scholarly journals Research on AE Source Location of Linear and Plane Rock Mass

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Haiyan Wang ◽  
Ji Ma ◽  
Gongda Wang ◽  
Han Gao ◽  
Guangyong Cui ◽  
...  
Keyword(s):  
Rock Mass ◽  
Wave Velocity ◽  
Source Localization ◽  
Wavelet Packet ◽  
Sensor Arrays ◽  
Positioning Error ◽  
Signal Delay ◽  
Wavelet Packet Analysis ◽  
Prediction And Control ◽  
Dynamic Disaster

The occurrence of rockburst dynamic disaster is a process from the microdamage to macroinstability of coal and rock mass, which is accompanied by the acoustic emission (AE) phenomenon. The application of AE technology can reliably help to judge and predict the damage evolution of coal and rock mass, as the most basic problem in the study of AE is the location of the AE source. In this work, the AE source localization experiments of rod-shaped rocks and plate-shaped rocks were carried out. The influence of calibration wave velocity of linear and plane positioning on the location of the AE source was studied. The feasibility analysis of the AE source localization of a plate-shaped rock with different sensor arrays was conducted. The result of the plane location was optimized by wavelet packet analysis combined with cross correlations. The results show that the homogeneity of marble members in this work is suitable, and the positioning error is least affected by wave velocity. In the positioning of the plane AE source, it is suitable to choose a diamond sensor array. The positioning source should be located near the center of the array network. The positioning effect of the rod-shaped rock is generally better than that of the plate-shaped rock. In the actual source positioning work, it should be simplified as much as possible as a linear positioning problem. A more accurate AE signal delay could be obtained using wavelet packet analysis combined with cross-correlation technology, which can greatly reduce the positioning error caused by the accuracy of time difference. The purpose of this work is to provide a basis for determining a more accurate location of the fracture source of rock materials, which is of great significance and application value on the prediction and control of rockburst dynamic disaster.

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.

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.

Structural Damage Identification Using Wavelet Packet Analysis and Neural Network

2006 ◽  
Vol 324-325 ◽  
pp. 205-208
Author(s):  
Qing Guo Fei ◽  
Ai Qun Li ◽  
Chang Qing Miao ◽  
Zhi Jun Li
Keyword(s):  
Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Wavelet Packet ◽  
Uniform Design ◽  
Network Models ◽  
Sampling Technique ◽  
Wavelet Packet Analysis ◽  
Neural Network Models ◽  
Network Training

This paper describes a study on damage identification using wavelet packet analysis and neural networks. The identification procedure could be divided into three steps. First, structure responses are decomposed into wavelet packet components. Then, the component energies are used to define damage feature and to train neural network models. Finally, in combination with the feature of the damaged structure response, the trained models are employed to determine the occurrence, the location and the qualification of the damage. The emphasis of this study is put on multi-damage case. Relevant issues are studied in detail especially the selection of training samples for multi-damage identification oriented neural network training. A frame model is utilized in the simulation cases to study the sampling techniques and the multi-damage identification. Uniform design is determined to be the most suitable sampling technique through simulation results. Identifications of multi-damage cases of the frame including different levels of damage at various locations are investigated. The results show that damages are successfully identified in all cases.

Sign in / Sign up

Export Citation Format

Share Document