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A Multi-mode Incipient Sensor Fault Detection and Diagnosis Method for Electrical Traction Systems

  • Hongtian Chen
  • Bin Jiang
  • Ningyun Lu
Regular Papers Control Theory and Applications
  • 106 Downloads

Abstract

This paper proposes a data-driven sensor fault detection and diagnosis (FDD) method for electrical traction systems. Considering their switched characteristics, electrical traction systems can be regarded as switched systems. A mixture non-Gaussian data set will be formed, which can be firstly divided into six different operation modes, and principal component analysis (PCA) is then used for feature extraction in each mode. For two fault indicators in principal and residual subspaces, their probability density functions (PDFs) are estimated and used to determine reasonable thresholds for FDD. The proposed methodology extends the application of multivariate statistical technology to electrical traction systems. It can be applied easily and effectively without requirements on system parameters, and can deal with incipient sensor faults in traction system. Experiments with several different types of incipient sensor faults are conducted, which can demonstrate the effectiveness of the proposed method.

Keywords

Electrical traction system fault detection and diagnosis (FDD) incipient sensor fault multi-mode non-Gaussian signal 

Nomenclature

Rn× m

The set of all n×m real matrix

XRN×m

Matrix with N observations from m sensors

M—i

The i-th mode

P

Loading matrix in principal subspace

Loading matrix in residual subspace

Λpc

Eigenvalue matrix in principal subspace

Λres

Eigenvalue matrix in residual subspace

l

The number of retained principal components

f(x)

Probability density function (PDF) of x

\({J_{{T^2}}}\), JSPE

Thresholds for T2 and SPE

K(x)

