The phase signals, combined with time-domain signal processing methods, are often used for recognition with the phase-sensitive optical time-domain reflectometer (Φ-OTDR). Considering the advanced and sophisticated algorithms prevalent in the field of image processing, a vibration signal imaging method is proposed to enhance the adaptability of phase signals for learning by image network. The phase time series is converted to a Markov Transition Fields (MTF) matrix, from which the based matrix is extracted by Non-negative Matrix Factorization (NMF) and saved as an RGB image. One-dimensional (1-D) Convolutional Neural Network (CNN) and 2-D CNN are applied in the experiment to classify the phase signals and images, respectively. The experimental results show that the training convergence efficiency of 2-D CNN using NMF-MTF images is significantly higher than that of 1-D CNN, demonstrating the effectiveness of converting phase signals into images. In addition, the average recognition accuracy for the four fence events is improved by more than 13% by introducing the NMF algorithm on the MTF matrix.

We report a magnetic field sensor based on an optofluidic microbottle resonator (OFMBR) with whispering gallery mode (WGM) strongly confined in the axial direction, with the hollow core inherently easy to infiltrate the magnetic fluid. The device is simply fabricated along a silica capillary through a fusing splicer. And the influence of number of discharges on the size of the device is analyzed according to the state equation of ideal gas, which is verified by experimental results. In the magnetic field range from the critical value (40 Gs) to the saturated value (300 Gs), the resonant wavelength shifts linearly with the increase of magnetic field strength, with a sensitivity of 8.45 pm/Gs demonstrated, based on the axial confined mode of OFMBR with a Q-factor of 1.28 10^6. This sensor possesses advantages of simple structure, easy fabrication process, and compact size for magnetic field sensing.

A novel fiber optic Fabry-Perot acoustic sensor based on a grooved diaphragm has been proposed, avoiding the decrease of resonant frequency due to the enhanced sensitivity of the sensor based on conventional circular diaphragm. The effect of the structural parameters on sensitivity and resonant frequency is investigated by finite element simulation, and the designed diaphragm is fabricated by a digital light processing 3D printer. Experimental results indicate that the sensor based on the grooved diaphragm has an acoustic pressure sensitivity of 37.44 mV/Pa @2 kHz and a resonant frequency of 2.12 kHz, where the sensitivity is about 2.07 times that of the sensor based on the conventional circular diaphragm and the resonant frequency is almost unchanged. It is shown that the proposed sensor has the advantages of simple fabrication process, high consistency, and excellent applicability for weak acoustic signal detection.

A novel fluorescent fiber sensor based on cerium (Ce) and terbium (Tb) co-doped silica fiber (CTDSF) for partial discharge (PD) detection is proposed. The luminescence mechanism and the PD detection principle of CTDSF are theoretically investigated. The CTDSF is fabricated using the rod-in-tube (RIT) technique combined with the CO2 laser-heated drawing method. The intense emission peak of Tb $^{{3}+}$ ions located at 542 nm ( $^{{5}}\text{D}_{{4}}$ - $^{{7}}\text{F}_{{5}}$ transition) is excited at 302 nm, which indicates the energy transfer (ET) between Ce $^{{3}+}$ and Tb $^{{3}+}$ ions. Moreover, the excitation peak region near 302 nm is well aligned with the PD light emission spectrum. Subsequently, the PD time-domain signals of the needle-plate defect are experimentally measured by utilizing the CTDSF sensor, high-frequency current transformer (HFCT), and plastic scintillation fiber (PSF). Also, the experimental results demonstrate that the CTDSF sensor can detect PD signals and has an excellent PD detection linear response with a sensitivity of 0.22 ns $\cdot \text{V}$ /pC. Furthermore, the sensitivity of the CTDSF sensor is approximately 2.4 times dramatically higher than that of the PSF sensor. The CTDSF sensor integrated with the all-fiber system is predicted to have a wide range of potential applications for online PD detection.

A high-sensitive curvature sensor based on the negative curvature hollow core fiber (NCHCF) is proposed and demonstrated. The graded index fiber (GIF) is introduced between single mode fiber (SMF) and NCHCF for the cascaded structure of SMF-GIF-NCHCF-GIF-SMF. Compared to the structure without GIF, the coupling efficiency is improved by 16.5%. For the cascaded structure, multi-mode interference (MMI) and anti-resonance (AR) mechanisms coexist in the transmission spectra. The experimental results show that MMI-based dips have a blue shift as curvature increases while AR is not sensitive to its variation. As such, the self-referencing approach is employed for improving the performance of curvature sensor with the sensitivity up to -5.27 nm/m ^-1 . Furthermore, the temperature sensitivity of the sensor is -0.003 nm/°C, indicating a negligible temperature crosstalk. With advantages of compactness, efficient coupling, high sensitivity and negligible temperature crosstalk, the curvature sensor shows prospects in structural security monitoring, aviation industry, energy exploration and other fields.

An extrinsic fiber optic Fabry–Perot interferometric (EFPI) ultrasonic sensor based on a grooved silicon diaphragm for partial discharge (PD) detection has been proposed. The size of the groove is determined by finite element simulation, which allows the resonant frequency of the sensor to meet the requirements of PD ultrasonic detection and improves the sensitivity of the sensor by 5.07 times compared with that based on a traditional circular diaphragm. The microelectro–mechanical system process is used to fabricate the diaphragm on a silicon-on-insulator wafer, and the prepared diaphragm has a grooved section with a diameter of 829.34 µm and a thickness of only 2.09 µm. At its resonant frequency of 61.5 kHz, the acoustic pressure sensitivity of the sensor is 172.42 mV/Pa. The ultrasonic signal detection capability of the sensor is verified in the PD experiment. Furthermore, the characteristics of the corona discharge are successfully manifested based on the ultrasonic waves detected by the EFPI sensor. It is demonstrated that the proposed sensor is suitable for PD detection due to its high sensitivity, simple production process, and good resistance to environmental interference.