A novel distributed fiber-optic sensor is proposed and demonstrated, in which two Mach-Zehnder interferometers are used to detect the interference signals with different wavelengths, and one 3 × 3 coupler is deployed to demodulate the time-varying phase change caused by vibration. The novel dual Mach-Zehnder interferometer (DMZI) is composed of two wavelength division multiplexers. Then, a time delay estimation algorithm is designed to construct two related signals using the phase difference, and the two constructed signals are used to obtain vibration position through cross-correlation. Experimental results show that the sensing distance can reach 100 km and the location error is within ±25 m.
A novel distributed fiber-optic localization algorithm with high sensitivity and precision based on merged Michelson-Sagnac interferometer is proposed and demonstrated. By performing simple addition and subtraction processing on the two phase differences of the two interferometers, two superimposed signals with a fixed delay can be obtained. The time delay can be calculated by a cross-correlation algorithm. Combined with the polynomial fitting interpolation, the disturbance location resolution is significantly improved. The total sensing distance can reach 120 km, and the localization errors are within ±35 m and ±160 m in the contact and non-contact disturbance experiment, respectively.
A new optical fiber humidity sensor with high sensitivity is reported. We effectively control the light-intensity changes in a smaller sensing area and achieve a significant increase in sensitivity by adjusting the depth of the evanescent field of the tapered fiber. The sensor is designed with an 8 µm diameter single-mode tapered fiber structure coated with a thickness of a 10 mm length carbomer layer in the tapered area. The average and maximum relative humidity (RH) sensitivities are 2.59 dB/%RH and 5.43 dB/%RH in the range of 68%–90%. To our best knowledge, the sensitivity of the sensor is highest compared with that of the previously reported. Moreover, the fast response time and recovery time of the sensor are ideal. In addition, the proposed humidity sensor has good repeatability and lower-temperature cross talk. Due to the excellent indicators, the proposed sensor has promising potential for highly sensitive RH sensing applications, especially early warning of special environments.
A dual-model hybrid pattern recognition based on a fiber optic line-based sensor with a large amount of data is proposed. The vibration signals are converted to gray-level images to reduce the memory requirement. The ResNet18 model for classification is used. To reduce the false positive rate, the over-zero rate and short-time energy are extracted from the intrusion signal, and a support vector machine (SVM) is used. Finally, a discriminator is constructed to determine the types of events by combining the two models trained on the validation dataset. The results demonstrate the excellent average recognition accuracy of this method, which achieves the 97.1% for six events.
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