Negative Pressure Wave Research Articles

Negative Pressure Waves in Hydraulic Systems

Negative Pressure Waves in Hydraulic Systems

WML: Wireless Sensor Network based Machine Learning for Leakage Detection and Size Estimation

Abstract Fluid (oil/gas/water) transportation systems present a significant challenge for pipeline health monitoring. With the development of smart devices capable of micro-sensing, on-board processing, and wireless communication capabilities, the wireless sensor networks are able to facilitate online learning and reliable event monitoring and reporting for distribution pipelines. This paper presents the design, development and testing of a smart wireless sensor network (WSN) for leak detection and size estimation in long range pipelines. This system uses wireless communication and machine learning (WML) to learn, make decisions and report the critical events like slow /small leakages in natural gas/oil pipeline autonomously. Machine learning is performed on negative pressure wave (NPW) to identify events based on raw data gathered by individual sensor nodes in network. In machine learning, we use support vector machine (SVM), K-nearest neighbor (KNN) and Gaussian mixture model (GMM) and Naive bayes in multi- dimensional feature space. The proposed technique is investigated for performance and capabilities by a series of trials on a field deployed test bed, with regard to performance of leakage detection and size estimation in pipelines.

Experimental study of pipeline leak detection based on hoop strain measurement

Pipelines are widely used for the transport of a large variety of fluids, such as natural gas, across long distances. While pipelines provide a convenient mode of transportation of fluids, their safe usage is one of the foremost concerns especially if their contents are harmful to the environment or if the hosting area is prone to third-party intrusions. Thus, rapid detection and localization of pipeline leakage is paramount to the minimization of damage brought to the environment and stakeholders in the event of an unexpected leakage. In this work, a novel hoop strain based negative pressure wave (NPW) approach was used to detect and localize pipeline leakages in a 180 ft PVC pipeline equipped with five manually controllable leakage points. Using the new approach, both the arrival time of the NPW and the energy attenuation profile of the NPW can be used to detect and localize leakages with higher accuracy and in a wider variety of situations. The time of arrival approach allowed accurate (within 7.33% error) and repeatable localization of the leakage points; however, using the energy attenuation of the NPW, leakages with low leakage rates (<5 lpm) can also be detected, albeit at the cost of inaccuracies at the inlet and outlet of the pipeline (8.3% error in the ends vs. 4.3% error in the body). Copyright © 2014 John Wiley & Sons, Ltd.

Leakage Diagnosis Method for Pipelines Based on Multi-Weight Neural Network

For reasons of low accuracy of traditional leakage, a pipeline leakage diagnosis method based on multi-weight neural networks is presented to recognized leak signal in city gas pipelines. By using analysis and modeling a multi-weight neural networks are established at normal node to simplify network structure. The Information entropy of leakage characteristic parameters of negative pressure wave was used as input eigenvector respectively for primary diagnosis. It has been applied for leakage diagnosis in city gas pipelines with the whole computational process done by a computer. Results of simulation and tests show that this method has its advantage in dealing with multi-coupled fault situations and is featured by a high probability of accuracy, which not only proves the method to be effective, but also provides a theoretical basis and a new way for leak diagnosis of other pipelines.

Integrated-signal-based leak location method for liquid pipelines

Leaks in pipelines can cause major incidents resulting in both human injuries and financial losses. Among the considerable leak detection and location methods, the Negative Pressure Wave (NPW) based method has been widely used in locating leaks in liquid pipelines. The NPW based method only monitors the pressure changes at two ends of a pipeline. But the pressure is apt to be fixed by the end equipment and the change of it induced by a small or slow leakage is too small to be detected, which limit the application of the NPW based method in these situations. This paper presents a novel leak location method based on integrated signal, which is a combination of the pressure and flow rate signals. The representation of the integrated signal is derived from the transient analysis of the leakage. For the change of the integrated signal induced by a leakage is larger than the pressure change and it is also unaffected by the end equipment, the proposed method can be used to detect and locate small or slow leakage in a pipeline and can also be used in pipelines which end pressures are fixed by some kinds of equipment. The validation of the proposed method also confirms its advantages.

