Embedded Sensors for Structural Health Monitoring

This paper focuses on the theoretical development of a practical algorithmic signal processing methodology for extracting information – on a real-time basis – from fiber optic Bragg grating (FOBG) sensors, which represent an important candidate sensor technology for embedded structural health management applications (e.g., aircraft). Using FOBG sensors, it is possible to acquire information pertinent to the diagnosis and isolation of structural degradation (e.g., local strain deformation effects) in various aircraft components and subsystems. This paper presents the essential signal processing operations for extracting such information in situ from raw optical data signals. Implementing such signal processing operations in an efficient manner – suitable for practical real-time application – has been a non-trivial challenge. This paper discusses a successful algorithmic solution technique that ARINC has developed. In particular, the methodology employs a highly efficient and precise technique (based on Hilbert transforms) for measuring peak centers, based on the relative phases of the real and imaginary parts of the reflectivity signals. Moreover, the technique is amenable to parallelization and decimation, both of which are key to achieving fast processing and feature extraction rates.