ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Prognostics and Health Management Key Technology of Aircraft Airborne System
Received date: 2014-01-03
Revised date: 2014-02-17
Online published: 2014-02-26
Supported by
National Basic Research Program of China (2014CB046402); National Natural Science Foundation of China (51175014); Programme of Introducing Talents of Discipline Universities of China.
In order to guarantee high reliability, safety and supportability of passenger aircraft, they are usually equipped with prognostics and health management (PHM) to realize reliable operation and health service. This paper introduces the prognostics and health management structure based on on-board monitoring system, air-ground data link and ground maintenance management system under open standards and three-level reasoning. With the multiple data resources from on-board sensors network, historical flight data and maintenance data, this paper gives the way to extract the failure features robustly. Through the hierarchy design, aircraft prognostics and health management adopts the intelligent fault diagnosis algorithm based on hierarchy clustering and enhanced cross check to realize high precision fault diagnosis and isolation. Even in the aircraft health service, the prognostics and health management can also predict the failure based on data driven reasoning, knowledge and failure physics, and then provide the condition-based maintenance strategy. Finally, this paper gives the prognostics and health management evaluation and applicability analysis of the corresponding technology of PHM.
WANG Shaoping . Prognostics and Health Management Key Technology of Aircraft Airborne System[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014 , 35(6) : 1459 -1472 . DOI: 10.7527/S1000-6893.2013.0548
[1] Smith C, Broadie M, DeHoff R. Turbine engine fault detection and isolation program, ADA119998. Wright-Patterson: Air Force Space Command, 1982.
[2] Urban L A. Gas path analysis applied to turbine engine condition monitoring[J]. Journal of Aircraft, 1973, 10(7): 400-406.
[3] James G E T. Robert S, John H S, et al. Failure mechanisms in high performance materials[M]. Cambridge: Cambridge University Press, 1985: 21-30.
[4] Lord D H, Gleason D., Design and evaluation methodology for built-in-test[J]. IEEE Transactions on Reliability, 1981, 30(3): 222-226.
[5] Stan O. A approach to intelligent integrated diagnostic design tools//Proceedings of Autotestcon, 1991: 319-328.
[6] Hammond W E, Jones W G. Vehicle health management[J]. Aerospace Engineering, 1994, 14(6): 17-23.
[7] Hess A, Fila L. The joint strike fighter (JSF) PHM concept: Potential impact on aging aircraft problems//Proceedings of IEEE Aerospace Conference, 2002, 6: 3021-3026.
[8] Roger K N, Kenneth W W. Flight testing of the Boeing 747-400 central maintenance Computer System//Annual Symposium, Society of Flight Test Engineers, 1990, 6(10): 6.1.1-6.1.9.
[9] Matt D, John S. Advanced health management system for the space shuttle main engine//Proceedings of the 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2004: 11-14.
[10] Zuniga F A, Maclise D C, Dennis J. R. Integrated system health management for exploration systems//Proceddings 1st Space Exploration Conference, 2005: 1-16.
[11] Andy H. Biography. http://www.phmtechnology.com/7/our-advisory-board/andy-hess.html.
[12] Brown D W. Biography. http://www.researchgate.net/researcher/70625506_Douglas_W_Brown/.
[13] Roemer M J. Biography. http://sbirsource.com/sbir/people/1666-dr-michael-j-roemer.
[14] Carl B. Biography. http://www.researchgate.net/profile/Carl_Byington/.
[15] Andrew M H. Biography. http://scheller.gatech.edu/directory/faculty/vitas/phd/hess_vita.pdf.
[16] Mark S. Biography. http://ti.arc.nasa.gov/profile/schwabac/publications/.
[17] Michael G, Kevin L. Joint strike fighter—prognostics and health management (PHM), AER200307014. USA: Lockheed Martin Aeronautics Company, 2003.
[18] Ricker N. Wavelet contraction, wavelet expansion and the control of seismic resolution[J]. Geophysics, 1953,18(4): 769-792.
[19] Bogert B P, Healy M J R, Tukey J W. The frequency analysis of time series for echoes: cepstrum, psuedo-autocovariance, cross-cepstrum and saphe cracking//Proceedings of the Symposium on Time Series Analysis, 1963: 209-243.
[20] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Science, 1998, 454(1971): 903-995.
[21] Yi W S. Research on chaos theory and method of weak signal detection. Changchun: College of Communication Engineering, Jilin University, 2006. (in Chinese) 衣文索. 微弱信号的混沌检测理论与方法研究. 长春: 吉林大学通信工程学院, 2006.
[22] Policker S, Geva A B. A new algorithm for time series predication by temporal fuzzy clustering//Proceedings of 15th International Conference on Pattern Recognition, 2000, 2: 728-731.
[23] Clark G J, Vian J L, West M E, et al. Multi-platform airplane health management//IEEE Aerospace Conference, 2007: 1-13.
[24] Xiong N, Svensson P. Multi-sensor management for information fudion: issues and approaches[J]. Information Fusion, 2002, 3(2): 163-186.
[25] Zadeh L A. Fuzzy logic and approximate reasoning[J]. Synthese, 1975, 30(3-4): 407-428.
[26] Hong Y S, Lee S Y, Kim S H, et al. Improvement of the low-speed friction characteristics of a hydraulic piston pump by PVD-coating of TiN[J]. Journal of Mechanical Science and Technology, 2006, 20(3): 358-365.
[27] Christensen R M. An evaluation of linear cumulative damage (Miner's law) using kinetic crack growth theory[J]. Mechanics of Time-Dependent Materials, 2002, 6(4): 363-377.
[28] Chelidze D. A nonlinear observer for damage evolution tracking. Pennysyvania: The Pennysyvania State University, 2000.
[29] Orchard M, Wu B, Vachtsevanos G. A particle filter framework for failure prognostics//Proceedings of World Tribology Congress Ⅲ, 2005: 1-2.
[30] Lembessis E, Antonopoulos G, King R E, et al. CASSANDRA: an on-line expert system for fault prognosis, computer integrated manufacturing//Proceedings of the 5th CIM Europe Conference, 1989: 371-377.
[31] Biagetti T, Sciubba E. Automatic diagnostics and prognostics of energy conversion processes via knowledge based systems [J]. Energy, 2004, 29(12): 2553-2572.
[32] Yang Z, Guo J, Xu W, et al. Multi-scale support vector machine fro regression estimation [J]. Lecture Notes in Computer Science, 2006, 3971: 1031-1037.
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