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This study integrates the array sensing module and the flow leakage algorithm. In this study, a real-time monitoring deep-sea pipeline damage sensing system is designed to provide decision-making parameters such as damage coordinates and damage area. The array sensor module is composed of multiple YF-S201 hall sensors and controllers. YF-S201 hall...
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... the actual size of the leak in the pipeline is Type I, the size calculated by the flowrate of the hall sensors is 2.58 cm 2 on average, as listed in Table 2, indicating that the error between experimental data and theoretical data is 46%. When the actual size of leakage in the pipeline is Type II, the size calculated by the flowrate of the hall sensors is 1.31 cm 2 on average, as listed in Table 3, indicating that the error between the experimental data and theoretical data is 21%. The cause of this error could be inaccurate flowrate detection resulted from an unstable flow field. ...Citations
... The configuration proposed for the experimental hydraulic model provides efficient and accurate support to analyze and study the behavior of pressure and flow in the system under study. However, the model presented different types of noise produced mainly by fluctuations in the input signal and electromagnetic interference in situ , so multiple signal processing techniques were implemented to improve the quality of the captured data [16] from sensor calibration, signal filtering, and correct connection of the electronic components. Cross-validation of the data generated by the sensors was performed concerning precise measurement equipment (standards), thus minimizing the effect of the signals' noise. ...
In this study, we propose a method based on phase space reconstruction to estimate the short-term future behavior of pressure signals in pipelines. The pressure time series data were obtained from an IoT experimental model conducted in the laboratory. The proposed hydraulic system demonstrated the presence of traces of weak chaos in the time series of the pressure signal. Fractal dimension analysis revealed a complex fractal structure in the data, indicating the existence of nonlinear dynamics. Similarly, Lyapunov coefficients, divergent trajectories, and autocorrelation analysis confirmed the presence of weak chaos in the time series. The results demonstrated the existence of apparently chaotic patterns that follow the theory proposed by Kolmogorov for deterministic dynamic systems that exhibit apparently random behaviors. Phase space reconstruction allowed us to show the dynamic characteristics of the signal so that short-term predictions were stable. Finally, the study of strange attractors in pipeline pressure time series can have significant contributions to anomaly detection.•A methodology is proposed for the reconstruction of the phase space to estimate the short-term future behavior of pressure signals in pipelines in real time.
•The analysis of the proposed hydraulic system revealed some indications of weak chaos in the time series of the pressure signal obtained experimentally.
•The methodology implemented and the results of this study showed that the short-term predictions were very accurate and consistent; Chaotic patterns were also identified that support the theory proposed by Kolmogorov.
