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Structural Health Monitoring of Composite Structures-Issues and Challenges


Affiliations
1 Advanced Composites Division, National Aerospace Laboratories (NAL), Council for Scientific and Industrial Research, Bangalore, India
     

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Composite materials are increasingly being used in aircraft structures due to their superior properties. Although composites have a higher ability than metals to withstand fatigue loading, they have a lower resistance to impact. Moreover, unlike in metals where impact results in a visible dent or damage, impact on composite structures results in internal defects, which cannot be detected through a regular visual inspection. Thus composites necessitate more sophisticated methods of non-destructive inspection resulting in higher maintenance costs. One of the ways to bring down this cost is Structural Health Monitoring (SHM). SHM technologies would lead to a ‘condition-based maintenance’ or ‘maintenance-on-demand’ paradigm that would increase inspection intervals, leading to lower maintenance costs. Apart from the diagnostic role that SHM would play, an equally important aspect is prognostics, wherein the damage/defect growth rates can be predicted or estimated. The key to an effective SHM system for aircraft structures is not only the appropriate sensor selection but also the processing of the sensor data to predict the flight load and the damages, if any, in the structure. However, structural failure along with sensor malfunction during flight can pose a serious challenge and can lead to erroneous load estimation or inaccurate damage quantification which is extremely critical as far as a robust health monitoring scheme is considered. This paper discusses the issues and challenges in realizing a fully online SHM system.

Keywords

Structural Health Monitoring, Damage, Debond, Impact, Fiber Bragg Grating, Artificial Neural Network.
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  • Structural Health Monitoring of Composite Structures-Issues and Challenges

Abstract Views: 277  |  PDF Views: 2

Authors

Ramesh Sundaram
Advanced Composites Division, National Aerospace Laboratories (NAL), Council for Scientific and Industrial Research, Bangalore, India
G. M. Kamath
Advanced Composites Division, National Aerospace Laboratories (NAL), Council for Scientific and Industrial Research, Bangalore, India
Nitesh Gupta
Advanced Composites Division, National Aerospace Laboratories (NAL), Council for Scientific and Industrial Research, Bangalore, India

Abstract


Composite materials are increasingly being used in aircraft structures due to their superior properties. Although composites have a higher ability than metals to withstand fatigue loading, they have a lower resistance to impact. Moreover, unlike in metals where impact results in a visible dent or damage, impact on composite structures results in internal defects, which cannot be detected through a regular visual inspection. Thus composites necessitate more sophisticated methods of non-destructive inspection resulting in higher maintenance costs. One of the ways to bring down this cost is Structural Health Monitoring (SHM). SHM technologies would lead to a ‘condition-based maintenance’ or ‘maintenance-on-demand’ paradigm that would increase inspection intervals, leading to lower maintenance costs. Apart from the diagnostic role that SHM would play, an equally important aspect is prognostics, wherein the damage/defect growth rates can be predicted or estimated. The key to an effective SHM system for aircraft structures is not only the appropriate sensor selection but also the processing of the sensor data to predict the flight load and the damages, if any, in the structure. However, structural failure along with sensor malfunction during flight can pose a serious challenge and can lead to erroneous load estimation or inaccurate damage quantification which is extremely critical as far as a robust health monitoring scheme is considered. This paper discusses the issues and challenges in realizing a fully online SHM system.

Keywords


Structural Health Monitoring, Damage, Debond, Impact, Fiber Bragg Grating, Artificial Neural Network.



DOI: https://doi.org/10.4273/ijvss.4.3.01