Assessment of Fibre Optic Sensor Architectures for Structural Health Monitoring
Bibliographic record
Abstract
Fibre Optic Sensors (FOS) present several advantages over their conventional electrical counterparts for Structural Health Monitoring (SHM) of aerospace structures. These sensors are dimensionally small and can be readily embedded into composite structures, or bonded to their surface with minimal effect on weight or aerodynamic characteristics. They also do not generate, and are immune, to Electro-Magnetic Interference (EMI), thus they do not affect neighboring electrical systems or avionics. Fiber Optic Sensors have been widely used for load monitoring in discrete or distributed architectures. Additionally, Fibre Bragg Grating (FBG) sensors, a family of FOS, have been explored for the detecti on of material acoustic waves and damage. Three sensing system configurations, coupled with the use of FBGs, have been studied and reported in the literature for the detection of material acoustic waves; namely, (1) the use of tunable lasers as light source and photodiodes as the light sensors; (2) Broad Band light Sources (BBS) and Arrayed Waveguide Gratings (AWG); and (3) BBS with cascaded, or pairs of phase-shifted FBGs. The first two configurations also offer the ability to perform load monitoring, through strain measurement, in addition to material acoustic wave and damage detection. In an effort to advance the field of Structural Health Monitoring and its implementation in aircraft applications, these three sensing system architectures were evaluated for their capability and ease for the detection of material acoustic waves and damage. Tests were performed using a realistic and aircraft representative skin composite panel, a quasi-isotropic Carbon Fibre Reinforced Polymer (CFRP) skin panel. In the performed evaluati on, material acoustic waves generated by a piezoceramic transducer were accurately and reliably detected using the first (1) and last (3) techni ques. The first architecture was implemented effortlessly and the last presented, inherently, less complexity. The use of the AWG, in the second (2) architecture, presented comparativel y additional challenges to the detection of the generated acoustic waves due to the requirement for a high power light source. As a result of this evaluati on, the first (1) technique was subsequently selected and assessed in damage detection trials with promising results. Due to the demonstrated success of these trials, the experimental composite skin panel, along with the demonstrated damage detecti on technique, are being integrated into the SHM infrastructure being developed at the National Research Council Canada (NRC). This infrastructure consists of structural platforms that range in complexity from a simple 2 m long aluminium beam to full scale aircraft structures - a CF188 wing and a Bell 206 helicopter tail boom. Additionally, such infrastructure offers full scale evaluations and testing of damage detection techniques and technologies and SHM capabilities employing realistic loads and spectra optionally in varying environment conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".