Features of energy distribution for blast vibration signals based on wavelet packet decomposition

2005 ◽  
Vol 12 (S1) ◽  
pp. 135-140 ◽  
Author(s):  
Tong-hua Ling ◽  
Xi-bing Li ◽  
Ta-gen Dai ◽  
Zhen-bin Peng
Keyword(s):  
Energy Distribution ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Blast Vibration ◽  
Vibration Signals

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.

Study on a Novel Bearing Fault Diagnosis Method from Frequency and Energy Perspective

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Xiumei Li ◽  
Yong Liu ◽  
Huiming Zhao ◽  
Wu Deng
Keyword(s):  
Wavelet Packet ◽  
Bottom Layer ◽  
Wavelet Packet Decomposition ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Organic Combination ◽  
Component Faults

AbstractEarly identification of faults in rolling element bearings is a challenging task; especially extracting transient characteristics from a noisy signal and identifying bearings fault become critical steps. In this paper, a novel method for real time fault detection in rolling element bearings is proposed to deal with non-stationary fault signals from frequency and energy perspective. Second-order blind identification (SOBI) and wavelet packet decomposition are organically integrated to diagnose the early bearing faults, the fault vibration signals are processed by SOBI algorithm, and feature information is extracted; meanwhile, fault vibration signals are decomposed by the wavelet packet, the energy of terminal nodes(at the bottom layer of wavelet packet decomposition) are analyzed because the energy of terminal nodes has different sensitive to different component faults. Therefore, the bearing faults can be diagnosed by organic combination of fault characteristic frequency analysis and energy of the terminal nodes, and the effectiveness, feasibility and robustness of the proposed method have been verified by experimental data.

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.

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.

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 Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Maohua Xiao ◽  
Wei Zhang ◽  
Kai Wen ◽  
Yue Zhu ◽  
Yilidaer Yiliyasi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Frequency Resolution ◽  
Particle Swarm Algorithm ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Training Time

AbstractIn the process of Wavelet Analysis, only the low-frequency signals are re-decomposed, and the high-frequency signals are no longer decomposed, resulting in a decrease in frequency resolution with increasing frequency. Therefore, in this paper, firstly, Wavelet Packet Decomposition is used for feature extraction of vibration signals, which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals, and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition. The features are visualized by the K-Means clustering method, and the results show that the extracted energy features can accurately distinguish the different states of the bearing. Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algorithm is proposed to identify the bearing faults. Compared with the Particle Swarm Algorithm, Beetle Algorithm can quickly find the error extreme value, which greatly reduces the training time of the model. At last, two experiments are conducted, which show that the accuracy of the model can reach more than 95%, and the model has a certain anti-interference ability.

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