scholarly journals Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3628 ◽  
Author(s):  
Shengshan Pan ◽  
Zhengdan Xu ◽  
Dongsheng Li ◽  
Dang Lu
Keyword(s):  
Acoustic Emission ◽  
Wavelet Decomposition ◽  
Estimation Method ◽  
Reference Value ◽  
Time Delay Estimation ◽  
Operating Conditions ◽  
Support Vector ◽  
Acoustic Emission Sensor ◽  
Mode Decomposition ◽  
Acoustic Emission Sensors

Because of the inconvenience of installing sensors in a buried pipeline, an acoustic emission sensor is initially proposed for collecting and analyzing leakage signals inside the pipeline. Four operating conditions of a fluid-filled pipeline are established and a support vector machine (SVM) method is used to accurately classify the leakage condition of the pipeline. Wavelet decomposition and empirical mode decomposition (EMD) methods are initially used in denoising these signals to address the problem in which original leakage acoustic emission signals contain too much noise. Signals with more information and energy are then reconstructed. The time-delay estimation method is finally used to accurately locate the leakage source in the pipeline. The results show that by using SVM, wavelet decomposition and EMD methods, leakage detection in a liquid-filled pipe with built-in acoustic emission sensors is effective and accurate and provides a reference value for real-time online monitoring of pipeline operational status with broad application prospects.

scholarly journals Experimental Investigation of the Diagnosis of Angular Contact Ball Bearings Using Acoustic Emission Method and Empirical Mode Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ramtin Tabatabaei ◽  
Aref Aasi ◽  
Seyed Mohammad Jafari ◽  
Enrico Ciulli
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Early Stage ◽  
Signal To Noise Ratio ◽  
Operating Conditions ◽  
Outer Race ◽  
Mode Decomposition ◽  
Speed Performance ◽  
Envelope Spectrum

Early detection of angular contact bearings, one of the important subsets of rolling element bearings (REBs), is critical for applications of high accuracy and high speed performance. In this study, acoustic emission (AE) method was applied to an experimental case with defects on angular contact bearing. AE signals were collected by AE sensors in different operating conditions. Signal to noise ratio (SNR) was calculated by kurtosis to entropy ratio (KER), then acquired signals were denoised by empirical mode decomposition (EMD) method, and optimal intrinsic mode function (IMF) was selected by the proposed method. Finally, envelope spectrum was applied to the denoised signals, and frequencies of defects were obtained in different rotating speeds, loadings, and defect sizes. For the first time, a small defect with width of 0.3 mm and loading of 475 N was detected in early stage of 0.04 KHz. Moreover, a comparison between theoretical and extracted defect frequencies suggested that our method successfully detected localized defects in both inner and outer race. Our results show promise in detecting small size defects in REBs.

scholarly journals Status recognition for fused deposition modeling manufactured parts based on acoustic emission

2019 ◽  
Vol 95 ◽  
pp. 01005 ◽  
Author(s):  
Feng Li ◽  
Zhonghua Yu ◽  
Xuanwei Shen ◽  
Hao Zhang
Keyword(s):  
Acoustic Emission ◽  
Fused Deposition Modeling ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Fused Deposition Modelling ◽  
Fused Deposition ◽  
Mode Decomposition ◽  
The Status ◽  
Ae Signal ◽  
Status Recognition

Fused deposition modelling (FDM), as one technology of additive manufacturing, fabricates parts always with curl and looseness defects which restrict its development to a great extent. In this paper, a method based on acoustic emission (AE) was proposed to recognise the status of the manufactured part in FDM process. Experiments were carried out to acquire the AE signal when the printing part was respectively in normal, looseness and curl state. The ensemble empirical mode decomposition (EEMD) was employed to the process of feature extraction and both the methods of Hidden-semi Markov model (HSMM) and support vector machine(SVM) were applied to recognise the three states of the normal, looseness and curl. The results reveal that the combination of EEMD and HSMM makes it more accurate to recognize these three states.

Characterization of Tape Edge Contact Force With Acoustic Emission

2007 ◽  
Vol 129 (4) ◽  
pp. 525-529 ◽  
Author(s):  
Bart Raeymaekers ◽  
Frank E. Talke
Keyword(s):  
Acoustic Emission ◽  
Contact Force ◽  
Operating Conditions ◽  
Dimensional Model ◽  
Tape Drive ◽  
Edge Contact ◽  
One Dimensional ◽  
Acoustic Emission Sensors ◽  
Tape Edge

Acoustic emission sensors were used to detect contact between a moving tape and the flange of a tape guide. The influence of tape drive operating conditions on the tape edge contact force was studied. A one-dimensional model was developed to predict the magnitude of tape/flange impact. The model fits the experimental data well.

Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

2012 ◽  
Vol 271-272 ◽  
pp. 932-935
Author(s):  
Hong Ying Hu ◽  
Wen Long Li ◽  
Feng Qiang Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Estimation Method ◽  
Regression Function ◽  
Regression Estimation ◽  
Function Estimation ◽  
Wavelet Regression ◽  
Nonparametric Regression Estimation ◽  
Mode Decomposition ◽  
Non Stationary Signal

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate trend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet regression estimation method, a new regression function estimation method based on EMD is presented. The simulation proves the advantages of the approach with easy computation and more accurate result.

scholarly journals A Novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator

2020 ◽  
Vol 6 (4) ◽  
Author(s):  
Ali Tabrizi ◽  
Luigi Garibaldi ◽  
Alessandro Fasana ◽  
Stefano Marcchesiello
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Ensemble Empirical Mode Decomposition ◽  
Operating Conditions ◽  
Support Vector ◽  
Roller Bearings ◽  
Support Vectors ◽  
Mode Decomposition ◽  
Eemd Method

Although Ensemble empirical mode decomposition (EEMD) method has been successfully applied to various applications, features extracted using EEMD could not detect anomalies for roller bearings, especially when anomalies includes small defects. In this study a novel feature extraction method is proposed to detect the state of roller bearings. Performance improved EEMD, which is a reliable adaptive method to calculate an appropriate noise amplitude is applied to decompose the acceleration signals into zero-mean components called intrinsic mode functions (IMFs). Then, three dimensional feature vectors are created by applying the Teager-Kaiser energy operator (TKEO) to the first three IMFs. The novel features obtained from the healthy bearing signals are utilized to construct the separating hyperplane using one-class support vector machine (SVM). In order to validate the method proposed, a number of operating conditions (shaft speed and load) are considered to generate the data (vibration signals) by means of an assembled test rig. It is shown that the proposed method can successfully identify the states of the new samples (healthy and faulty). The uncertainty of the model prediction is investigated computing Margin and the number of support vectors. It create less complex (less fraction of support vectors) and more reliable (higher Margin) hyperplane than the EEMD method.

Acoustic Emission Sensors using Silicon Mechanical Resonators

2015 ◽  
Vol 135 (12) ◽  
pp. 484-489
Author(s):  
Kengo Takata ◽  
Takashi Sasaki ◽  
Mitsutomo Nishizawa ◽  
Hiroshi Saito ◽  
Shinsuke Yamazaki ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Mechanical Resonators ◽  
Acoustic Emission Sensors

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

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