The Arc Sound Characteristic on MIG Weld Penetration

2010 ◽  
Vol 97-101 ◽  
pp. 3948-3951 ◽  
Author(s):  
Li Jun Liu ◽  
Lan Hu
Keyword(s):  
Welding Process ◽  
Sound Signal ◽  
Weld Penetration ◽  
Time Frequency ◽  
Sample Data ◽  
On Line ◽  
The Relationship ◽  
Arc Sound ◽  
Arc Sound Signal ◽  
Selection Of

Arc sound signal in MIG welding contains plenty of welding information, which is an important signal source of weld penetration. Aiming at on-line monitoring of weld penetration, the relationship between arc sound signal and penetration is analyzed in time-frequency domain through a reasonable selection of sample data. The results show that the spectrum of arc sound signal and penetration status are highly correlative. The changes of penetration can accurately detected by variation of signal energy from 1500 Hz to 4500 Hz in welding process. The above experiments verify the feasibility of monitoring the penetration through arc sound signal and provides theory and application foundation for penetration adaptive control.

A novel method for detecting the pantograph–catenary arc based on the arc sound characteristics

2018 ◽  
Vol 233 (5) ◽  
pp. 506-515 ◽  
Author(s):  
Wenfu Wei ◽  
Chongliang Liang ◽  
Zefeng Yang ◽  
Pan Xu ◽  
Xin Yan ◽  
...  
Keyword(s):  
High Speed ◽  
Average Energy ◽  
Sound Signal ◽  
Time Duration ◽  
Detection And Identification ◽  
Actual Operation ◽  
Catenary System ◽  
Line Level ◽  
Arc Sound ◽  
Arc Sound Signal

In recent years, the speed and the current transmission density of high-speed railways are increased. As a result, the rate of the occurrence of pantograph–catenary arc also increases, which directly influences the service performance of the pantograph–catenary system. The detection and identification of the pantograph–catenary arc is of great significance for evaluating the off-line level to ensure the safe operation of the pantograph–catenary system. The currently used conventional optical arc detection method would be distorted by strong sunlight or other environmental lights in the actual operation, which would affect the accuracy of detection. On the contrary, the arc sound signal is not affected by the diurnal variation and can be easily obtained; it can also be distinguished well from other interfering sounds. In this paper, a new method that uses the arc sound is proposed to realize the effective detection of the pantograph–catenary arc. The frequency spectrum and the intensity characteristics of the pantograph–catenary arc sound are analyzed. The results show that the frequency band of the arc sound signal is wide and mainly gets distributed at 5–17 kHz. The curve of the sound waveform is drawn by calculating the short-time average energy, which is used to obtain the arc’s starting time, duration, and intensity, for calculating the off-line rate.

Time-Domain Characteristics Analysis of Penetration Arc Sound Signal in MIG Welding

2010 ◽  
Vol 154-155 ◽  
pp. 453-456
Author(s):  
Li Jun Liu ◽  
Qi Wang ◽  
Lan Hu
Keyword(s):  
Time Domain ◽  
Sound Signal ◽  
Pitch Estimation ◽  
Zero Crossing ◽  
Butt Welding ◽  
Spray Transfer ◽  
Characteristics Analysis ◽  
Short Time ◽  
Arc Sound ◽  
Arc Sound Signal

Many important time-domain characteristic parameters are extracted through the short-time analysis of arc sound signal in MIG butt welding with spray transfer, which can be used for the diagnosis on the weld penetration status. And the short-time autocorrelation function and short-time average amplitude difference function are adopted to pitch estimation. The analysis results show that the penetration status can be accurately recognized via the short-time energy, average magnitude, average zero-crossing and zero-to-energy and so on. Meanwhile, the pitch estimation of arc sound signal in experiments is at 220 points, that is 5 ms or 200 Hz in cycle. The methods and results provide a foundation for the diagnosis on penetration based on analysis of arc sound signal and have great theoretical meaning and practical value.

scholarly journals On the Assessment of Homogeneity in a Uniform Regression Model

2020 ◽  
pp. 115-118
Author(s):  
T.P. Makhaeva ◽  
I.V. Ponomarev
Keyword(s):  
Regression Model ◽  
Correlation Coefficient ◽  
Probabilistic Approach ◽  
Geometric Interpretation ◽  
Functional Dependencies ◽  
Initial Stage ◽  
Correlation Component ◽  
Sample Data ◽  
The Relationship ◽  
Selection Of

When conducting an exploratory analysis of the data and the subsequent construction of functional dependencies between the observed phenomena, it is often necessary to assess the degree of dependence between the studied data. The basis for obtaining such criteria with a probabilistic approach usually includes the correlation component of the sample. The choice of the used indicator directly depends on the methods of studying the sample, as well as the tools for constructing the model. In most cases, at the initial stage of modeling, it is precisely the homogeneity estimates of the sample that are studied, a good selection of which can reduce the complexity of constructing the relationship between the data.In this paper, we study a method for assessing  the uniformity of sample data when constructing a uniform regression model. The first part of the paper describes the correlation coefficient for the L∞ regression, studies the interval of its change, describes the geometric interpretation and the algorithm for constructing this indicator. In the second part of the paper, we study the method of constructing an indicator of "concentration" of the sample. For this, formulas are derived that relate the correlation coefficient to the magnitude of the original sample. In conclusion, a description is given of the algorithms for constructing the considered indicators, and conclusions are drawn about the complexity of these algorithms.

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