Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Cameron, J.D., Morris, S.C.: Spatial Correlation Based Stall Inception Analysis, pp. 433–444 (2007)
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)
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)
Cousins, W.T.: The dynamics of stall and surge behavior in axial-centrifugal compressors. Bja Br. J. Anaesth. 50(9), 1027–34 (1997)
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)
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)
Haddad, W.M., Chellaboina, V.S., Hui, Q.: Nonnegative and Compartmental Dynamical Systems. Princeton University Press, Princeton (2010)
Li, C., Xiong, B., HAN, W.: Surge detection of an axial compressor based on statistical characteristics. J. Aerosp. Power 12, 2656–2659 (2010)
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)
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)
Seiler, P., Pant, A., Hedrick, K.: Disturbance propagation in vehicle strings. IEEE Trans. Autom. Control 49(10), 1835–1842 (2004)
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)
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)
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)
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)
Acknowledgements
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Li, JB., Zhang, S., Sun, XY., Xia, WG. (2019). SDA-RVM Based Approach for Surge Fault Detection and Diagnosis During Aero-Engine Take-Off Process. In: Lam, J., Chen, Y., Liu, X., Zhao, X., Zhang, J. (eds) Positive Systems . POSTA 2018. Lecture Notes in Control and Information Sciences, vol 480. Springer, Cham. https://doi.org/10.1007/978-3-030-04327-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-04327-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04326-1
Online ISBN: 978-3-030-04327-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)