A study on the effect of intraglottal vortical structures on vocal fold vibration

Recent investigations suggested possible formation of the vortical structures in the intraglottal region during the closing phase of the phonation cycle. Vortical regions in the flow field are locations of negative pressure, and it has been hypothesized that this negative pressure might facilitate the glottal closure and thus affects the vibration pattern and voice production for high subglottal pressures. However, it is unclear whether the vortex-induced negative pressure is large enough, compared with vocal fold inertia and elastic recoil, to have a noticeable effect on glottal closure. In addition, the intraglottal vortical structures generally exist only for a small fraction of the closing phase when the glottis becomes divergent enough to induce flow separation. In the current work, oscillation of the vocal folds and the flow field are modeled using a non-linear finite element solver and a reduced order flow solver, respectively. The effect of vortical structures is modeled as a sinusoidal negative pressure wave applied to vocal fold surface between the flow separation point and the superior edge of the vocal folds. The effects of this vortex-induced negative pressure are quantified at different conditions of vocal fold stiffness and subglottal pressures. [Work supported by NIH.]

Fiber-Optic Distributed Vibration Sensor for Pipeline Pre-Alarm

Pipeline security is very important in oil and gas transportation. Traditional technology such as negative pressure wave detection, acoustic detection doesn’t match the need of pre-alarm. A fiber-optic distributed vibration sensor (FDVS) is proposed and investigated. It is found that the FDVS has the advantages of long sensing distance, high spatial resolution, high sensitivity, high reliability, etc. According to calculating the time delay of the two MZIs , the vibration can be located.

About Possible Role of Negative Pressure Waves in Geophysical Processes

There are different model views on the mechanism of extreme geologic processes. Nevertheless, many principal questions remain unanswered because the models don’t characterize sufficiently the peculiarities of these processes. Here is proposed new approach to mechanism of evolution and appearance of extreme geologic processes on the basis of Negative Pressure phenomenon.

Experimental study of leakage detection of natural gas pipeline using FBG based strain sensor and least square support vector machine

Leakage is the most common cause of natural gas pipeline accidents. This work was devoted to natural gas pipeline leakage detection, which is based on detecting negative pressure wave signals caused by leakage. The FBG strain sensor, which is based on monitoring the hoop strain of a pipeline to detect negative pressure wave signals, is fabricated and experimentally tested. Compared to conventional pressure sensors, FBG strain sensors were shown to be less influenced by noise, and they have the advantage of being a nondestructive sensing method. This makes them ideal for sensing pressure transients, which could be analyzed to detect natural gas pipeline leakage. Toward this objective, a least square support vector machine (LS-SVM) classifier was developed as an automatic leakage detection technique. This technique proved to be effective at detecting leakage.

Multi-chamber actuated micro-dispensing with a single nozzle for sub-nanoliter droplet formation

A novel concept of single-nozzle micro-dispensing device with multiple pressurizing chambers is proposed for high-throughput drug screening applications such as arraying new drug candidates with sub-nanoliter volume. The theoretical study with a simplified electrical circuit model of the fluidic system shows that the proposed model is effective to sustain jetting stability at high frequency due to an increase in the natural frequency of the fluidic system and high attenuation of the negative pressure wave in the fluidic system. The fabricated device was able to form uniform droplets at up to 7 kHz having 115 pL (1.15 × 10−10 L) in volume and 1.8 m s−1 ∼ 2.5 m s−1 in velocity.

A Gas Pipeline Leakage Diagnosis of Fusing BP Neural Network Basing on WSN and D-S Theory

For reasons of low accuracy of artificial survey leakage, a gas pipeline leakage diagnosis method based on BP neural networks and D-S theory is presented by introducing WSN and information fusion theory. Two sub-neural networks are established at normal node to simplify network structure. The leakage characteristic parameters of negative pressure wave and acoustic emission signals are used as input eigenvector respectively for primary diagnosis. Through making preliminary fusion result s as the basic probability assignment of evidence, the impersonal valuations are realized. Finally, all evidences are aggregated at normal and sink node respectively by using the improved combination rules. The method makes full use of redundant and complementary leakage information. Numerical example shows that the proposed improves the leakage diagnosis accuracy and decreases the recognition uncertainty.

Pipeline Leak Detection Based on Double Sensor Negative Pressure Wave

Pipeline leak lead to huge economic losses and environmental pollution. Leak detection system based on single sensor negative pressure wave often causes false alarm. In this paper the double sensor method is adopted to exclude false alarm by determining the propagation direction of the pressure wave. In order to remove the inverse coherent interference caused by pump running, the phase difference of primary low frequency component is used to identify the sign of the time delay between the double sensors. The experiment shows the mothod is effective.

