We report on current theoretical and experimental results of hydroacoustic sensing array based on ultra-weak Fiber Bragg Gratings (FBG), using a modified phase generated carrier (PGC) demodulation method with a self-referencing signal. The self-referencing signal is obtained by a sensor isolated from acoustic signals and other environmental disturbances. We report improvements over the conventional PGC methods. Using our demodulation method and with non-sensitized bare fiber (reference sensor0) keeping separate from water, experiment testing demonstrates a minimum detectable hydroacoustic pressure of 2239 μPa/sqrtHz. The properties of our demodulation method are also compared with those of the conventional PGC algorithms. Both simulation and experiments indicate that our demodulation method is immune to the drifts of modulation depth C and of carrier frequency, and the detectable frequency range can be increased by 5 times compared to using conventional PGC methods with the same carrier frequency. The sensor array exhibits a particularly large response in the very low frequency (VLF) region, which is of great importance for underwater seismic detection and submarine applications.

Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.

Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) addresses the incomplete decomposition problem in ensemble empirical mode decomposition (EEMD) during the ensemble averaging process, but the residual noise in intrinsic mode function (IMF) severely affect the decomposition results and the efficiency of the algorithm. To address the shortcomings of CEEMDAN, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm is presented. This method introduces pairs of Gaussian white noise with opposite amplitudes to assist in the decomposition of original signal. The final IMF is the ensemble of both the IMFs with positive and negative noises, where the introduced noise can be effectively neutralized. Simulation and experimental results indicate that ICEEMDAN has advantages in improving the efficiency and decomposition accuracy of the algorithm. The proposed approach was applied to signal denoising in fiber optic sensing, and the analysis results verified the effectiveness and superiority of the method.

Ocean temperature monitoring is of great significance to marine fishing, aquaculture, and marine operations. Traditional electric sensors lack the potential to multiplex several sensors, and may suffer from electromagnetic interference. Meanwhile, fiber Bragg grating-based sensors have the advantages of high sensitivity, possibility for large-scale multiplexing, and immunity to electromagnetic interference. In this paper, we propose a Fabry–Pérot (FP) interferometer based on the draw tower grating array and combine it with the phase measurement method for demonstration and testing. In the sensor system, two adjacent fiber Bragg gratings (FBGs) are used as mirrors and an optical fiber connects them, forming a sensor unit. The signal was detected through the compensation of the optical path difference via two-arm path differences in an unbalanced interferometer. The sensor is calibrated in the range of 36.00–36.50 °C, and back to 36.00 °C, in steps of 0.10 °C. A thermocouple (DW1222) is used as a reference. Experimental testing demonstrates that under the thermal loop, the temperature and phase can be approximated as a linear relationship, the Pearson square correlation coefficient is 0.9996, and the temperature sensitivity is −9846 rad/°C. To prove that our experimental device can achieve a higher temperature resolution, we measured the background noise of the system. The experimental results indicate that the order of magnitude of our system temperature resolution can reach 10⁻⁵ °C. Thus, we believe that the sensor system is promising for the application of ocean temperature detection, and owing to the ultraweak reflection characteristics of the FBG, this method provides the possibility for large-scale multiplexing of the system.

Multi-point vibration sensing at the low frequency range of 0.5–100 Hz is of vital importance for applications such as seismic monitoring and underwater acoustic imaging. Location-resolved multi-point sensing using a single fiber and a single demodulation system can greatly reduce system deployment and maintenance costs. We propose and demonstrate the demodulation of a fiber-optic system consisting of 500 identical ultra-weak Fiber Bragg gratings (uwFBGs), capable of measuring the amplitude, frequency and phase of acoustic signals from 499 sensing fibers covering a total range of 2.5 km. For demonstration purposes, we arbitrarily chose six consecutive sensors and studied their performance in detail. Using a passive demodulation method, we interrogated the six sensors simultaneously, and achieved a high signal-to-noise ratio of 22.1 dB, excellent linearity, phase sensitivity of around 0.024 rad/Pa, and a dynamic range of about 38 dB. We demonstrated a frequency response flatness of <1.2 dB in the range of 0.5–100 Hz. Compared to the prior state-of-the-art demonstration using a similar method, we have increased the sensing range from 1 km to 2.5 km, and increased the frequency range from 0.4 octaves to 7.6 octaves, in addition to achieving sensing in the very challenging low-frequency range of 0.5–100 Hz.