Search Results (7)

A theoretical model is presented for the accurate detection of a gas leak source through a pinhole in a cylindrical storage vessel using the acoustic emission (AE) technique. Pinholes of various diameters ranging from 0.20 to 1.2 mm were installed as leak sources, and safe N₂ was used as a filler gas. AE signals were measured and analyzed in terms of AE parameters (such as frequency, amplitude and RMS) as a function of angle and axial distance. Among them, the amplitude characteristic was the most important parameter to determine the leakage dynamics of AE with a continuous waveform. The simulation of AE amplitude was performed using the theoretical model for AE. For practical applications, the theoretical formula was modified into two semi-empirical equations by introducing the normalization method to fit the angular and axial characteristics of the observed AE amplitude, respectively. The main finding of this study is that the semi-empirical equations provide an accurate solution for leak source localization in the cylindrical vessel. As a priori knowledge, the value of ${\kappa }_{\eta }$ in Green’s function, which determines the angular and axial dependence of the AE amplitude, was determined by applying external excitation to the cylinder surface. The proposed formulas provide a suitable approach for practical application in the localization of leak sources in cylindrical storage tanks. Full article

An acoustic emission (AE) is caused by the sudden release of energy by a material as a result of material degradation related to deformations, cracks, or faults within a solid. The same situation also occurs in leaks caused by turbulence in the fluid around the leak. In this study, analytical modeling for an AE due to leakage through a circular pinhole in a gas storage cylinder was performed. The displacement fields responsible for AEs, excited by the concentrated force (CF) associated with the turbulent flow though the pinhole, were derived by solving the Navier–Lamé equation. The CF as an excitation source was formulated in terms of a fluctuating Reynolds stress (FRS) and spatial Green’s function. In particular, a series of experiments were conducted under different operating conditions to explore the characteristics of the AE signals due to leak in a gas cylinder. Finally, the simulation and experimental results were compared to verify the accuracy of the simulation results. Full article

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform. Full article

Samples that were meant to simulate the behavior of neural implants were put into Ringer’s solution, and the occurring damage was assessed. The samples consist of an interdigitated gold-structure and two contact pads embedded between two Polyimide layers, resulting in free-floating structures. The two parts of the interdigitated structure have no electric contacts and are submerged in the solution during the experiment. The samples were held at temperatures of 37 and 57 ${}^{\circ }$C in order to undergo an accelerated lifetime test and to compare the results. During the course of the experiment, a voltage was applied and measured over a resistance of 1 kOhm over time. Arduinos were used as measuring devices. As the intact samples are insulating, a sudden rise in voltage indicates a sample failure due to liquid leaking in between the two polyimide layers. Once a short-circuit occurred and a sample broke down, the samples were taken out of the vial and examined under a microscope. In virtually all cases, delamination was observable, with variation in the extent of the delaminated area. A comparison between measured voltages after failure and damage did not show a correlation between voltage and area affected by delamination. However, at a temperature of 37 ${}^{\circ }$C, voltage remained constant most of the time after delamination, and a pin-hole lead to a lower measured voltage and strong fluctuations. Visually, no difference in damage between the 37 and the 57 ${}^{\circ }$C samples was observed, although fluctuations of measured voltage occurred in numerous samples at a higher temperature. This difference hints at differences in the reasons for failure and thus limited applicability of accelerated lifetime tests. Full article

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability. Full article

In this work, the salinity of permeate obtained with membrane distillation (MD) in pilot scale systems was analyzed. Experiments were performed with three different spiral-wound commercial modules, one from Solar Spring with 10 m² surface membrane area and two from Aquastill with 7.2 and 24 m². Intermittent operation meant that high permeate conductivity was measured in the beginning of each experiment, which was gradually decreasing until reaching a constant value (3–143 µS·cm⁻¹ for seawater feed). The final quality reached did not depend on operating conditions, only the time it took to reach it. This can be because the permeate flux dilutes the minimal feed leak taking place through pinholes in the membranes. Larger feed leak through the membrane was observed when operating in vacuum-enhanced air-gap MD configuration (V-AGMD), which is compatible with this explanation. However, for the increase of feed leak with salinity (up to 1.8 M), a conclusive explanation cannot be given. Pore wetting due to crystallization is discarded because the high permeate quality was recovered after washing with distilled water. More studies at higher salinities and also at membrane level are required to investigate this. Full article

Many copper water lines for municipal drinking water in Santa Fe, New Mexico USA, have developed pinhole leaks. The pitting matches the description of Type I pitting of copper, which has historically been attributed to water chemistry and to contaminants on the copper tubing surface. However, more recent studies attribute copper pitting to microbial induced corrosion (MIC). In order to test for microbes, the copper tubing was fixed in hexamethyldisilazane (HMDS), then the tops of the corrosion mounds were broken open, and the interior of the corrosion pits were examined with scanning electron microscopy (SEM). The analysis found that microbes resembling actinobacteria were deep inside the pits and wedged between the crystallographic planes of the corroded copper grains. The presence of actinobacteria confirms the possibility that the cause of this pitting corrosion was MIC. This observation provides better understanding and new methods for preventing the pitting of copper tubing in municipal water. Full article