In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network

Traditionally permanent acoustic sensors leak detection techniques have been proven to be very effective in water distribution pipes. However, these methods need long distance deployment and proper position of sensors and cannot be implemented on underground pipelines. An inline-inspection acoustic device is developed which consists of acoustic sensors. The device will travel by the flow of water through the pipes which record all noise events and detect small leaks. However, it records all the noise events regarding background noises, but the time domain noisy acoustic signal cannot manifest complete features such as the leak flow rate which does not distinguish the leak signal and environmental disturbance. This paper presents an algorithm structure with the modularity of wavelet and neural network, which combines the capability of wavelet transform analyzing leakage signals and classification capability of artificial neural networks. This study validates that the time domain is not evident to the complete features regarding noisy leak signals and significance of selection of mother wavelet to extract the noise event features in water distribution pipes. The simulation consequences have shown that an appropriate mother wavelet has been selected and localized to extract the features of the signal with leak noise and background noise, and by neural network implementation, the method improves the classification performance of extracted features

Properties and hydration mechanism of foamed magnesium oxysulfate cement under acid modification

Foamed magnesium oxysulfate cement (FMOSC) as a new type of porous building material has many advantages, but deficiencies such as low strength and poor water resistance limit its application to an extent. To mitigate them, this study aims to optimize the performance of the FMOSC system using the modifiers 1-Hydroxyethylidene-1,1-diphosphonic acid (HEDP) and potassium dihydrogen phosphate (KDP). The individual and combined effects of the two modifiers on the compressive strength, water resistance, and pore structure of FMOSC are compared. The microstructure and phase composition of FMOSC are further analyzed using X-ray diffraction (XRD), Fourier transform infrared spectra (FT-IR), laser microscopic Raman spectroscopy (Raman), thermogravimetric-differential scanning calorimetry (TG-DSC), and field-emission scanning electron microscopy-energy dispersive spectroscopy (FESEM-EDS). The findings reveal that both single and compound modifiers positively influence the FMOSC systems. The compressive strength, water resistance and pore structure of the specimens were significantly optimized when the dosage of compound modifier was 0.75 % m(HEDP):m(KDP)=2:1 compared to the single modifier. HEDP and KDP stabilize [Mg(OH)(H2O)x]+ through chelation and adsorption, respectively, thereby promoting the generation of the 5·1·7 phase. Furthermore, a positive synergistic effect generates more 5·1·7 phases when both are added simultaneously. Additionally, with the aid of anionic surfactants, the 5·1·7 phase in the bubble wall region grows more densely compared to the 5·1·7 phase in the triangle region. The results of this study show that adding modifiers to FMOSC can expand the scope of its applications, increasing its potential as a structural building material and contributing to building energy efficiency and sustainable development

The Free-Swimming Device Leakage Detection in Plastic Water-filled Pipes through Tuning the Wavelet Transform to the Underwater Acoustic Signals

The conventional fixed acoustic sensors leak detection methods have been demonstrated to be very practical for locating leakages in water distribution pipelines. However, these methods demand proper installation of sensors, and therefore cannot be implemented on buried long water distribution pipelines for condition assessment, early leak detection, and the estimation of leak size effect. Due to these limitations, a free-swimming device is developed. The free-swimming device with the potential of high acoustic sensitivity is capable of detecting the small underwater leakages in the plastic water-filled pipes. Despite the fact that a number of factors influence the underwater acoustic signals, such as water flow noise. Therefore, the interpretation of the leakage and influence of leakage size is considerably challenging from the underwater measured signals. The new method is proposed for reliable leakage detection by tuning the wavelet transform to underwater water acoustic signals. In this method, firstly, Short-Time Fourier Transforms (STFT) of underwater acoustic signals over a relatively long time-interval is monitored to capture the leakage-signals signature. The captured signals efficiently lead in the selection of mother wavelet (tuned wavelet) for the excellent signal localization in the time-frequency domain. Finally, the acoustic signals are analyzed in the tuned wavelet transform to detect the events. In this paper, the practical application of the proposed method, the controlled experiments are designed, and acoustic signals are collected from an experimental setup by launching the free-swimming device. The measured acoustic signals are used to identify the leakage-signals signature from unwanted interfering signals (instantaneous pipe vibrations, water flow noise, pipe's natural frequencies, and background noise). The evaluation of results validated that the free-swimming device and the tuned wavelet transform together can efficiently lead to reliable underwater leakage detection, as well as the influence of the leakage size in plastic water-filled pipes

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

As a core part of protecting water quality safety in water distribution systems, contamination event detection requires high accuracy. Previously, temporal analysis-based methods for single sensor stations have shown limited performance as they fail to consider spatial information. Besides, abundant historical data from multiple stations are still underexploited in causal relationship modelling. In this paper, a contamination event detection method is proposed, in which both temporal and spatial information from multi-stations in water distribution systems are used. The causal relationship between upstream and downstream stations is modelled by Bayesian Network, using the historical water quality data and hydraulic data. Then, the spatial abnormal probability for one station is obtained by comparing its current causal relationship with the established model. Meanwhile, temporal abnormal probability is obtained by conventional methods, such as an Autoregressive (AR) or threshold model for the same station. The integrated probability that is calculated employed temporal and spatial probabilities using Logistic Regression to determine the final detection result. The proposed method is tested over two networks and its detection performance is evaluated against results obtained from traditional methods using only temporal analysis. Results indicate that the proposed method shows higher accuracy due to its increased information from both temporal and spatial dimensions

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems

Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning

This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy

Simultaneous Determination of Bufalin and Its Nine Metabolites in Rat Plasma for Characterization of Metabolic Profiles and Pharmacokinetic Study by LC–MS/MS

Characterization and determination of metabolites to monitor metabolic pathways play a paramount role in evaluating the efficacy and safety of medicines. However, the separation and quantification of metabolites are rather difficult due to their limited contents in vivo, especially in the case of Chinese medicine, due to its complexity. In this study, an effective and convenient method was developed to simultaneously quantify bufalin and its nine metabolites (semi-quantitation) in rat plasma after an oral administration of 10 mg/kg to rats. The prototype and metabolites that were identified were subsequently quantified using positive electrospray ionization in multiple reaction monitoring (MRM) mode with transitions of m/z 387.4→369.6 and 387.4→351.3 for bufalin, m/z 513.7→145.3 for IS, and 387.4→369.6, 419.2→365.2, and 403.2→349.2 for the main metabolites (3-epi-bufalin, dihydroxylated bufalin, and hydroxylated bufalin, respectively). The method was validated over the calibration curve range of 1.00−100 ng/mL with a limit of quantitation (LOQ) of 1 ng/mL for bufalin. No obvious matrix effect was observed, and the intra- and inter-day precisions, as well as accuracy, were all within the acceptable criteria in this method. Then, this method was successfully applied in metabolic profiling and a pharmacokinetic study of bufalin after an oral administration of 10 mg/kg to rats. The method of simultaneous determination of bufalin and its nine metabolites in rat plasma could be useful for pharmacokinetic−pharmacodynamic relationship research of bufalin, providing experimental evidence for explaining the occurrence of some adverse effects of Venenum Bufonis and its related preparations