Influence of explosion parameters on energy distribution of blast vibration signals with wavelet packet analysis

2011 ◽  
Author(s):  
Chunlei Zhang ◽  
Guosheng Zhong
Keyword(s):  
Energy Distribution ◽  
Wavelet Packet ◽  
Wavelet Packet Analysis ◽  
Blast Vibration ◽  
Vibration Signals

Study on Energy Distribution of Single-Hole Blasting Vibration Signal Based on Wavelet Packet Transform

2013 ◽  
Vol 321-324 ◽  
pp. 1284-1289
Author(s):  
Dong Tao Li ◽  
Li Xin Xu ◽  
Yuan Yuan Sun ◽  
Qiu Rui Jia ◽  
Jing Long Yan
Keyword(s):  
Energy Distribution ◽  
Concentration Distribution ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Vibration Signal ◽  
Vibration Velocity ◽  
Blasting Vibration ◽  
Wavelet Packet Analysis ◽  
Vibration Signals ◽  
Single Hole

It is conducive to reducedamage of blasting vibration to realize energy distribution and attenuation lawof single-hole blasting vibration signal. With the measured single-holeblasting vibration velocity curves, used wavelet packet analysis technologywith high-resolution character, the law of energy distribution of single-holeblasting vibration signals in different frequency bands, and the effect ofblasting source and distance from the source on single-hole blasting vibrationsignal energy distribution were analysised. The results show that the energy ofsingle-hole blasting vibration signals attenuation very quickly in thefrequency domain concentration distribution in 0~100Hz; and distance from thesource has significant influence on energy distribution in the frequencydomain; The energy is mainly distributed in the low frequency band whendistance from the source is larger, which has guiding significance inmitigation of blast-induced vibrations.

scholarly journals Frequency Spectrum and Wavelet Packet Analyses of Blasting Vibration Signals for Different Charge Structures in Blasting Peripheral Holes

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ke Man ◽  
Xiaoli Liu ◽  
Zhifei Song
Keyword(s):  
Energy Distribution ◽  
Frequency Spectrum ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Energy Concentration ◽  
Vibration Velocity ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
The Stability ◽  
Smooth Blasting

Based on the blasting principle of the cutting seam cartridge, smooth blasting with the charge structures of the usual cartridge and cutting seam cartridge has been designed and implemented, respectively, for different peripheral holes in the same face. Meanwhile, the blasting vibration has been monitored. Through the analysis of the frequency spectrum of blasting vibration signals, it is found that the maximum blasting vibration velocity of the cutting seam cartridge charge is lower than that of the usual cartridge charge, from 0.21 m/s to 0.12 m/s. Moreover, the blasting energy distribution is more balanced. Especially in the low-frequency part, the blasting energy is less, and there is a transferring trend to the high-frequency part, which shows that the cutting seam cartridge charge has a better optimization effect. Furthermore, using wavelet packet analysis, the cutting seam cartridge charge could effectively reduce the energy concentration in the low-frequency part. The energy distribution is much more dispersed, and the disturbance to the structure could be less, which is conducive to the stability of the structure. According to the blasting effect, the overbreak and underexcavation quantity at the cutting seam cartridge charge is better than that at the usual cartridge charge.

Radiated Noise Source Localization of Hydraulic Excavator Based on Wavelet Packet Analysis

2013 ◽  
Vol 415 ◽  
pp. 409-413
Author(s):  
Qing Qing Zhang ◽  
Yi Qi Zhou ◽  
Liang Liang Fan
Keyword(s):  
Energy Distribution ◽  
Source Localization ◽  
Noise Source ◽  
Wavelet Packet ◽  
Hydraulic Excavator ◽  
Wavelet Packet Analysis ◽  
Main Band ◽  
Radiated Noise ◽  
Hydraulic Excavators ◽  
Exhaust Noise

Collect a hydraulic excavators radiated noise ten meters away under set conditions, and also the relevant noises near the excavator. Analyze noise signals with wavelet packet to get the main band of energy distribution. Then calculate the two signals correlation coefficient, which identifies the muffler exhaust noise and inlet noise as the main source for right rear radiated noise.

scholarly journals Feature extraction for fault diagnosis based on wavelet packet decomposition: An application on linear rolling guide

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879636 ◽  
Author(s):  
Hutian Feng ◽  
Rong Chen ◽  
Yiwei Wang
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Wavelet Packet ◽  
Numerical Control ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Good Ability ◽  
Rolling Guide ◽  
Lubrication Conditions

Linear rolling guide is increasingly being used as the transmission system in computer numerical control machine tools due to its high stiffness, low friction, good ability of precision retaining, and so on. The lubrication of rolling linear guide affects significantly its performance and hence monitoring the lubrication condition during its operation is of great importance. In this article, the relation between different lubrication conditions of linear rolling guide and their corresponding vibration signals is studied. Three lubrication conditions labeled as “Poor,”“Medium,” and “Good” are simulated to represent the actual working conditions. A data acquisition system is set up to acquire the vibration signals corresponding to different conditions. The wavelet packet decomposition is employed to perform time–frequency analysis of the raw signal, after which the energy distribution of the decomposed signals is extracted as the feature. Two linear rolling guides manufactured by different companies are used in the experiments. The results demonstrate that the relation between the energy distribution extracted from vibration signals and lubrication conditions follows a certain rule. A typical feedforward backpropagation neural network is used as the classifier to verify the effectiveness of energy distribution. The average classification accuracy of the network with energy distribution as input is more than 95%. The results show that the lubrication conditions can be characterized by “energy” hidden in the vibration signals and the energy distribution is an appropriate feature that can be used for fault diagnosis of linear rolling guide.

Real-time localization of micro-damage in concrete beams using DIC technology and wavelet packet analysis

2021 ◽  
pp. 104198
Author(s):  
Kaiyang Zhou ◽  
Dong Lei ◽  
Jintao He ◽  
Pei Zhang ◽  
Pengxiang Bai ◽  
...  
Keyword(s):  
Real Time ◽  
Concrete Beams ◽  
Wavelet Packet ◽  
Wavelet Packet Analysis ◽  
Damage In Concrete

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

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