Abstract

To enhance the capability of distributed optical fiber (DOF) monitoring system on real-time broken wire signal detection of prestressed concrete cylinder pipe (PCCP), this research proposes a novel effective parameter mining algorithm by utilizing wrapper theory and sparrow search algorithm (SSA). First, the improvement approach of SSA is investigated by applying the adaptive adjustment strategy, the Gaussian mutation perturbation, and the Tent chaotic perturbation. The subset search method is constructed based on the improved SSA (ISSA). Second, the similarity degrees of vibration signals are characterized by the Euclidean distance and the average linkage distance. On this basis, the subset evaluation criterion is established according to the classification accuracy of hierarchical clustering (HC). Finally, the prototype experiment is conducted in which the phase-sensitive optical time-domain reflectometry (Φ-OTDR) monitoring system is employed to collect vibration signals. Then, the effectiveness and the performance of the developed methodology are validated. The following results are drawn. (1) Benchmark function tests indicate that the convergence performance, the optimization ability, and the operation stability of ISSA are superior to those of SSA and the improved particle swarm optimization (IPSO). (2) The original set that has 122 feature parameters is established, and ISSA-HC, IPSO-HC, and SSA-HC extract 24, 31, and 55 effective parameters, respectively. (3) The recognition accuracies of the 3 parameter subsets are 95.83%, 84.17%, and 80.83%, respectively. The parameter subset mined by ISSA-HC has the best pattern recognition ability. This research provides technical support for the broken wire monitoring of in-service PCCP engineering. Meanwhile, it offers a foundation for further developing suitable pattern recognition algorithms to identify PCCP broken wire signals.

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