Method to determine instantaneous transient responses in pressurized pipes from transfer functions and state space for evaluation of leak signals.

This article addresses the impact of transient pressure anomalies in hydraulic systems, triggered by the opening or closing of valves or pumps, instantly disturbing the line of hydraulic gradient (LGH). This variation in pressure has significant consequences both in hydraulic and structural terms for water networks. Most of the existing techniques to detect transients in water distribution systems use asynchronous methods, generating timeless information that limits the response capacity in critical situations. Therefore, an automatic transient detection system based on the Internet of Things (IoT) is proposed, capable of identifying overpressure or underpressure pulses in soft real-time, activating alarms to facilitate decision-making. This approach helps maintain the safety of the water distribution system and prevent leaks in the network. Furthermore, a model of the transient behavior of pressure and flow is presented by linearizing the water hammer equations from the Laplace transform, thus generating a transfer function that describes the algebraic relationship between the outlet and inlet of the hydraulic system.•The transient analysis of the hydraulic system prototype underscores its high sensitivity to initial conditions, attributed to turbulence. This observation suggests the possible presence of a dynamic strange attractor related to water hammer phenomena in pressure pipes.•The methodology involving transfer functions and state-space models enables the assessment of how leaks impact the transient responses of the system, including the magnitude, duration, and frequency of disturbances generated by them.•The proposed method introduces a dynamic transfer function capable of identifying instantaneous changes over time in terms of flow and pressure.

Method for phase space reconstruction to estimate the short-term future behavior of pressure signals in pipelines.

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.