google Scholar 

  4. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45.

    Article  Google Scholar 

  5. Arnold, T., Bertus, M., & Godbey, J. M. (2008). A simplified approach to understanding the Kalman filter technique. Engineering Economist, 53(2), 140–155.

    Article  Google Scholar 

  6. Shi, Y., & Fang, H. Z. (2010). Kalman filter-based identification for systems with randomly missing measurements in a network environment. International Journal of Control, 83(3), 538–551.

    Article  MathSciNet  MATH  Google Scholar 

  7. Li, Y., Rehtanz, C., Ruberg, S., et al. (2012). Assessment and choice of input signals for multiple HVDC and FACTS wide-area damping controllers. IEEE Transactions on Power Systems, 27(4), 1969–1977.

    Article  Google Scholar 

  8. Rincón, F. D., Esposito, M., de Araújo, P. H. H., et al. (2013). Calorimetric estimation employing the unscented Kalman filter for a batch emulsion ploymerization reactor. Macromolecular Reaction Engineering, 7(1), 24–35.

    Article  Google Scholar 

  9. Heij, C., Dijk, D. V., & Grognen, P. J. F. (2011). Real-time macroeconomic forecasting with leading indicators: An empirical comparison. International Journal of Forecasting, 27, 466–481.

    Article  Google Scholar 

  10. Sun, X. L., Tian, Y. Z., & Wang, W. B. (2010). A financial distress state space dynamic model based on Kalman filtering. Journal of Systems and Management, 19(4), 408–414.

    Google Scholar 

  11. Sun, X. L. (2013). Dynamic early warning model based on a financial failure state space: An empirical study in China. China Soft Science, 4, 140–147.

    Google Scholar 

  12. Q. Zhuang, L.H. Chen, (2014). Dynamic prediction of financial failure based on Kalman filtering. Discrete Dynamics in Nature and Society, 1-10.

  13. Li, L. J., Liu, J., & Sun, S. M. (2016). Study on the propagation and control of financial failure on the perception of supply chain. Chinese Journal of Management Science, 24(6), 46–51.

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 71602188), the Key program of National Social Science Foundation of China (Grant: 15ZDB167).

Author information

Authors and Affiliations

  1. School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, 211198, China

    Qian Zhuang

Authors
  1. Qian Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian Zhuang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhuang, Q. A Real-Time Detection Method of System Failure Based on Kalman Filtering. Wireless Pers Commun 102, 1461–1469 (2018). https://doi.org/10.1007/s11277-017-5205-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-5205-0

Keywords

Search

Navigation