Based on the analysis and research of the existing intelligent bolt technology, this article proposes a smart bolt based on fiber Bragg grating sensing technology. The bolt design process was systematically introduced, and the physical model of the three-point fixed anti-tail fiber sloshing was given, as well as the corresponding simulation and experimental comparison. The ability to monitor the looseness of the smart bolts was also tested and installed as a smart unit in the fiber-optic smart grid system for monitoring and testing. The wire dancing simulation test machine was used to simulate various practical application conditions, and the effective monitoring performance of the intelligent bolt was verified.

A method of monitoring the transmission line galloping based on fiber Bragg grating (FBG) fitting sensor is proposed and verified by video monitoring method in the overhead transmission line experiment. The proposed method can be used to measure the line tension, the galloping mode and amplitude in horizontal and vertical directions in real-time, simultaneously. The functions of this method were confirmed by the 92 hours transmission line galloping results and the influence of the wind speed and direction to the galloping measurement were also analyzed. Compared with the video monitoring method, it has the advantage of being able to measure the tension and galloping mode of line, higher resolution of galloping amplitude monitoring and real-time monitoring regardless of weather condition. This method can be widely used in transmission line monitoring and has positive significance to promote smart grid.

To address the problem of early warning of pipeline leakage, this letter proposes a pipeline leakage early warning method based on DAS. Through DAS monitoring the vibration signals induced by different degree of pipeline corrosion and blockage, using pattern recognition techniques, distributed early warning and location of pipeline leakage are realized. By conducting simulations and experimental analyses, the differences in vibration characteristics caused by four types of pipeline anomalies were validated. Combining wavelet packet energy ratios and the BP neural network, the average recognition rate for the five event classes (normal pipeline, 40% corrosion, 80% corrosion, 40% blockage, 70% blockage) was 95.39% based on 3910 samples. The results demonstrate that the proposed method can effectively achieve the early warning of pipeline leakage.

A novel side-hole fiber Bragg grating (SHFBG) based sensor is proposed and realized surface tension, refractive index (RI) and temperature of liquid monitoring simultaneously. A segment of side-hole fiber (SHF) is spliced of a single mode fiber (SMF), and a 10mm long fiber Bragg grating (FBG) is inscribed within the core of the open end of the SHF. Due to the capillary effect, the liquid level in the SHF changes as a function of the surface tension of the liquid, which can be measured by monitoring the liquid level in the hole. The reflectivity of the FBG is affected by the fraction length immersed in the liquid, while the Bragg wavelength is affected by the refractive index and temperature of the liquid. The absolute shift and relative shift of Bragg wavelength can be used to measure the temperature and the refractive index of the liquid, respectively. The sensor described in this paper can measure the surface tension and refractive index easily taking advantage of the simple structure and bring, an additional benefit of the “internal thermometer” provided by the absolute value of the Bragg wavelength.

To address the problem of decreased recognition accuracy of event samples in practical phase-sensitive optical time-domain reflectometer (Φ-OTDR) monitoring scenarios due to external environmental interference, this paper proposes a feature correction algorithm based on sample feature weighting method. By establishing a correlation evaluation method and a weight allocation scheme based on sample feature correlation, combined with the back propagation (BP) algorithm, an average recognition rate of 99.50% for four types of events (climbing, strong wind, knocking and background, 6000 samples) in strong wind environments was achieved, which is 3% higher than the algorithm using BP classifier. The results demonstrate that the proposed algorithm can effectively enhance the performance of Φ-OTDR in complex environments.

A method to optimize the arrangement of an fiber Bragg grating (FBG) array is described to address the need to improve the accuracy of impact localization for aerospace vehicles. The sensitive area of the FBG, which is approximately elliptical, is analyzed using finite element simulation and experimental measurement. Based on this analysis, three optimized FBG sensing arrangements are proposed to improve the coverage of the FBG sensitive area at the center, located on the side and corner positions of an experimental test sample when subject to an impact. By employing the inverse problem analysis method in multiple repetitions of impact localization experiments, the accuracy of impact localization is improved from 74% with the traditional 4-corner arrangement to 84%, 88%, and 91% respectively for the three arrangements, demonstrating the effectiveness of the optimized FBG array arrangement method.