Fiber optic sensors are used for a large variety of sensing applications, including security applications and the monitoring of bridges, dams, and pipelines. We propose an algorithm that can achieve highly accurate and robust detection of multiple intrusions over distributed localizations (distributed sensing) in the Sagnac fiber sensing system. This distributed-sensing algorithm involves the application of the fast Fourier transform to the linear spectrum of the phase difference signals resulting from the intrusion. The distances or localizations for intrusions occurring at different places correspond to different “response” peaks, which can be conveniently evaluated in the final localization chart. The fundamental theory underlying the algorithm is presented, and its efficacy is demonstrated via a series of experiments with a 130-km long sensing fiber. The localization-sensing performance of our algorithm, with a minimum standard deviation of 28 m for 23 intrusions at same position, shows high robustness. We believe that our approach can significantly contribute to the development of fiber-optic sensing.
An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.
In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.
Distributed Fiber optical sensor has been widely used in communication cables and pipelines defense. Among them, Fiber
Sagnac Interferometer shows several merits such as low noise, low requirement and high reliability. While the loop-based
configurations are difficult in practical application for two aspects: the inconvenience to install Sagnac loop along a line
(such as communication cables) and the isolation of the unused half of the Sagnac loop. Though some linear structures with
delay loops or dual-loop were developed to satisfy reality requirements, they usually make a sacrifice of sensitivity and
have complex circuits. To acquire high sensitivity with simple circuits, we propose a structure in which the two sides of
Sagnac loop are in one cable. When a disturbance applies to the cable, one fiber is compressed and another is stretched, and
vice versa. The phases of clockwise (CW) light and the counter clockwise (CCW) light are affected by the disturbance at
the same time but with different direction. It means that the phase affection acting on the two fibers by the intrusion are
synchronous but differ with half period. Besides the advantages of linear laying and high sensitivity, the high order of null
frequencies are integer multiple of the fundamental null frequency. Closer null frequencies make more accuracy on peaks
location on the Fourier transform. Experiments on simulating the intrusion in lab have been launched. A 50m resolution
has been achieved when the intrusion distance is 100km. This structure is proved simple and accurate.
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