scholarly journals Deep Learning Object-Impulse Detection for Enhancing Leakage Detection of a Boiler Tube Using Acoustic Emission Signal

2019 ◽  
Vol 9 (20) ◽  
pp. 4368 ◽  
Author(s):  
Bach Phi Duong ◽  
Jaeyoung Kim ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Power Plants ◽  
Thermal Power ◽  
High Amplitude ◽  
Leakage Detection ◽  
Boiler Tube ◽  
Emission Signal ◽  
Coal Fuel ◽  
Ae Signal

Advances in technology have enhanced the ability to detect leakages in boiler tube components in thermal power plants. As a specific issue, the interaction between the coal fuel stream and the boiler tube membrane generates random and high-amplitude impulses, which negatively affect the measured acoustic emission (AE) signal from leakages. It is essential to detect and practically handle these kinds of impulses. Based on the object detection concept, this paper proposes an impulse detection methodology that employs deep learning flexible boundary regression (DLFBR). First, the shape extraction (SE) preprocessing technique is implemented to yield the shape signal, which contains intrinsic information about the impulse from the raw AE signal. Then, DLFBR extracts and generates both the feature map and the confidence mask from the shape signal to regress a boundary box, which specifies the position of the impulse. For illustration purposes, the proposed algorithm is applied to an experimental leakage detection dataset recorded from a subcritical boiler unit with a tube membrane. Experimental results show that the proposed method is effective for detecting impulses of leakage in a boiler tube testbed, providing 99.8% average classification accuracy.

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

scholarly journals Experimental study of the thermochemical treatment of the low-grade coal prior to boiler combustion at coal-fired power station

2019 ◽  
Vol 109 ◽  
pp. 00119
Author(s):  
Volodymyr Yemelianenko ◽  
Vitalii Pertsevyi ◽  
Oleksandr Zhevzhyk ◽  
Iryna Potapchuk ◽  
Oleksandr Lutai
Keyword(s):  
Experimental Study ◽  
Power Plants ◽  
Thermal Power ◽  
Thermochemical Treatment ◽  
Gas Pressure ◽  
Pulverized Coal ◽  
Time Range ◽  
Low Grade ◽  
Boiler Furnace ◽  
Coal Fuel

Analysis of the perspectives of the coal fuel for thermal power plants is carried out. The necessity of the experimental study for temperature measurement in the boiler furnace. The results of the experimental study are presented: temperature change over time at the burner outlet for different constant pressure value of the backlighting gas, dependence of the temperature at the burner outlet from the backlighting gas pressure for constant concentration value of pulverized coal in coal-air mixture, dependence of the temperature at the burner outlet from the concentration of pulverized coal in coal-air mixture for constant value of the backlighting gas pressure, temperature measurements for constant backlighting gas pressure value, constant value of the concentration of pulverized coal in coal-air mixture when plasmatron is switched and operates for some time range. The results of the study could be applied to the solid fuel treatment for different thermal units.

Acoustic Emission Signal Classification Using Feature Analysis and Deep Learning Neural Network

2020 ◽  
pp. 2150030
Author(s):  
Jian-Da Wu ◽  
Yu-Han Wong ◽  
Wen-Jun Luo ◽  
Kai-Chao Yao
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
Signal Classification ◽  
Learning Method ◽  
Identification Rate ◽  
Emission Signal ◽  
Identification Time ◽  
The Impact

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

The Research on Leak Detection Technology of Natural Gas Pipeline Based on EMD

2014 ◽  
Vol 494-495 ◽  
pp. 793-796 ◽  
Author(s):  
Chuan Jiang Li ◽  
Jia Pan Zhang ◽  
Zi Qiang Zhang ◽  
Ju Li Hu ◽  
Yi Li
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Leakage Detection ◽  
Intrinsic Mode Functions ◽  
Emission Signal ◽  
Natural Gas Pipeline ◽  
Detection Technology ◽  
Mode Decomposition ◽  
Leakage Characteristics ◽  
Signal Processing Methods

The acoustic emission signal of pipeline leakage is characterized by nonlinear and non-stationary. It is not feasible to extract the leakage feature signal in traditional signal processing methods. The leak locations can be detected by employing the improved empirical mode decomposition (EMD) to decompose the acoustic emission signal into several intrinsic mode functions (IMF), choosing IMFs containing leakage characteristics to be reconstructed, and doing correlation analysis. Experimental results show that the positioning accuracy of leakage detection is improved obviously.

