In order to better monitor and identify PCCP pipeline wire broken and obtain wire broken signal characteristics, this paper adopts φ-OTDR system to monitor pipeline wire broken signal and use wavelet packet decomposition to study the energy distribution of wire broken signal in different frequency bands. Firstly, the wire broken signal is collected by φ-OTDR system, and secondly, the wavelet packet decomposition is performed on the wire broken signal collected by the system to obtain the energy distribution of the wire broken vibration signal in different frequency bands. The results show that at 10kHz sampling rate, the frequency band energy (FBE) distribution of the wire broken signal is characterized as follows: the energy of the broken wire signal is higher in the high frequency band (2500-3125Hz, 3437.5-3750Hz, 4375-4687.5Hz) and lower in the low frequency range, and this feature is obvious compared with the non-broken wire case. This method provides a new idea for identifying wire broken events.
The structural health monitoring of Prestressed Concrete Cylinder Pipes (PCCP) is still a difficult issue because the existing detection methods and pipeline protection methods require pipelines to stop running for detection and maintenance, and cannot monitor the running status of pipelines online in real time. As a result, it is impossible to prevent pipeline damage timely and effective and prevent third-party intrusion and damage. Aiming at problems such as PCCP pipeline leakage and pipe burst caused by the external third-party intrusion, pipeline aging, and other factors, this paper proposes a distributed fiber-optic acoustic sensing monitoring method based on the combination of fiber-optic back Rayleigh scattering and phase-sensitive optical time-domain reflectometry. When the pipeline is running normally, by collecting and demodulating the vibration, sound, positioning information and other data along the vibrating optical cable laid on the pipeline, the monitoring and rapid positioning of the pipeline intrusion damage and broken wire can be realized, to achieve the effect of real-time online monitoring of the structural health of the pipeline. The simulation test results show that the system can monitor the length of the pipeline up to 50km, the fault location accuracy is less than 5m, and the system has a single-point listening function, which can realize the secondary review of the fault point alarm information.
KEYWORDS: Denoising, Signal processing, Fiber optics, Signal to noise ratio, Reconstruction algorithms, Optimization (mathematics), Genetic algorithms, Feature extraction, Signal detection
In the practical application of φ-OTDR system, the accuracy of the system is affected by the existence of environmental noise and so on. In order to effectively reduce the noise composition of the measured signal and better obtain the signal characteristics, this paper proposes a noise reduction method GA-VMD which combines genetic algorithm (GA) and variational mode decomposition (VMD). The method firstly optimizes the decomposition layer number (K) and penalty factor (α) of VMD by GA, and then performs multiscale permutation entropy (MPE) randomness detection of the intrinsic mode function (IMF) obtained by decomposition, so as to achieve the purpose of noise reduction. Through the processing of the measured signal, it is shown that the GA-VMD method is better than the empirical mode decomposition (EMD) and the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method in terms of signal-to-noise ratio and cross-correlation coefficient. It shows that the GA-VMD algorithm is better than the EMD and CEEMDAN algorithms, which verifies the effectiveness of the method.
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