Time Frequency Analysis of Blasting Vibration Superposing Signal under Different Millisecond Intervals

2012 ◽  
Vol 170-173 ◽  
pp. 3097-3101
Author(s):  
Wei Zhang ◽  
Shi Hai Chen
Keyword(s):  
Vibration Reduction ◽  
Maximum Amplitude ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Single Stage ◽  
Blasting Vibration ◽  
Reduction Theory ◽  
Time Frequency ◽  
Vibration Effect ◽  
Signal Time

Based on the measured single-stage blasting vibration signal, time-frequency characteristics of two single-stage superposing signals were analyzed under the condition of different millisecond intervals ranging from 1ms to 350ms, also, variation laws of dominant frequency, amplitude and energy of the blasting vibration superposing signal with the delay time and the determination method of rational millisecond interval of similar engineering were put forward. Then, changing laws of the millisecond interval with the interference effect was obtained. It is found that, millisecond delay blasting does not follow the disturbance vibration reduction theory strictly that the vibration effect is weakened when interval is (2n-1)T/2 and strengthened when interval is nT, and the more similar the vibration characteristics of single-stage signals are, the larger the maximum amplitude declining rate of the obtained superposing signal is.

Blasting Vibration Signals Time-Frequency Analysis Based on S-Transform

2012 ◽  
Vol 588-589 ◽  
pp. 2013-2017
Author(s):  
Dong Tao Li ◽  
Jing Long Yan ◽  
Le Zhang
Keyword(s):  
Frequency Analysis ◽  
Vibration Signal ◽  
Transformation Method ◽  
Blasting Vibration ◽  
Time Duration ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Vibration Wave

Introduced the theory of S-transform, designed simulation experiment and the frequency components distribution versus time was, verified that the S-transformation method is suitable for blasting vibration signal time-frequency analyzed. Applied it to the time-frequency analysis of measured blasting vibration signals at situ, the results show that S-transform has excellent time-frequency representation ability and higher resolution, reveals the detail information of blasting vibration wave changing with time and frequency, and provides a new way for blasting vibration research. Determined the desired delay intervals through comparing the energy of signal and the time duration of the waveform at characteristic frequency between two-hole blasting vibration signals with different delay intervals.

scholarly journals Time-Frequency Characteristics Analysis on Vibration Signals of a Three-Supported Rotor System with Misalignment

2011 ◽  
Vol 2-3 ◽  
pp. 717-721 ◽  
Author(s):  
Xiao Xuan Qi ◽  
Mei Ling Wang ◽  
Li Jing Lin ◽  
Jian Wei Ji ◽  
Qing Kai Han
Keyword(s):  
Wavelet Packet ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Characteristics Analysis ◽  
Novel Method ◽  
Misalignment Fault

In light of the complex and non-stationary characteristics of misalignment vibration signal, this paper proposed a novel method to analyze in time-frequency domain under different working conditions. Firstly, decompose raw misalignment signal into different frequency bands by wavelet packet (WP) and reconstruct it in accordance with the band energy to remove noises. Secondly, employ empirical mode decomposition (EMD) to the reconstructed signal to obtain a certain number of stationary intrinsic mode functions (IMF). Finally, apply further spectrum analysis on the interested IMFs. In this way, weak signal is caught and dominant frequency is picked up for the diagnosis of misalignment fault. Experimental results show that the proposed method is able to detect misalignment fault characteristic frequency effectively.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

Refinement Analysis for Time-Frequency Characteristic of Blasting Seismic Exploration Signals

2014 ◽  
Vol 926-930 ◽  
pp. 3541-3544
Author(s):  
Ming Shou Zhong ◽  
Quan Min Xie ◽  
Tao Guo ◽  
Xing Bo Xie ◽  
Hao Quan Liu ◽  
...  
Keyword(s):  
Wavelet Transformation ◽  
Seismic Exploration ◽  
Blasting Vibration ◽  
Seismic Signals ◽  
Analysis Method ◽  
Effect Analysis ◽  
Time Frequency ◽  
Vibration Effect ◽  
New Research ◽  
Frequency Features

Accurate extraction of time-frequency features for blasting vibration signals has great significance for blasting seismic exploration, so time-frequency analysis method for blasting seismic signals was researched based on frequency slice wavelet transformation technology, and separation and extraction of time-frequency features were were successfully achieved. Frequency slice wavelet transformation can be introduced into blasting vibration effect analysis fields, it can provide a new research idea for refinement analysis of time-frequency characteristics, and it also has great significance for improving the effect of blasting seismic exploration in China.