An Improved Negative Pressure Wave Method for Natural Gas Pipeline Leak Location Using FBG Based Strain Sensor and Wavelet Transform

Methods that more quickly locate leakages in natural gas pipelines are urgently required. In this paper, an improved negative pressure wave method based on FBG based strain sensors and wavelet analysis is proposed. This method takes into account the variation in the negative pressure wave propagation velocity and the gas velocity variation, uses the traditional leak location formula, and employs Compound Simpson and Dichotomy Searching for solving this formula. In addition, a FBG based strain sensor instead of a traditional pressure sensor was developed for detecting the negative pressure wave signal produced by leakage. Unlike traditional sensors, FBG sensors can be installed anywhere along the pipeline, thus leading to high positioning accuracy through more frequent installment of the sensors. Finally, a wavelet transform method was employed to locate the pressure drop points within the FBG signals. Experiment results show good positioning accuracy for natural gas pipeline leakage, using this new method.

Application Research of the Signal Processing Method Based on Db5 Wavelet Transform in Leakage Location of Ship Pipeline

In the complex ship noise environment, the effective signal should be extracted before the leakage signals of acquisition which often mix with noise signal do further analysis. So the signal processing method based on db5 wavelet transform is put forward. In order to make the signal-to-noise separation and restore the original signal waveform, wavelet de-noising processing is done for the pressure sensors receiving signal. Finally, the negative pressure wave method is used to find leakage location of ship pipeline. The experiment proved that the signal processing method based on db5 wavelet transform is suitable for complex ship noise environment and improves location accuracy.

Improvement in Accuracy of Ultrasonic Measurement of Transient Change in Viscoelasticity of Radial Arterial Wall Due to Flow-Mediated Dilation by Adaptive Low-Pass Filtering

In our previous study, the stress–strain relationship of the radial arterial wall was measured and the viscoelasticity of the intima–media region was estimated from the stress–strain relationship. Furthermore, the transient change in viscoelasticity due to flow-mediated dilation (FMD) was estimated by the automated detection of wall boundaries. In the present study, the strain rate was adaptively filtered to improve the accuracy of viscoelasticity estimation by decreasing the high-frequency noise. Additionally, in a basic experiment, this method was validated using a silicone tube (simulating artery). In the basic experiment, the elasticity was estimated with a mean error of 1.2%. The elasticity measured at each beam position was highly reproducible among measurements, whereas there was a slight variation in measured elasticity among beams. Consequently, in in vivo measurements, the normalized mean square error (MSE) was clearly decreased. Additionally, the stress–strain relationship of the radial arterial wall was obtained and the viscoelasticity was estimated accurately. The inner small loop, which corresponds to the negative pressure wave caused by the closure of the aortic valve, can be observed using the adaptive low-pass filtering (LPF). Moreover, the transient changes in these parameters were similar to those in the previous study. These results show the potential of the proposed method for the thorough analysis of the transient change in viscoelasticity due to FMD.

Leak detection of gas pipelines based on Wigner distribution

ABSTRACTLeak detection of gas pipelines based on acoustic signals has drawn intensive attention in recent years because of its advantages over the widely adopted method based on negative pressure wave. However, detection of a small leakage in a long gas pipeline with a low pressure and complex disturbances is still a challenging problem, because in this case, the leak acoustic signal that arrives at a transducer may be weak and thus thoroughly covered by disturbances. This paper reports on the application of the Wigner–Ville distribution (WVD) method in leak detection of gas pipelines based on acoustic signals. The WVD of the acoustic transducer signal is used to find the sensitive frequency band corresponding to a leak and to detect the leak acoustic signal from strong disturbances. To suppress the influence of cross terms of the WVD, we adopted a time‐averaging WVD. Offline tests based on historical leak experimental data of a real pipeline show the effectiveness of the method in detecting leak faults of a long gas pipeline with a low pressure and strong disturbances. © 2012 Curtin University of Technology and John Wiley &amp; Sons, Ltd.

