Failure prognosis of multiple uncertainty system based on Kalman filter and its application to aircraft fuel system

Failure prognosis is the key point of prognostic and health management or condition-based maintenance, the multiple uncertainty sources in real world will lead to inaccurate prediction. In this paper, an advanced failure prognosis method with Kalman filter is presented to address the real-world uncertainties. The multiple uncertainty sources are analyzed and classified first and then theoretical methods are derived, respectively, for the different uncertainty sources. Afterward, the failure prognosis algorithm is developed by taking into consideration. In the end, an aircraft fuel feeding system health monitoring case simulation is presented to demonstrate the effectiveness of the proposed method.

[1]  Wenbin Wang,et al.  A model for residual life prediction based on Brownian motion with an adaptive drift , 2011, Microelectron. Reliab..

[2]  T.D. Batzel,et al.  Prognostic Health Management of Aircraft Power Generators , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[3]  V.V.S. Sarma,et al.  A Decision Theory Model for Health Monitoring of Aeroengines , 1979 .

[4]  Jie Gu,et al.  Prognostics implementation of electronics under vibration loading , 2007, Microelectron. Reliab..

[5]  B. Friedland.  Treatment of bias in recursive filtering , 1969 .

[6]  Cunbao Ma,et al.  Multiple failure prognosis of landing gear retraction/extension system based on H∞ filtering , 2015 .

[7]  Chen Jie,et al.  Multiple failure prognosis of landing gear retraction/extension system based on H∞ filtering , 2015 .

[8]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[9]  W. Wang,et al.  A data-model-fusion prognostic framework for dynamic system state forecasting , 2012, Eng. Appl. Artif. Intell..

[10]  Nikhil Manohar Vichare,et al.  Prognostics and Health Management of Electronics by Utilizing Environmental and Usage Loads , 2006 .

[11]  Qi Zhang,et al.  Neural network based dynamic surface control of hypersonic flight dynamics using small-gain theorem , 2016, Neurocomputing.

[12]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[13]  Bin Xu,et al.  Disturbance Observer-Based Dynamic Surface Control of Transport Aircraft With Continuous Heavy Cargo Airdrop , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[15]  Jie Gu,et al.  Uncertainty Assessment of Prognostics of Electronics Subject to Random Vibration , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[16]  Rui Kang,et al.  Benefits and Challenges of System Prognostics , 2012, IEEE Transactions on Reliability.

[17]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[18]  Gyu-In Jee,et al.  The stability analysis of the adaptive fading extended Kalman filter using the innovation covariance , 2009 .

[19]  Danwei Wang,et al.  Fault-tolerant control using command-filtered adaptive back-stepping technique: Application to hypersonic longitudinal flight dynamics , 2016 .

[20]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[21]  Brian Adam Tuchband.  Implementation of Prognostics and Health Management for Electronic Systems , 2007 .

[22]  E. Brookner.  Tracking and Kalman Filtering Made Easy , 1998 .

[23]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[24]  Dah-Jing Jwo,et al.  A practical note on evaluating Kalman filter performance optimality and degradation , 2007, Appl. Math. Comput..

[25]  Zeng Sheng-kui.  Performance reliability analysis method of dynamic systems under stochastic processes , 2011 .

[26]  Peter Willett,et al.  Predicting Time to Failure Using the IMM and Excitable Tests , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.