Analysis about Effect of Hollow on Time-Frequency Characteristic of Surface Vibration Signal

2014 ◽  
Vol 533 ◽  
pp. 181-186
Author(s):  
Ming Sheng Zhao ◽  
En An Chi ◽  
Qiang Kang ◽  
Tie Jun Tao
Keyword(s):  
Vibration Signal ◽  
Dominant Frequency ◽  
Frequency Characteristics ◽  
Distribution Method ◽  
Vibration Signals ◽  
Band Energy ◽  
Surface Vibration ◽  
Time Frequency ◽  
Working Face ◽  
Blasting Excavation

In blasting excavation of shallow tunnel, the surface vibration of excavated tunnel can be amplified due to effect of hollow. This effect is an important factor for safety of surface buildings. Based on the measured data of one tunnel excavation project, combining wavelet analysis and AOK time-frequency distribution method, the surface vibration signals in front and rear position of working face are processed into different frequency bands. Taking PPV, dominant frequency, d7 (7.8125-15.625 Hz) band energy ratio and d7 (7.8125-15.625 Hz) band energy duration as indexes, the effect of hollow on time-frequency characteristics of surface vibration signal is studied in this article. The results show that, affected by the hollow in excavated region, the PPV and dominant frequency increase, and the d7 (7.8125-15.625 Hz) band energy shows fluctuant ratio of total energy and an increase of band energy duration. The results show that the hollow influence on the frequency characteristics of the surface vibration signals comprehensively, and also provide an analytical basis for anti-vibration and vibration reduction study from the angle of energy.

scholarly journals Analysis and Research on Blasting Network Delay of Deep-Buried Diversion Tunnel Crossing Fault Zone Based on EP-CEEMDAN-INHT

2021 ◽  
Author(s):  
Jing Wu ◽  
Li Wu ◽  
Miao Sun ◽  
Ya-ni Lu ◽  
Yan-hua Han
Keyword(s):  
Fault Zone ◽  
Vibration Signal ◽  
Practical Significance ◽  
Time Interval ◽  
Blasting Vibration ◽  
Diversion Tunnel ◽  
Safety Control ◽  
Time Frequency ◽  
Damping Effect ◽  
The Hilbert Transform

Abstract The Empirical Mode Decomposition (EMD) of blasting seismic wave monitoring signal with noise can get IMFs with serious mode confusion and divergent end points. The Hilbert transform is constrained by the Bedrosian theorem, when it dealing with such IMFs will get negative instantaneous frequency, which leads to serious error in the identification of non-electric millisecond detonator initiation delay. EP-CEEMDAN-INHT is proposed and applied to the delay analysis of blasting network of deep buried diversion tunnel crossing fault zone. Comparing EP-CEEMDAN-INHT with EMD-HHT, it is found that EP-CEEMDAN-INHT can clearly display the time-frequency information contained in the measured blasting vibration signal, and EMD mode confusion and endpoint effect are well suppressed. The actual millisecond time interval obtained by EP-CEEMDAN-INHT can judge whether the detonator is in normal service. At the same time, the blasting millisecond interval with the best damping effect is 54.51ms to 59.75ms, which can realize the optimization of blasting network and has important practical significance for blasting safety control.

Blasting Vibration Signal Analysis Based on Hilbert-Huang Transform

2011 ◽  
Vol 474-476 ◽  
pp. 2279-2285 ◽  
Author(s):  
Dong Li ◽  
Fang Xiang ◽  
Hao Quan Liu ◽  
Tao Guo ◽  
Guang Hua Wu
Keyword(s):  
Vibration Signal ◽  
Blasting Vibration ◽  
Matlab Simulation ◽  
Base Function ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Signal Change ◽  
Vibration Signal Analysis ◽  
Non Stationary Signal

This paper introduces the empirical mode decomposition and Hilbert transform principle. The validity and superiority of Hilbert—Huang transform is proved by MATLAB simulation experiment on computer. Finally, HHT method is used to analyze the collected blasting vibration signal as an example. Research shows that EMD method can process this kind of non-stationary signal such as blasting vibration effectively. Each IMF component decomposed by EMD has clear physical meaning. IMF is determined by signal itself. It has no base function and is adaptive. It can extract main characteristics of signal change and is suitable for analysis of blasting vibration signal which has the features of fast mutation and attenuation. The distribution of time-frequency-energy can be quantitatively described by HHT.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Wei ◽  
Xuwen Jing ◽  
Bingqiang Li ◽  
Chao Kang ◽  
Zhenhuan Dou ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Rotating Machinery ◽  
Second Order ◽  
Variable Speed ◽  
Short Time Fourier Transform ◽  
Stationary Characteristic ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Effective Technology ◽  
Short Time

AbstractIn recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.

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