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.

Wavelets base Automatic Extraction Technology for Vibration Signal Characteristics of Hybrid Electric Vehicle Engine

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Dongfang Song ◽  
Guanfei Yin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Domain Analysis ◽  
Extraction Technology ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Signal Characteristics

Traditional automatic characteristic extraction technology of engine vibration signals for hybrid electric vehicles (HEV) only focuses on the analysis of engine vibration signals in time domain and frequency domain. Single time domain analysis or single frequency domain analysis cannot accurately analyse the vibration signals, while both time domain analysis and frequency domain analysis have cross-analysis. As a result, the analysis results are repetitive and conflicting, which makes it difficult to extract the characteristics of engine vibration signals. The final extraction accuracy is not high and the extraction efficiency is low. For this reason, an automatic characteristic extraction technology of HEV engine vibration signal based on wavelet packet energy analysis is proposed. Firstly, the mechanical vibration of engine is converted into corresponding voltage and current signals by various sensors and then converted into digital signals by A/D (analog/digital) conditioner. The data of vibration signals are often mixed with various noises, which have a great impact on the final analysis of vibration signals. Data interception and pre-filtering are adopted. Wave, zero-mean, elimination of trend term and elimination of staggered points are used to pre-treat the vibration signals with mixed noise. Short-Time Fourier Transform (STFT) algorithm is introduced to analyse the pre-processed engine vibration signals and the fundamental properties of the non-stationary vibration signals in actual operation of the engine are obtained. The energy distribution of the analysed engine vibration signal is calculated by the wavelet packet energy analysis method. The calculated parameters of the energy distribution of the wavelet packet are taken as the characteristic parameters of the vibration signal. The vibration signal characteristics of the engine are automatically extracted. The experiment is carried out in the form of comparison with the traditional method. The experimental results show that the time-frequency joint analysis applied in the proposed technology can accurately analyse the essential characteristics of the engine vibration signal of HEV. The wavelet packet energy analysis method can ensure the extraction accuracy of the engine vibration signal characteristics.

FEATURE SELECTION FOR OPTIMIZATION OF WAVELET PACKET DECOMPOSITION IN RELIABILITY ANALYSIS OF SYSTEMS

2013 ◽  
Vol 22 (05) ◽  
pp. 1360011 ◽  
Author(s):  
RANDALL WALD ◽  
TAGHI M. KHOSHGOFTAAR ◽  
JOHN C. SLOAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Machine Learning Algorithms ◽  
Wavelet Packet Decomposition ◽  
Time Frequency ◽  
Speed Up ◽  
Frequency Domain Techniques ◽  
Feature Selection Techniques

One of the most important types of signal found in the area of machine condition monitoring/prognostic health monitoring (MCM/PHM) is the vibration signal, a type of waveform. Many time-frequency domain techniques have been proposed to interpret such signals, including wavelet packet decomposition (WPD). Previous work has shown how to extend the WPD algorithm to operate on streaming signals, but the number of output variables becomes exponential in the number of levels of decomposition, hindering data mining in limited-memory environments. Feature selection techniques, well understood in other areas of data mining, can be used to greatly reduce the number of output variables and speed up the machine learning algorithms. This paper presents a case study comparing two versions of WPD both with and without feature selection, demonstrating that removing most of the features produced by the WPD does not impair its performance within the context of MCM/PHM.

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.

RESONANCE-BASED VIBRATION SIGNAL DENOISING USING WAVELET PACKET DECOMPOSITION

2007 ◽  
Vol 07 (03) ◽  
pp. L257-L262
Author(s):  
M. SHAGHAGHI ◽  
M. H. KAHAEI ◽  
J. POSHTAN
Keyword(s):  
Wavelet Packet ◽  
Computational Cost ◽  
Vibration Signal ◽  
Wavelet Packets ◽  
Signal Denoising ◽  
Wavelet Packet Decomposition ◽  
Noisy Environments ◽  
Vibration Signals ◽  
On Line

This paper presents a new on-line technique for denoising impulsive vibration signals in noisy environments using Wavelet Packets. The proposed algorithm is based on localizing the frequency subbands of the resonances embedded in impulsive vibration signals. The performance of the algorithm is evaluated in denoising real vibration signals measured from faulty bearings. The results compared to the theoretical values and those obtained by the HFD algorithm show the effectiveness of the proposed algorithm while the computational cost reduces to half.

