The demand for distributed acoustic sensors, which are capable of reconstructing the amplitude, frequency, and phase of an acoustic field, is increasing. These sensors are typically realized by phase-sensitive optical time domain reflectometry ( $\Phi$ -OTDR). However, common $\Phi$ -OTDR systems suffer from a fading phenomenon, which causes amplitude fluctuation on Rayleigh backscattering traces, and the scattered light may not have enough intensity in some regions. These areas have low amplitude backscattering that may be even lower than the system noise floor during some periods of time. Therefore, we cannot reconstruct the phase signal properly in such low intensity areas. Systems with multiple frequencies have been proposed to achieve fading suppression. However, online data processing of such schemes remains a challenge, especially for continuous in-field applications. Prediction of fading must be performed in real time. Any delay caused by massive calculations is not acceptable. In this paper, a continuous fading suppression method based on a $\Phi$ -OTDR system with three different probe frequencies is presented as well as a tracking algorithm for selecting the optimum probe signal for any time, which predicts the occurrence of fading before it actually occurs. The performance of the proposed method has been experimentally evaluated and statistically analyzed. The distortion induced by the fading effect could be suppressed to 1.15% under continuous real-time running. The method is highly effective and repeatable, which makes it suitable for practical online purposes.

The last years have witnessed the wide application of Distributed Acoustic Sensor (DAS) systems in several fields, such as submarine cable monitoring, seismic wave detection, structural health monitoring, etc. Due to their distributed measurement ability and high sensitivity, DAS systems can be employed as a promising tool for the phase sensitive optical time domain reflectometry (Φ-OTDR). However, it is also well-known that the traditional Φ-OTDR system suffers from Rayleigh backscattering (RBS) fading effects, which induce dead zones in the measurement results. Worse still, in practice it is difficult to achieve the optimum matching between spatial resolution (SR) and signal to noise ratio (SNR). Further, the overall frequency response range (FRR) of the traditional Φ-OTDR is commonly limited by the length of the fiber in order to prevent RBS signals from overlapping with each other. Additionally, it is usually difficult to reconstruct high frequency vibration signals accurately for long range monitoring. Aiming at solving these problems, we introduce frequency division multiplexing (FDM) that makes it easier to improve the system performance with less system structure changes. We propose several novel Φ-OTDR schemes based on Frequency Division Multiplexing (FDM) technology to solve the above problems. Experimental results showed that the distortion induced by fading effects could be suppressed to 1.26%; when the SR of Φ-OTDR is consistent with the length of the vibration region, the SNR of the sensing system is improved by 3 dB compared to the average SNR with different SRs; vibration frequencies up to 440 kHz have been detected along 330 m artificial microstructures. Thus, the proposed sensing system offers a promising solution for the performance enhancement of DAS systems that could achieve high SNR, broadband FRR and dead zone-free measurements at the same time.

This paper proposes a general integration method which can effectively describe the characteristics of pipeline leakage and help distinguish multiple pipeline microstates. Since the rapid development of Φ-OTDR in recent years, this technology has been applied to more and more fields, such as fiber optic safety monitoring, seismic monitoring, and structural health monitoring. Among them, Φ-OTDR has the characteristic of continuous full-scale monitoring in pipeline monitoring, but there are few researches on pipeline state characteristics at present. In this paper, based on the analysis of the pipeline state with Φ-OTDR technology, a method of extracting multiple microstates of pipelines is proposed. This method combined with the peak-to-average power ratio, short-term interval zero crossing, and fractal characteristics in the frequency domain can effectively characterize the microstate of pipes and provide support for identification of more microstates of pipelines. These features reflect the common characteristics of leaks in gas pipelines and liquid pipelines. Meanwhile, their combination features can represent the small differences in pipeline states. The experimental results show that the method can effectively characterize the microstate information of the pipeline, and the recognition rate of the hybrid feature under two kinds of pipeline leakage and multipressure conditions reaches 91% and 83%.

It is commonly known that for characteristics, such as long-distance, high-sensitivity, and full-scale monitoring, phase-sensitive optical time-domain reflectometry (Φ-OTDR) has developed rapidly in many fields, especially with the arrival of 5G. Nevertheless, there are still some problems obstructing the application for practical environments. First, the fading effect leads to some results falling into the dead zone, which cannot be demodulated effectively. Second, because of the high sensitivity, the Φ-OTDR system is easy to be interfered with by strong noise in practical environments. Third, the large volume of data caused by the fast responses require a lot of calculations. All the above problems hinder the performance of Φ-OTDR in practical applications. This paper proposes an integration method based on a complete ensemble empirical mode decomposition with adaptive noise and Kullback–Leibler divergence (CEEMDAN-KL) and an adaptive moving neighbor binary pattern (AMNBP) to enhance the performance of Φ-OTDR. CEEMDAN-KL improved the signal characteristics in low signal-to-noise ratio (SNR) conditions. AMNBP optimized the location and recognition via a high calculation efficiency. Experimental results show that the average recognition rate of four kinds of events reached 94.03% and the calculation efficiency increased by 20.0%, which show the excellent performance of Φ-OTDR regarding location and recognition in practical environments.

Pipeline leakage is an important security issue, which directly affects the normal operation of life and production. The development of optical fiber sensing technology is fast and must be the trend of pipeline monitoring. The research of pipeline leak characteristics based on optical fiber monitoring data will become more and more important. This paper puts forward a series of characteristic indicators of pipeline leakage. The characteristics of gas pipeline leakage and liquid pipeline leakage are compared and analyzed, and the commonness and individuality of pipeline leakage are studied, whose researches are proved through practical experiments.