A reliability-based framework for fatigue damage prognosis of composite aircraft structures

Gobbato, Maurizio; Conte, Joseph Le; Kosmatka, J. B.; Farrar, Charles R.

doi:10.1016/j.probengmech.2011.11.004

Cited by 42 publications

(41 citation statements)

References 19 publications

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“…Aircraft, Helicopter, e.g. [189][190][191][192] Mainly vibration data, can involve damage size data, brake use, aircraft weight, brake wear measurement, etc. .…”

Section: Opportunities and Challengesmentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

305

153

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a b s t r a c tThe field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under nonlinear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under nonstationary and non-linear conditions are also discussed.

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“…Aircraft, Helicopter, e.g. [189][190][191][192] Mainly vibration data, can involve damage size data, brake use, aircraft weight, brake wear measurement, etc. .…”

Section: Opportunities and Challengesmentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

305

153

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“…These data could then be utilized in concert with a physics-based structural model for state and loads estimation 15 . Combining these estimates with active sensing-based estimates of damage location 14 would enable predictions of future structural performance 16 . 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 F o r P e e r R e v i e w The WASP prototype utilizes an ARM Cortex-M3 processor with an STM3210C evaluation board.…”

Section: A Overviewmentioning

confidence: 99%

Active-sensing platform for structural health monitoring: Development and deployment

Taylor

Raby

Farinholt

et al. 2016

Structural Health Monitoring

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Embedded sensing for structural health monitoring is a rapidly expanding field, propelled by algorithmic advances in structural health monitoring and the ever-shrinking size and cost of electronic hardware necessary for its implementation. Although commercial systems are available to perform the relevant tasks, they are usually bulky and/or expensive because of their high degree of general utility to a wider range of applications. As a result, multiple separate devices may be required in order to obtain the same results that could be obtained with a structural health monitoring–specific device. This work presents the development and deployment of a versatile, Wireless Active-Sensing Platform, designed for the particular needs of embedded sensing for multi-scale structural health monitoring. The Wireless Active-Sensing Platform combines a conventional data acquisition ability to record voltage output (e.g. from strain or acceleration transducers) with ultrasonic guided wave-based active-sensing, and a seamlessly integrated impedance measurement mode, enabling impedance-based structural health monitoring and piezoelectric sensor diagnostics to reduce the potential for false positives in damage identification. The motivation, capabilities, and hardware design for the Wireless Active-Sensing Platform are reviewed, and three deployment examples are presented, each demonstrating an important aspect of embedded sensing for structural health monitoring.

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“…Peng et al [18] developed a Bayesian imaging method to detect and size delamination damage in composites using Lamb waves. Recently, some authors have made use of recursive Bayesian updating techniques for on-line damage identification in the context of fatigue life prediction in composite materials [19][20][21]. Other researchers have adopted Bayesian IP approaches for ultrasound-based feature identification in biomaterials and soft tissue [22,23].…”

Section: Introductionmentioning

confidence: 99%

A multilevel Bayesian method for ultrasound-based damage identification in composite laminates

Chiachío

Bochud

Chiachío

et al. 2017

Mechanical Systems and Signal Processing

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This version is available at https://strathprints.strath.ac.uk/65462/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Abstract Estimating deterministic single-valued damage parameters when evaluating the actual health state of a material has a limited meaning if one considers not only the existence of measurement errors, but also that the model chosen to represent the damage behavior is just an idealization of reality. This paper proposes a multilevel Bayesian inverse problem framework to deal with these sources of uncertainty in the context of ultrasound-based damage identification. Although the methodology has a broad spectrum of applicability, here it is oriented to model-based damage assessment in layered composite materials using through-transmission ultrasonic measurements. The overall procedure is first validated on synthetically generated signals and then evaluated on real signals obtained from a post-impact fatigue damage experiment in a cross-ply carbon-epoxy laminate. The evidence of the hypothesized model of damage is revealed as a suitable measure of the overall ability of that candidate hypothesis to represent the actual damage state observed by the ultrasound, thus avoiding the extremes of over-fitting or under-fitting the ultrasonic signal.

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A reliability-based framework for fatigue damage prognosis of composite aircraft structures

Cited by 42 publications

References 19 publications

A review on prognostic techniques for non-stationary and non-linear rotating systems

A review on prognostic techniques for non-stationary and non-linear rotating systems

Active-sensing platform for structural health monitoring: Development and deployment

A multilevel Bayesian method for ultrasound-based damage identification in composite laminates

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