Finite Element based Bayesian Particle Filtering for the estimation of crack damage evolution on metallic panels
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
A lot of studies are nowadays devoted to structural health monitoring, especially inside the aeronautical environment. In particular, focusing the attention on metallic structures, fatigue cracks represent both a design and maintenance issue. The disposal of real time diagnostic technique for the assessment of structural health has led the attention also toward the prognostic assessment of the residual useful life, trying to develop robust prognostic health management systems to assist the operators in scheduling maintenance actions. The work reported inside this paper is about the development of a Bayesian particle filter to be used to refine the posterior probability density functions of both the damage condition and the residual useful life, given a prior knowledge on damage evolution is available from NASGRO material characterization. The prognostic algorithm has been applied to two cases. The former consists in an off-line application, receiving diagnostic inputs retrieved with manual structure scanning for fault identification. The latter is used on-line to filter the input coming from a real-time automatic diagnostic system. A massive usage of FEM simulations is used in order to enhance the algorithm performances.
How to Cite
##plugins.themes.bootstrap3.article.details##
particle filtering, on-line condition monitoring, Automatic diagnostics, finite element, aluminium panel
Boller, C. (2001), Ways and options for aircraft structural health management, Smart Materials & Structures, 10: 432-440.
Budynas & Nisbett (2006), Shigley’s Mechanical Engineering Design, VIII edition, McGraw-Hill Cadini, F., Zio, E. & Avram, D. (2009), Monte Carlo-based filtering for fatigue crack growth estimation, Probabilistic Engineering Mechanics, 24: 367-373.
Giglio, M. & Manes, A. (2008), Crack propagation on helicopter panel: experimental test and analysis, Engineering fracture mechanics, 75:866-879.
Haug A.J. (2005), A tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes, MITRE technical report, Virginia.
JSSG-2006, Joint Service Specification Guide – Aircraft Structures, Department of USA defence.
Lazzeri, L. & Mariani, U. (2009), Application of Damage Tolerance principles to the design of helicopters, International Journal of Fatigue, 31(6): 1039-1045.
NASGRO reference manual (2005), Fracture Mechanics and Fatigue Crack Growth Analysis Software, version 4.2.
Sbarufatti, C., Manes, A. and Giglio, M. (2010), Probability of detection and false alarms for metallic aerospace panel health monitoring, Proc. 7th Int. Conf. on CM & MFPT, Stratford Upon Avon, England.
Sbarufatti, C., Manes, A. & Giglio, M. (2011), HECTOR: one more step toward the embedded Structural Health Monitoring system, Proc. CEAS 2011, Venice, Italy.
Sbarufatti, C., Manes, A. & Giglio, M. (2011), Advanced Stochastic FEM-Based Artificial Neural Network for Crack Damage Detection, Proc. Coupled 2011, Kos, Greece.
Sbarufatti, C., Manes, A. & Giglio, M. (2011), Sensor network optimization for damage detection on aluminum stiffened helicopter panels, Proc. Coupled 2011, Kos, Greece.
Sbarufatti, C., Manes, A. & Giglio, M. (2012), Diagnostic System for Damage Monitoring of Helicopter Fuselage, Proc. EWSHM 2012, Dresden, Germany.
Schmidt, H.J. & Schmidt-Brandecker, B. (2009), Design Benefits in Aeronautics Resulting from SHM, Encyclopedia of Structural Health Monitoring.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.