.png){kind=icon}
.png){kind=icon}
Return
Self-healing FBG sensor network fault-detection based on a multi-class SVM algorithm
.png){kind=icon}
OA
AI
DOI：10.1364/OE.509286.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a three-layer ring architecture with enhanced reconfigurable capabilities for fiber Bragg grating (FBG) sensor networks. The proposed network is capable of self-healing when three fiber links fail. In addition to self-healing, soft faults in the FBG sensors can be detected using a multi-classification support vector machine (multi-class SVM) algorithm. The detection accuracy reached 99%. Additionally, we used an artificial neural network (ANN) reliability estimation model to estimate the reliability of the FBG self-healing network. The results show that the ANN reliability analysis model can accurately estimate the reliability of the architecture at a reasonable cost.
Keywords:
SENSING SYSTEM
FIBER
ARCHITECTURE
PREDICTION
Journal
IF:
3.3
Papers:
6.1W
Citations:
14.3W