Synchronous Endoscopy and Sonotubometry of the Eustachian Tube

To describe for the first time a method of recording of Eustachian tube (ET) function by simultaneous and synchronous endoscopy and sonotubometry and explore its advantages compared with the performance of these tests independently. Observational study. Academic tertiary medical center. Eighteen healthy subjects. Endoscopic nasopharyngoscopy with simultaneous, synchronous sonotubometry. Each subject performed three maneuvers: pronouncing the constant "k", swallowing and yawning. The number of ET opening as a fraction of all efforts to open the tube, the duration of each opening and the sound intensity recorded by sonotubometry. Six (35.3%) of 17 subjects used for data analysis did not open their ET during swallowing. Excluding nonopeners, the ET opened in 3 of 4 of the swallows. The average duration of opening of the ET during swallowing was 0.44 seconds. The ET does not open every time the endoscopic view notes dilation. A negative sound pressure wave was recorded in a number of instances, immediately preceding a swallow-related opening. Contraction of the tensor veli palatini muscle was essential for ET opening. Simultaneous synchronous endoscopy and sonotubometry may improve the accuracy of either performed separately as an ET function measurement tool. Sonotubometry may prevent a false-positive endoscopy (ET viewed as open but no functional patency achieved). Endoscopy can lower the threshold considered as positive for sonotubometry. A negative pressure wave recorded by sonotubometry may reflect the ET role of clearing the middle ear of secretions toward the nasopharynx. This novel measurement technique provided additional evidence that the tensor veli palatini muscle provides the final opening action of the ET.

Application of SOM Network in Ship Pipeline Leakage Detecting

At present, ship pipeline leakage has become a great hidden risk of safe navigation and environmental pollution, but piping detecting technology mostly focuses on long-distance oil and gas pipeline, and does a little on the complicated pipeline system, for example, ship pipeline system. The frequently-used leakage detecting of negative pressure wave method, because the frequent adjustable pump or reset valve of ship pipeline system will also produce the negative pressure wave, may easily fail to report or even misreport. In order to monitor ship pipeline leakage effectively and greatly reduce fault alarm rate and missing alarm rate, SOM network (self-organizing feature map neural network) had been used to identify leakage from different working conditions. At first, the waveform characteristics of pressure and flow signals were analyzed by kurtosis calculating to obtain condition eigenvectors. From data sampling in terms of pipe working conditions, learning samples were obtained. Accordingly, the nonlinear mapping between SOM neural network inputs and outputs were well established via training. Afterwards, ship piping leakage was detected based on input eigenve

Detection of small leakage from long transportation pipeline with complex noise

In the long distance pipeline remote monitoring system, small leak detection becomes an important issue. Weak singularities in small leak signals are usually difficult to detect precisely under complicated noise background, which may cause false alarm or miss alarm. The advantage of applying the harmonic wavelet method is explored in this paper. Pipeline small leak sensitive characteristics are recognized and the negative pressure wave inflexions are extracted by harmonic wavelet analysis, expressed in terms of harmonic wavelet time-frequency mesh map, time-frequency contour map, and time-frequency profile plot. This paper also presents a comparative study of both Daubechies wavelet and harmonic wavelet analysis when applied to pipeline small leak detection under complicated background noises. Results of simulating test and field experiment show that it is possible to distinguish weak non-stationarities from complicated noises by harmonic wavelet analysis in pipeline small leak detection system. The comparison clearly illustrates that harmonic wavelet based pipeline small leakage detection method is significantly more accurate than other wavelets analysis such as Daubechies wavelet. This work provides a reliable and safe guarantee for oil and gas long distance transportation, reducing petroleum product losses and protecting surrounding environment.

Sonothrombolysis for the treatment of acute stroke: current concepts and future directions

Achieving rapid reperfusion transcranial color-coded duplex is the critical issue in acute stroke treatment. Ultrasound (US) generates negative pressure waves that are associated with an increase in either intrinsic or intravenous tissue plasminogen activator (tPA)-induced fibrinolytic activity. Higher rates of tPA-induced arterial recanalization, associated with a trend towards better functional outcome, have been safely achieved by using high-frequency US. By contrast, the use of low-frequency US and transcranial color-coded duplex has been linked to significant hemorrhagic complications. US-accelerated thrombolysis has been safely enhanced by lowering the amount of energy needed for acoustic cavitation with the administration of microbubbles. Other applications of US are being studied, including its intra-arterial use. Operator-independent devices, which will spread the use of these US techniques further, are also being developed. This article reviews the present status of sonothrombolysis in acute stroke treatment, highlighting both experimental and clinical studies addressing this issue, and discusses its future regarding both efficacy and safety.