Health-Informed Uncertainty Quantifications via Bayesian Filters with Markov Chain Monte Carlo Simulations for Fatigue Critical Rotorcraft Components

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Published Oct 2, 2017
Michael Shiao Anindya Ghoshal

Abstract

This paper presents the applications of Bayesian-filters (BF) with Markov chain Monte Carlo (MCMC) simulations for probabilistic lifing assessment of aircraft fatigue critical components. Uncertainties in damage growth parameters are updated with new information obtained through structural
health monitoring (SHM) systems and the remaining useful life (RUL) are predicted. State transition function representing virtual damage growth of a component and measurement function representing the SHM measurements of the component are defined. State transition function is described by a typical Paris equation for fatigue crack propagation. In the equation, the initial crack size and crack growth rate are updated by incoming SHM measurements. Measurement functions are assumed in this study which describe the relationship between the damage features derived from SHM signals and the damage sizes. Damage tolerance (DT) and risk-based remaining useful life of fatigue critical structural components are determined at various reliability levels. The variabilities of RUL are also quantified for various magnitudes of random measurement noise and various measurement frequencies. It is found that the variability of the RUL is proportional to that of the measurement noise. In addition, more frequent measurements will result in less variability in RUL.

How to Cite

Shiao, M., & Ghoshal, A. (2017). Health-Informed Uncertainty Quantifications via Bayesian Filters with Markov Chain Monte Carlo Simulations for Fatigue Critical Rotorcraft Components. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2448
Abstract 111 | PDF Downloads 103

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Keywords

Bayesian, Remaining useful Life, Markov Chain Monte Carlo (MCMC), damage, probabilistic lifing

References
An, D., Choi, J.-H., and Kim, N.-H. (2013). Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab. Reliability Engineering and System Safety. 115 (2013) 161–169
Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processing, vol. 50, no. 2, pp. 174–188, Feb.
Baraldi, P., Compare, M., Sauco, S., and Zio, E. (2012). Fatigue crack growth prognostics by particle filtering and ensemble neural networks. Proceedings of First European Conference of the Prognostics and Health Management Society 2012, PHM-E’12, ISBN - 978-1-936263-04-2, July 3 – 5, Dresden, Germany
Chen, T., and Shiao, M. (2015). Enhanced recursive probabilistic integration method for probabilistic fatigue life management using structural health monitoring. International Workshop on Structural Health Monitoring. September 1-3, Stanford, CA
Chen, Z. (2003). Bayesian filtering: From Kalman filters to particle filters and beyond. Statistics, Volume 182, Issue 1, Pages 1-69 Department of Defense. (2009). Nondestructive Evaluation System Reliability Assessment. MIL-HDBK-1823A, April 7
Doucet, A., Godsill, S., and Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, vol. 10, issue 3, pp. 197–208
Doucet, A., de Freitas, N., Murphy, K., and Russell, S. (2000). Rao-Blackwellised particle filtering for dynamic Bayesian networks. The Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000), UAI-P-2000-PG-176-183
He, D., Bechhoefer, E., Demsey, P., and Ma, J. (2012). An integrated approach for gear health prognostics. 68th American Helicopter Society, International Annual Forum 2012, May 1-3, Fort Worth, Texas
Ihn, J.-B., Pado, L., Leonard, M.S., Desimio, M.P., and Olson, S.E. (2011). Development and performance quantification of an ultrasonic structural health monitoring system for monitoring fatigue cracks on a complex aircraft structures. International Workshop on Structural Health Monitoring: From Condition-based Maintenance to Autonomous Structures. September 13-15, Stanford, California
Kabban, C.M., Greenwell, B.M., Desimio, M.P., Derriso, M.M. (2015). The probability of detection for structural health monitoring systems: Repeated Measures Data. Structural Health Monitoring, 14(3):252-264, April
Khan, Z., Balch, T., Dellaert F. (2004). An MCMC-based particle filter for tracking multiple interacting target. In ECCV (4), pages 279–290
Orchard, M.E., and Vachtsevanos, G.J. (2009). A Particle filtering approach for on-line fault Diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, volume 31, issue: 3-4, pp. 221–246
Renard, B., Garreta, V., and Lang, M. (2006). An application of Bayesian analysis and Markov chain Monte Carlo methods to the estimation of a regional trend in annual maxima. WATER RESOURCES RESEARCH. VOL. 42, W12422, doi:10.1029/2005WR004591
Shiao, M., Chen, T., and Ghoshal, A. (2016). Probabilistic lifing methods for fatigue management of life-limited propulsion components. AHS International 72nd Annual Forum & Technology Display. May 17-19, West Palm Beach, Florida
Shiao, M., Chen, T., Wu, Y-T., and Ghoshal, A. (2016). A novel probabilistic method for life cycle management of fatigue critical aircraft components using in situ nondestructive inspection. Frontiers in Aerospace Engineering. 5(1), 1-18
Tanizaki, H. (2000). Nonlinear and non-Gaussian state-space
modeling with Monte Carlo techniques: A survey and
comparative study. in Handbook of Statistics, C. R. Rao
and D. N. Shanbhag, Eds., North-Holland
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Technical Research Papers