scholarly journals Time–Frequency Domain Characteristics of Acoustic Emission Signals and Critical Fracture Precursor Signals in the Deep Granite Deformation Process

2021 ◽  
Vol 11 (17) ◽  
pp. 8236
Author(s):  
Le Zhang ◽  
Hongguang Ji ◽  
Liyuan Liu ◽  
Jiwei Zhao
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
High Frequency ◽  
Wavelet Packet ◽  
Confining Pressure ◽  
Wavelet Packet Decomposition ◽  
Evolution Law ◽  
Time Frequency ◽  
Characteristic Signal ◽  
Failure Precursor

To study the crack evolution law and failure precursory characteristics of deep granite rocks in the process of deformation and failure under high confining pressure, granite samples obtained from a depth of 1150 m are tested using a TAW-2000 triaxial hydraulic servo testing machine and a PCI-II acoustic emission monitoring system. Based on the stress–strain curve and IET function, the loading process of the sample is divided into five stages: crack closure, linear elastic deformation, microcrack generation and development, macroscopic fracture generation and energy surge, and post-peak failure. The evolution trend and fracture evolution law of the acoustic emission signal event interval function in different stages are analyzed. In particular, the signals with an amplitude greater than 85 dB, a peak frequency greater than 350 kHz, and a frequency centroid greater than 275 kHz are defined as the failure precursor signals before the rock reaches the peak stress. The defined precursor signal conditions agree well with the experimental results. The time–frequency analysis and wavelet packet decomposition of the precursor signal are performed on the extracted characteristic signal of the failure precursor. The results show that the time-domain signal is in the form of a continuous waveform, and the frequency-domain waveform has multi-peak coexistence that is mainly concentrated in the high-frequency region. The energy distribution obtained by the wavelet packet decomposition of the characteristic signal is verified with the frequency-domain waveform. The energy distribution of the signal is mainly concentrated in the 343.75–375 kHz frequency band, followed by the 281.25–312.5 kHz frequency band. The energy proportion of the high-frequency signal increases with the confining pressure.

Wavelet decomposition and nonlinear prediction of nonstationary vibration signals

2020 ◽  
Vol 51 (3) ◽  
pp. 52-59 ◽  
Author(s):  
Xiao-bin Fan ◽  
Bin Zhao ◽  
Bing-xu Fan
Keyword(s):  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Prediction Method ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Maximum Lyapunov Exponent ◽  
Nonlinear Prediction ◽  
Local Method ◽  
Time Frequency ◽  
Nonstationary Vibration

In order to overcome the shortcomings (such as the time–frequency localization and the nonstationary signal analysis ability) of the Fourier transform, time–frequency analysis has been carried out by wavelet packet decomposition and reconstruction according to the actual nonstationary vibration signal from a large equipment located in a large Steel Corporation in this article. The effect of wavelet decomposition on signal denoising and the selection of high-frequency weight coefficients for each layer on signal denoising were analyzed. The nonlinear prediction of the chaotic time series was made by global method, local method, weighted first-order local method, and maximum Lyapunov exponent prediction method correspondingly. It was found the multi-step prediction method is better than other prediction methods.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

Study on the Method of Vibration Signal De-Noising Using Wavelet Packet Based on QPSO

2014 ◽  
Vol 599-601 ◽  
pp. 1738-1744
Author(s):  
Kai Zhao ◽  
Ben Wei Li ◽  
Jing Chen
Keyword(s):  
Experimental Verification ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Threshold Function ◽  
Vibration Signals ◽  
Selection Of

Although many wavelet de-noising methods have been studied and proposed, the parameters of them are obtained by experience mostly, which makes the de-noising effect instable. To solve the issues, the solutions, such as the selection of wavelet function and threshold function, the calculation of decomposition levels, the optimal wavelet packet basis and the thresholds obtained based on QPSO, have been studied in this paper. Every parameter is obtained by calculation. This method is applied to the de-noising experiment of sine and vibration signals. Through the experimental verification, the effect of this de-noising method is obvious.

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