SDA-RVM Based Approach for Surge Fault Detection and Diagnosis During Aero-Engine Take-Off Process

  • Ji-Bang Li
  • Shuo ZhangEmail author
  • Xiao-Yu Sun
  • Wei-Guo Xia
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 480)


Under various operation conditions, the take-off process of aero-engine is regarded as a typical positive system. Meaning while, the aero-engine surge caused by exerting force in the take-off process brings catastrophic risk to the flight safety and affects overall aero-engine performance. Therefore the precise forecasting of aero-engine rotating stall development process under complex conditions is an effective method for the detection and diagnosis of aero-engine surge fault. In order to avoid the roughness result of the binary classification and the difficulty of feature extraction within high dimensional data for traditional machine learning (ML) approaches, SDA-RVM is developed to provide an accurate rotating stall detection and a surge warning window. Firstly, the SDA is implemented to extract the implicit feature beneath the high dimensional data. Then, the RVM is carried out to calculate the stall trigger probability under the reconstructed vector input. Finally, the surge alert window is identified according to the stall probability. The result of various ML algorithm is compared with the data of on service aero-engine, demonstrating the efficacy of the proposed SDA-RVM approach.


Surge fault diagnosis Rotating stall detection Relevance vector machine (RVM) Stacked de-nosing auto-encoders (SDA) 



We are grateful for the financial support of the Fundamental Research Funds for the Central Universities in China (No. DUT16RC(3)115) and State Key Laboratory of Robotics Fund Project (No. 2017-O03).


  1. 1.
    Benzaouia, A., Mesquine, F., Benhayoun, M., Schulte, H., Georg, S.: Stabilization of positive constrained T-S fuzzy systems: application to a Buck converter. J. Frankl. Inst. 351(8), 4111–4123 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cameron, J.D., Morris, S.C.: Spatial Correlation Based Stall Inception Analysis, pp. 433–444 (2007)Google Scholar
  3. 3.
    Cao, H.: Study of the surge fault diagnosis of an aeroengine based on the LS-SVM least square-supporting vector machine. J. Eng. Therm. Energy Power, pp. 23–27 (2013)Google Scholar
  4. 4.
    Cao, Y., Zang, S., GE, B.: Analyzing the acoustic signal of compressor surge by using fast fourier transform and wavelet transform. Energy Technol. 3, 125–128 (2010)Google Scholar
  5. 5.
    Cousins, W.T.: The dynamics of stall and surge behavior in axial-centrifugal compressors. Bja Br. J. Anaesth. 50(9), 1027–34 (1997)Google Scholar
  6. 6.
    Cui, J., Shan, M., Yan, R., Wu, Y.: Aero-engine fault diagnosis using improved local discriminant bases and support vector machine. Math. Probl. Eng. pp. 1–9 (2014)Google Scholar
  7. 7.
    Goodwin, G.C., Medioli, A.M., Carrasco, D.S., King, B.R.: A fundamental control limitation for linear positive systems with application to type 1 diabetes treatment. Autom. J. IFAC Int. Fed. Autom. Control 55, 73–77 (2015)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Haddad, W.M., Chellaboina, V.S., Hui, Q.: Nonnegative and Compartmental Dynamical Systems. Princeton University Press, Princeton (2010)Google Scholar
  9. 9.
    Li, C., Xiong, B., HAN, W.: Surge detection of an axial compressor based on statistical characteristics. J. Aerosp. Power 12, 2656–2659 (2010)Google Scholar
  10. 10.
    Liu, Y., Dhingra, M., Prasad, J.V.R.: Active compressor stability management via a stall margin control mode. J. Eng. Gas Turbines. Power, pp. 731–743 (2010)Google Scholar
  11. 11.
    Lu, C., Wang, Z.Y., Qin, W.L., Ma, J.: Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 130, 377–388 (2017)CrossRefGoogle Scholar
  12. 12.
    Seiler, P., Pant, A., Hedrick, K.: Disturbance propagation in vehicle strings. IEEE Trans. Autom. Control 49(10), 1835–1842 (2004)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., Chen, X.: A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89, 171–178 (2016)CrossRefGoogle Scholar
  14. 14.
    Tanabe, S., Ichihara, H., Ebihara, Y., Peaucelle, D.: Persistence analysis of discrete-time interconnected positive systems and its application to mobile robot formation. IFAC-Pap. 50(1), 3105–3110 (2017)CrossRefGoogle Scholar
  15. 15.
    Wang, J., Duan, X.H., Li, Y., Bai, P.: Prediction of aero engine fault by relative vector machine and genetic algorithm model. Adv. Mater. Res. 998–999, 1033–1036 (2014)Google Scholar
  16. 16.
    Yan, B., Weidong, Q.: Aero-engine sensor fault diagnosis based on stacked denoising autoencoders. In: Proceedings of 35th Chinese Control Conference (CCC), pp. 6542–6546 (2016)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ji-Bang Li
    • 1
  • Shuo Zhang
    • 1
    Email author
  • Xiao-Yu Sun
    • 1
  • Wei-Guo Xia
    • 1
  1. 1.Dalian University of TechnologyDalianChina

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