Life cycle structural health monitoring of airframe structures by strain mapping using FBG sensors

2010 ◽  
Author(s):  
I. Takahashi ◽  
K. Sekine ◽  
H. Takeya ◽  
Y. Iwahori ◽  
N. Takeda ◽  
...  
Keyword(s):  
Life Cycle ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Strain Mapping ◽  
Structural Health ◽  
Fbg Sensors ◽  
Airframe Structures

Development of a Convolutional Neural Network Assisted Fiber Optics Based Passive Structural Health Monitoring System

2021 ◽  
Author(s):  
Ainulla Khan ◽  
Krishnan Balasubramaniam
Keyword(s):  
Structural Health Monitoring ◽  
Fiber Optics ◽  
Health Monitoring ◽  
Alternative Energy ◽  
Power Sources ◽  
Structural Health ◽  
Learning Framework ◽  
Fbg Sensors ◽  
Non Destructive

Abstract The continuous Non-Destructive Evaluation of assets for long-term assurance of performance has led to several developments over the deployment of a Real-Time Structural Health Monitoring (SHM) system. Considering the challenges involved under the implementation of an SHM system for the applications working under harsh environmental conditions with limited access to power sources this work is aimed to contribute towards overcoming those challenges by using the noise from the structure’s machinery or any ambient source as an alternative energy source and employing Fiber Optics based sensing, for its applicability under harsh environments. The required SHM system is realized with the cross-correlation of a fully diffused noise field, sensed using the Fiber Bragg Grating (FBG) sensors at two random locations. With no control on the input received as noise, to this end, a method is developed based on a Deep Learning framework, which is aimed towards a Universal Deployment of the passive SHM system. The methodology is designed to perform the health monitoring of the system, independent of the input perturbations. The validation performed on simulation data has demonstrated the feasibility of the developed technique towards the required kind of passive SHM system.

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