Research on Early Fault Self-Recovery Monitoring of Aero-Engine Rotor System

DOI: 10.4236/eng.2010.21008   PDF   HTML     4,653 Downloads   8,055 Views   Citations


In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method that it not only can identify the early fault of AERS but also it can do self-recovery monitoring of fault. Our method is based on the analysis of the early fault features on AERS, and it combined the SVM (Support Vector Machine) with the stochastic resonance theory and the wavelet packet decomposition and fault self-recovery. First, we zoom the early fault feature signals by using the stochastic resonance theory. Second, we extract the feature vectors of early fault using the multi-resolution analysis of the wavelet packet. Third, we input the feature vectors to a fault classifier, which can be used to identify the early fault of AERS and carry out self-recovery monitoring of fault. In this paper, features of early fault on AERS, the zoom of early fault characteristics, the extraction method of early fault characteristics, the construction of multi-fault classifier and way of fault self-recovery monitoring are studied. Results show that our method can effectively identify the early fault of AERS, especially for identifying of fault with small samples, and it can carry on self-recovery monitoring of fault.

Share and Cite:

Z. WANG and S. MA, "Research on Early Fault Self-Recovery Monitoring of Aero-Engine Rotor System," Engineering, Vol. 2 No. 1, 2010, pp. 60-64. doi: 10.4236/eng.2010.21008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] D. Simon, “A comparison of filtering approaches for aircraft engine health estimation,” Aerospace Science and Technology, Vol. 12, No. 4, pp. 276–284, 2008.
[2] S. Borguet and O. Léonard, “Coupling principal component analysis and Kalman filtering algorithms for on-line aircraft engine diagnostics,” Control Engineering Practice, Vol. 17, No. 4, pp. 494–502, 2009.
[3] T. Ramesh Babu and A. S. Sekhar, “Detection of two cracks in a rotor-bearing system using amplitude deviation curve,” Journal of Sound and Vibration, Vol. 314, No. 3–5, pp. 457–464, 2008.
[4] A. Dubov and S. Kolokoinikov, “Review of welding pro- blems and allied processes and their solution using the metal magnetic memory effect,” Welding in the World, Vol. 49, No .9, pp. 306–313, 2005.
[5] K. Tanaka and M. Kawakatsu, “Stochastic resonance in auditory steady-state responses in a magnetoencephalogram,” Clinical Neurophysiology, Vol. 119, No. 9, pp. 2104–2110, 2008.
[6] S. Hurlebaus and L. Gaul, “Smart structure dynamics,” Mechanical Systems and Signal Processing, Vol. 20, No. 2, pp. 255–281, 2006.
[7] A. Ignatios and B. Alexey, “Anomaly induced effects in a magnetic field,” Nuclear Physics B, Vol. 793, No. 1–2, pp. 246–259, 2008.
[8] Mueller and S. N. Sokolova, “Characteristics of lightweight aggregate from primary and recycled raw materials,” Construction and Building Materials, Vol. 22, No. 4, pp. 703–712, 2008.
[9] Y. Asher, P. Yosef, and L. Yuri, “Spectral and variational principles of electromagnetic field excitation in wave guides,” Physics Letters A, Vol. 344, No. 1, pp. 18–28, 2005.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.