Kernel function of x

X2

Chi-square distribution

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References

  1. [1]
    S. X. Ding, Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, Springer, Berlin, 2008.Google Scholar
  2. [2]
    X. Jin, G. Yang, and W. Che, “Adaptive synchronization of master-slave large-scale systems against bias sctuators and network attenuations,” International Journal of Control, Automation, and Systems, vol. 10, no. 6, pp. 1102–1110, Dec. 2012.CrossRefGoogle Scholar
  3. [3]
    X. Li and G. Yang, “Adaptive fault detection and isolation approach for actuator stuck faults in closed-loop systems,” International Journal of Control, Automation, and Systems, vol. 10, no. 4, pp. 830–834, Aug. 2012.CrossRefGoogle Scholar
  4. [4]
    J. Dong and G. Yang, “Reliable state feedback control of T-S fuzzy systems with sensor faults,” IEEE Trans. Fuzzy Syst., vol. 23, no. 2, pp. 421–433, Apr. 2015.MathSciNetCrossRefGoogle Scholar
  5. [5]
    M. S. Ballal, H. M. Suryawanshi, and M. K. Mishra, “Detection of incipient faults in induction motors using FIS, ANN and ANFIS techniques,” J. Power Electron., vol. 8, no. 2, pp. 181–191, Apr. 2008.Google Scholar
  6. [6]
    H. Chen, B. Jiang, N. Lu, and Z. Mao, “Multimode KPCA based incipient fault detection for PWM Inverter of CRH5,” Adv. Mech. Eng., doi:10.1177/1687814017727383.Google Scholar
  7. [7]
    R. Wang and J. Wang, “Fault-tolerant control with active fault diagnosis for four-wheel independently driven electric ground vehicles,” IEEE Trans. Veh. Technol., vol. 60, no. 9, pp. 4276–4287, Nov. 2011.CrossRefGoogle Scholar
  8. [8]
    Z. Mao, Y. Wang, B. Jiang, and G. Tao, “Fault diagnosis for a class of active suspension systems with dynamic actuators’ faults,” International Journal of Control, Automation, and Systems, vol. 14, no. 5, pp. 1160–1172, Oct. 2016.CrossRefGoogle Scholar
  9. [9]
    H. Berriri, M. W. Naouar, and I. Slama-Belkhodja, “Easy and fast sensor fault detection and isolation algorithm for electrical drives,” IEEE Trans. Power Electron., vol. 27, no. 2, pp. 490–499, Feb. 2012.CrossRefGoogle Scholar
  10. [10]
    S. Yang, D. Xiang, A. Bryant, P. Mawby, L. Ran, and P. Tavner, “Condition monitoring for device reliability in power electronic converters: a review,” IEEE Trans. Power Electron., vol. 25, no. 11, pp. 2734–2752, Nov. 2010.CrossRefGoogle Scholar
  11. [11]
    C. Edwardsa and C. P. Tan, “Sensor fault tolerant control using sliding mode observers,” Control Eng. Pract., vol. 14, pp. 897–908, Aug. 2006.CrossRefGoogle Scholar
  12. [12]
    S. Yin, S. X. Ding, A. H. A. Sari, and H. Hao, “Data-driven monitoring for stochastic systems and its application on batch process,” International Journal of System Science, vol. 44, no. 7, pp. 1366–1376, Feb. 2012.MathSciNetCrossRefzbMATHGoogle Scholar
  13. [13]
    N. Lu, F. Gao, and F. Wang, “Sub-PCA modeling and online monitoring strategy for batch processes,” AIChE J., vol. 50, no. 1, pp. 255–259, Jan. 2004.CrossRefGoogle Scholar
  14. [14]
    S. J. Qin, “Survey on data-driven industrial process monitoring and diagnosis,” Ann. Rev. Control, vol. 36, no. 2, pp. 220–234, Dec. 2012.CrossRefGoogle Scholar
  15. [15]
    J. Guzinski, H. Abu-Rub, M. Diguet, Z. Krzeminski, and A. Lewicki, “Speed and load torque observer application in high-speed train electric drive,” IEEE Trans. Ind. Electron., vol. 57, no. 2, pp. 565–574, Feb. 2010.CrossRefGoogle Scholar
  16. [16]
    J. Zhang, J. Zhao, D. Zhou, and C. Huang, “Highperformance fault diagnosis in PWM voltage-source inverters for vector-controlled induction motor drives,” IEEE Trans. Power Electron., vol. 29, no. 11, pp. 6087–6099, Nov. 2014.CrossRefGoogle Scholar
  17. [17]
    K. Rothenhagen and F. W. Fuchs, “Doubly fed induction generator model based sensor fault detection and control loop reconfiguration,” IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 4229–4238, Oct. 2009.CrossRefGoogle Scholar
  18. [18]
    Y. Jeong, S. K. Sul, E. S. Steven, and N. R. Patel, “Fault detection and fault-tolerant control of interior permanentmagnet motor drive system for electric vehicle,” IEEE Trans. Ind. Appl., vol. 41, no. 1, pp. 46–51, Jan. 2005.CrossRefGoogle Scholar
  19. [19]
    M. Benbouzid, D. Diallo, and A. Makouf, “A fault-tolerant control architecture for induction motor drives in automotive applications,” IEEE Trans. Ind. Appl., vol. 53, no. 6, pp. 1847–1855, Nov. 2004.Google Scholar
  20. [20]
    F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “Recent developments of induction motor drives fault diagnosis using AI techniques,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 994–1004, Oct. 2000.CrossRefGoogle Scholar
  21. [21]
    M. AbulMasrur, Z. Chen, and Y. Murphey, “Intelligent diagnosis of open and short circuit faults in electric drive inverters for real-time applications,” IET Power Electron., vol. 3, no. 2, pp. 279–291, Mar. 2000.CrossRefGoogle Scholar
  22. [22]
    T. Wang, G. Zhang, J. Zhao, Z. He, J. Wang, and M. J. Pérez-Jiménez, “Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems,” IEEE Trans. Power Syst., vol. 30, no. 3, pp. 1182–1194, May. 2015.CrossRefGoogle Scholar
  23. [23]
    J. M. Finch and D. Giaouris, “Controlled AC electrical drives,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 481–491, Feb. 2008.CrossRefGoogle Scholar
  24. [24]
    G. S. Buja and M. P. Kazmierkowski, “Direct torque control of PWM inverter-fed AC motors-a survey,” IEEE Trans. Ind. Electron., vol. 51, no. 4, pp. 744–757, Aug. 2004.CrossRefGoogle Scholar
  25. [25]
    T. A. Najafabadi, F. R. Salmasi, and P. Jabehdar-Maralani, “Detection and isolation of speed-, DC-link voltage-, and current-sensor faults based on an adaptive observer in induction-motor drives,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1662–1672, May 2011.CrossRefGoogle Scholar
  26. [26]
    B. Akin, U. Orguner, A. Ersak, and M. Ehsani, “Simple derivative-free nonlinear state observer for sensorless AC drives,” IEEE/ASME Trans. Mechatronics, vol. 11, no. 5, pp. 634–643, Oct. 2006.CrossRefGoogle Scholar
  27. [27]
    M. E. H. Benbouzid, D. Diallo, and M. Zeraoulia, “Advanced fault-tolerant control of induction-motor drives for EV/HEV traction applications: From conventional to modern and intelligent control techniques,” IEEE Trans. Veh. Technol., vol. 56, no. 2, pp. 519–528, Mar. 2007.CrossRefGoogle Scholar
  28. [28]
    H. Chen, B. Jiang, and N. Lu, “Data driven incipient sensor fault estimation with application in inverter of high-speed railway,” Math. Prob. Eng., doi:10.1155/2017/8937356.Google Scholar
  29. [29]
    J. Harmouche, C. Delpha, and D. Diallo, “Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: part II,” Signal Processing, vol. 109, pp. 334–344, 2015.CrossRefGoogle Scholar
  30. [30]
    J. Harmouche, C. Delpha, and D. Diallo, “Incipient fault amplitude estimation using KL divergence with a probabilistic approach,” Signal Processing, vol. 120, pp. 1–7, 2016.CrossRefGoogle Scholar
  31. [31]
    L. Ren, Z. Xu, and X. Yan, “Single-sensor incipient fault detection,” IEEE Sensors J., vol. 11, no. 9, pp. 278–287, Sep. 2011.CrossRefGoogle Scholar
  32. [32]
    U. Kruger and L. Xie, Advances in Statistical Monitoring of Complex Multivariate Processes: with Applications in Industrial Process Control, John Wiley & Sons, 2012.CrossRefGoogle Scholar
  33. [33]
    J. Zeng, U. Kruger, J. Geluk, X. Wang, and L. Xie, “Detecting abnormal situations using the Kullback-Leibler divergence,” Automatica, vol. 50, no. 11, pp. 2777–2786, Nov. 2014.MathSciNetCrossRefzbMATHGoogle Scholar
  34. [34]
    Y. Zhang, Y. Fan, and N. Yang, “Fault diagnosis of multimode processes based on similarities,” IEEE Trans. Ind. Electron., vol. 63, no. 4, pp. 2606–2614, Apr. 2016.Google Scholar
  35. [35]
    S. Yin, S. X. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418–6428, Nov. 2014.CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingP. R. China
  2. 2.Jiangsu Key Laboratory of Internet of Things and Control Technologies (Nanjing University of Aeronautics and Astronautics)NanjingP. R. China

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