Improved method to mitigate the effect of coal fuel switching on critical process parameters of a steam generator in a thermal power plant

2011 ◽  
Vol 225 (8) ◽  
pp. 1026-1040 ◽  
Author(s):  
S Dharmalingam ◽  
L Sivakumar ◽  
T Anandhi ◽  
M Umapathy
Keyword(s):  
Process Parameters ◽  
Power Plants ◽  
Thermal Power ◽  
Improved Method ◽  
Heating Value ◽  
Fuel Switching ◽  
Coal Fuel ◽  
Embedded Controller ◽  
Critical Process Parameters ◽  
Critical Process

The design and performance of steam generators supplied to thermal power plants are greatly influenced by the properties of coal burnt. All coals are not same and the variation of heating value of coal supplied to boiler results in changes in critical process parameters like pressure and temperature of main steam produced. These fluctuations are normally controlled by master pressure controller, provided the variation in heating value is within certain limits. If the heating value of coal being burnt varies substantially, a remedial measure is necessary to control the pressure and temperature fluctuations during coal fuel switching. The composition of the coal burnt currently in a boiler is determined from an online analyser, and an embedded controller computes the current heating value of the coal and suitably modifies the gains of all the controllers to arrest the undue fluctuations in pressure and temperature. A validated mathematical model for a typical 500 MW plant is used to simulate the variations in pressure and temperature of steam with normal and embedded controllers. Significant reduction in pressure and temperature variation has been achieved with an embedded controller. This article discusses the improved method of ensuring optimum boiler performance during coal fuel switchover.

Acoustic Emission (AE) Propagation Attenuation Theory and Rule in Non-Perfect Elastic Coal and Rock

2013 ◽  
Vol 477-478 ◽  
pp. 620-623
Author(s):  
Guo Wei Dong
Keyword(s):  
Numerical Simulation ◽  
Acoustic Emission ◽  
Quality Factor ◽  
Field Test ◽  
Propagation Distance ◽  
Signal Propagation ◽  
Emission Signal ◽  
Ae Signal ◽  
Propagation Rule ◽  
Quality Factor Q

Propagation rule of acoustic emission (AE) signal in coal and rock is an important basis when AE technique forecasts coal and rock dynamical disasters. Based on correlative theory of quality factor Q, Acoustic emission signal propagation attenuation formula in non-perfect elastic coal and rock are analyzed, Based on the theoretic formula, Effects of different quality factor and propagation distance on AE propagation attenuation are theoretically analyzed ;Based on theoretic analysis results, AE signal propagation numerical simulation and field test programs are designed, AE signal propagation rules in elastoplastic coal and rock are obtained. Field test and numerical simulation experimentation results validate rationality of theoretic forumla. Study production can guide AE technique that forecasts mine and rock dynamical disasters.

scholarly journals Investigation on recognition method of acoustic emission signal of the compressor valve based on CNN and LSTM network

2021 ◽  
Vol 252 ◽  
pp. 02023
Author(s):  
Yanfeng Wang ◽  
Jin Wang ◽  
Junwei Sun ◽  
Enhao Liang ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Dynamic Characteristics ◽  
Short Term Memory ◽  
Reciprocating Compressor ◽  
Short Term ◽  
Emission Signal ◽  
Ae Signal ◽  
Long Short Term Memory ◽  
Lstm Network

The valve is one of the important parts of the reciprocating compressor, which directly affects the thermodynamic process and reliability of the compressor. In this paper, acoustic emission (AE) technology is used to predict the dynamic characteristics of valves. The AE signal of the compressor valve is analyzed based on the deep learning method, and the mapping relation between the AE signal and the dynamic characteristics of the valve is obtained. The results show that the prediction accuracy of the models trained by Long Short-Term Memory (LSTM) artificial neural network and Convolutional Neural Network (CNN) is 97% and 95%, respectively, which can accurately predict the dynamic characteristics of the valve. Although the prediction results of CNN are slightly lower than that of LSTM network, the calculation speed of CNN is relatively faster.

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