A review of uncertainty in flight vehicle structural damage monitoring, diagnosis and control: Challenges and opportunities

Lopez, Israel; Sarigul‐Klijn, Nesrin

doi:10.1016/j.paerosci.2010.03.003

Cited by 126 publications

(78 citation statements)

References 135 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A variety of techniques have been developed for aircraft fault detection and failure evaluations. [34][35][36][37][38] In the presence of more severe situations like airframe damages resulting form fatigue cracks, foreign objects and overstress during upsets, which normal PHM cannot diagnose, we need a process to implement the detection, isolation and estimation of those damages. Few researchers focus on this problem, part of the reason is that the structural damage causes the change of not only aerodynamic coefficients but also the overall structure, and it always couples with the paralysis of actuators.…”

Section: 23mentioning

confidence: 99%

Online Safe Flight Envelope Prediction for Damaged Aircraft: A Database-driven Approach

Zhang

Visser

Chu

2016

AIAA Modeling and Simulation Technologies Conference

View full text Add to dashboard Cite

This paper proposed a framework of database-driven approach to solve the problem of online safe flight envelope prediction for aircraft safe recovery under abnormal conditions. On occurrence of sudden accidents like structural damages, conventional systems have neither enough measurements for the online reidentification of global damaged models, nor enough time for the onboard safe flight envelope computation. To circumvent these technical bottlenecks and make online applications more feasible in such emergency situations, some information, like global damaged models and the corresponding safe flight envelops, could be stored offline in a database for later online retrieval.

show abstract

Section: 23mentioning

confidence: 99%

Online Safe Flight Envelope Prediction for Damaged Aircraft: A Database-driven Approach

Zhang

Visser

Chu

2016

AIAA Modeling and Simulation Technologies Conference

View full text Add to dashboard Cite

show abstract

“…The uncertainty of the performance of IVHM tools has been well documented. Lopez & Sarigul-Klijn [6], showed how the reliability of an IVHM tool varies depending on the characteristics of the fault, which are different on every occasion, and this translates into uncertainty about its performance. Furthermore, Saxena et al [7] also analysed how the accuracy of prognostic algorithms evolves with time, with the RUL becoming more accurate as the component approaches its point of failure.…”

Section: Uncertainty and Health Monitoringmentioning

confidence: 99%

Downtime uncertainty reduction through the correct implementation of health monitoring tools

Esperon-Miguez¹,

John²,

Jennions³

2012

IET &Amp; IAM Asset Management Conference 2012

View full text Add to dashboard Cite

The objective of Integrated Vehicle Health Management (IVHM) is to increase platform availability and reduce maintenance times and costs through the use of health monitoring on key systems. The information generated using condition monitoring algorithms can be used to reduce maintenance times, improve the management of the support process and operate the fleet more efficiently. This paper discusses the effect of advanced health monitoring tools on the uncertainty of predicted downtimes and costs for vehicles and fleets and how they affect the management of the asset.

show abstract

“…However, the nature of the system model and observed data has strong uncertainty under the influence of some factors such as the observation noise, modeling error and environmental factors etc., which causes the estimation of system parameters become a kind of uncertain problems [1].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian Estimation of System Parameters from Dynamic Responses

Yang¹,

Zhang²

2018

dtcse

View full text Add to dashboard Cite

Abstract.A hierarchical Bayesian approach is proposed for estimating system parameters by directly taking dynamic responses as the fixed target. The basic theories of hierarchical Bayesian model are first introduced, and then the estimation process of system parameters is illustrated. A mass-spring system of eight degrees of freedom is studied to validate the proposed method, and the effects of modeling errors, incompleteness of dynamic response data and measurement noise on the identification results are numerically investigated. The research results show that the proposed method can accurately identify physical parameters of the system with random modeling errors and measurement noise from only several dynamic responses of the system. This method can provide a new way for parameter estimation of the system with modeling errors, incompleteness of dynamic response data and serous pollution of measurement noise.

show abstract

A review of uncertainty in flight vehicle structural damage monitoring, diagnosis and control: Challenges and opportunities

Cited by 126 publications

References 135 publications

Online Safe Flight Envelope Prediction for Damaged Aircraft: A Database-driven Approach

Online Safe Flight Envelope Prediction for Damaged Aircraft: A Database-driven Approach

Downtime uncertainty reduction through the correct implementation of health monitoring tools

Hierarchical Bayesian Estimation of System Parameters from Dynamic Responses

Contact Info

Product

Resources

About