scholarly journals Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Lin Gao ◽  
Lili Dong ◽  
Jianguo Cao ◽  
Shaofeng Wang ◽  
Wenjing Liu
Keyword(s):  
Acoustic Emission ◽  
Leak Detection ◽  
Average Frequency ◽  
Kernel Functions ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Pipeline System ◽  
Small Leak ◽  
Pipe Joints ◽  
Pipe Joint

For pipes connected by pipe joints, leaks in the pipeline system are likely to occur at the pipe joints as opposed to the tube itself. Thus, early detection is critical to ensure the safety of the pipeline system. Based on acoustic emission (AE) techniques, this paper presents an experimental research on small leak detection in gas distribution pipelines due to loosening of the pipe joint connection. Firstly, the acoustic characteristics of leak signals are studied; then, features of signals are extracted. Finally, a classifier based on the support vector machine (SVM) technology is established, and the qualified features are selected to detect the leak. It is verified that the main frequency of the AE small leak signal due to the failure of the pipe joint is focused in the range of 33–45 kHz, and the algorithms based on SVM with kernel functions all can reach a better estimation accuracy of 98% using the feature “envelope area” or the feature set {standard deviation (STD), root mean square (RMS), energy, average frequency}.

scholarly journals Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline

2020 ◽  
Vol 10 (6) ◽  
pp. 1933
Author(s):  
Bach Phi Duong ◽  
JaeYoung Kim ◽  
Inkyu Jeong ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Support Vector ◽  
Pipeline System ◽  
Background Signal ◽  
Crucial Information ◽  
Fault Diagnosis System ◽  
Multi Level ◽  
The One ◽  
Constant Fault ◽  
Different Levels

Acoustic emission bursts are signal waveforms that include a number of consecutive imbrication transients with variable strengths and contain crucial information on the leakage phenomenon in a pipeline system. Detection and isolation of a burst against the background signal increases the ability of a pipe’s fault diagnosis system. This paper proposes a methodology using the Enhanced Constant Fault Alarm Rate (ECFAR) to detect bursts and exploit the burst phenomenon in acoustic emission. The extracted information from the burst waveform is used to distinguish several levels of leakage in a laboratory leak-off experimental testbed. The multi-class support vector machine in the one-against-all method is established as the classifier. The results are compared with those of the wavelet threshold-based method, another algorithm utilized for impulse and burst detection, which indicates that the ECFAR method gives an ameliorative classification result with an accuracy of 93% for different levels of leakage.

scholarly journals Study on the Physical Properties of Split-type Iron Base Shape Memory Alloy Pipe Joint

2021 ◽  
Vol 2083 (2) ◽  
pp. 022072
Author(s):  
Yun Tian ◽  
Qi Yao ◽  
Dong Jiang ◽  
Yanni Xiao ◽  
Licheng Liu ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Pipeline System ◽  
Pull Out ◽  
Pressure Pipeline ◽  
Pipe Joints ◽  
Iron Based ◽  
Pulling Force ◽  
Pipe Joint

Abstract Aiming at the leakage phenomenon of pipeline system due to corrosion and other reasons, a split type iron-based shape memory alloy pipe joint based on the button bond connection was designed for repair. This split-type pipe joint can be used for quick in-situ maintenance of pipelines without cutting off pipelines or stopping transmission without pressure relief. It is especially suitable for intensive pipeline maintenance and online emergency maintenance. In order to study the connection performance of the split iron-based shape memory alloy pipe joint, this paper took the Fe17Mn4Si10Cr4Ni alloy pipe joint as the research object, and carried out pressure sealing and pull-out experiments respectively on the integral pipe joint and the split pipe joint which had not been carried out and had been subjected to one heat and mechanical training. The results showed that, compared with the integral pipe joint, the pressure seal value of the split pipe joint decreased by 40% to 20MPa and the pulling force decreased by 48.9% without heat-mechanical training. However, after 1 time of thermal-mechanical training, the pressure sealing value of the split-type pipe joint decreased by 12.5% to 32MPa and the pulling force decreased by 21.2% compared with the integral pipe joint. It can be seen that thermal-mechanical training can significantly improve the joint performance of split pipe joints, and the split pipe joint can meet the requirements of medium and low pressure pipeline connection and maintenance.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Evaluation of the mechanical properties of Inconel 718 to SCM 440 dissimilar friction welding through real-time monitoring of the acoustic emission system

2021 ◽  
pp. 146442072199383
Author(s):  
Yu Sik Kong ◽  
Muralimohan Cheepu ◽  
Jin-Kyung Lee
Keyword(s):  
Acoustic Emission ◽  
Friction Welding ◽  
Inconel 718 ◽  
Low Voltage ◽  
Dissimilar Materials ◽  
Average Frequency ◽  
Very High Frequency ◽  
Cumulative Count ◽  
Emission System ◽  
Very High

Friction welding was chosen for its versatility in the joining of dissimilar materials with high quality. The aim of this study is to determine the optimal welding conditions for attaining quality joints by using online monitoring of acoustic emission system signals. During friction welding, the formation of cracks, defects, or any abnormalities in the joining process which have a detrimental effect on the joints quality was identified. The most widely used materials in the aerospace industry—Inconel 718 and molybdenum steel—were joined by friction welding. The precision of the joints, internal defects, and quality are major concerns for aerospace parts. The results of the present research determined the optimal welding conditions for high tensile strength by nondestructively inducing acoustic emission signals. During friction time and upset time periods, the typical waveforms and frequency spectrum of the acoustic emission signals were recorded, and their energy level, average frequency, cumulative count, and amplitude were analyzed. Both cumulative count and amplitude were found to be useful parameters for deriving the optimal welding conditions. In the initial stage of friction welding, a very high voltage of continuous form was generated with frequency characteristics of 0.44 MHz and 0.54 MHz. The signals generated during the upset stage had a low voltage, but a very high frequency of 1.56 MHz and 1.74 MHz with a burst-type signal. The amplitude of the signal generated for the optimally welded joints was about 100 dB at the friction time and about 45 dB at the upset time.

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

Leak detection and localization for pipelines using multivariable fuzzy learning backstepping

2021 ◽  
pp. 1-12
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Transmission Lines ◽  
Leak Detection ◽  
Support Vector ◽  
Robust Learning ◽  
Regression Techniques ◽  
Space Formulation ◽  
Points Of View ◽  
Detection And Localization ◽  
Fuzzy Learning ◽  
Backstepping Algorithm

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups.

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