A four-loop shaped structure of fiber Bragg grating (FBG) acoustic emission (AE) sensor based on additive manufacturing (AM) technology is proposed in the letter. The finite element analysis (FEA) method was used to model and analyze the sensor structure. We aimed at improving the sensitivity, the static load analysis, and the dynamic response analysis of the normal FBG acoustic emission sensor and the FBG AE sensor with improved structure parameters. We constructed the FBG AE sensor experimental system based on a narrowband laser demodulation method and test on real acoustic emission signals. The results demonstrated that the response sensitivity of the FBG acoustic emission sensor was 1.47 times higher than the sensitivity of the normal FBG sensor. The sensitivity coefficient of PLA-AE-FBG2 sensor was 3.057, and that of PLA-AE-FBG1 was 2.0702. Through structural design and parameter optimization, the sensitivity and stability of the FBG AE sensor are improved. The four-loop shaped sensor is more suitable for the health monitoring in fields such as aero-engine blade, micro-crack of structure, and crack growth in bonded joints. While ensuring the sensing characteristics, sensitivity, and stability of the four-loop shaped sensor have been enhanced. It is possible to apply the FBG AE sensor in some complex engineering environments.

In this work, we systematically study the transmission spectral characteristics of the microfiber knot resonator (MKR) both in simulation and in an experiment. The transmission spectrum shows several possible phenomena, including dominating resonance, dominating interference, the coexistence of resonance and interference with different intensity ratios, and resonance with Vernier effect. The transmission spectra with various shapes are obtained in simulation by adjusting parameters like fractional coupling intensity loss, intensity coupling ratio, and propagation constants of different modes. The relationship between the transmission spectrum and the size of the resonant ring is studied by reducing the diameter of the resonant ring and analyzing the corresponding transmission spectrum. The performance of a polydimethylsiloxane-coated MKR in refractive index and temperature measurement is also investigated, and the responses of the transmission spectrum, the extracted interference spectrum, the extracted resonance spectrum, the extracted envelope spectrum of Vernier effect are all analyzed, indicating that these spectra have similar responses to the variation of refractive index or temperature. Linear fit indicates that the sensitivities of the transmission spectrum to refractive index and temperature are about 1264.09 nm RIU ⁻¹ and −2.60 nm/°C, respectively. This systematic and comprehensive study will provide a helpful guideline for understanding, analyzing and designing MKR-based devices with desired spectral characteristics to meet different application requirements.

A novel fiber Bragg grating (FBG) acoustic emission (AE) sensor based on coupling cone structure is proposed in this paper, which is flexible and reusable. The sensor is fabricated by additive manufacturing technology. The numerical modeling of the coupling cone structure was performed using finite element analysis. The influence of the coupling cone angle and material on the AE detection performance was studied. The main parameters of the coupling cone structure are determined. The AE detection of material tensile tests was studied by edge-filter-demodulation of spectrum technology. The experimental results demonstrated that the coupling cone FBG AE (Cone-FBG-AE) sensor has advantages of high sensitivity, flexibility and adjustable positioning, and can be reused. It realizes real-time monitoring of AE signals in the tensile process of the specimen, which is consistent with the effect of the resonant AE sensor. The Cone-FBG-AE sensor has great application value in the fields of health monitoring, such as aero-engines, microcracks of structural defects.

Locally overlapped spectra of identical weak-reflectivity fiber Bragg gratings in optical frequency domain reflectometry (OFDR) are susceptible to occasional large-error predictions when adjacent-peak coupling and target-peak degradation obscure shift-related spectral features. This work formulates robust shift tracking as a tail-error suppression problem and introduces Tail-aware training into a one-dimensional convolutional neural network. By combining a tail-penalty term with hard-sample weighting, the training objective increases the contribution of high-error samples and strengthens the constraint on difficult local spectra. An OFDR experimental system was established, and the method was evaluated under uniform-temperature-control baseline experiment, independent temperature control of adjacent gratings, and non-uniform heating of the target grating. For the non-uniform heating case, an operational equivalent reference wavelength shift was constructed from the average temperature at both ends of the target grating and the calibration relationship obtained under uniform heating. Experimental results show that the proposed CNN-Tail-aware (Tail-aware CNN)improves tracking stability and suppresses high-percentile errors under complex local spectral overlap. Based on the completed ten-seed means, its RMSE and P99 were reduced by 26.7% and 20.4%, respectively, compared with CNN-MAE (Baseline CNN). These results indicate that Tail-aware training provides a task-oriented way to reduce large-error prediction risk in robust tracking of locally overlapped spectra under OFDR conditions.