scholarly journals Chatter Detection in Milling Process Based on the Combination of Wavelet Packet Transform and PSO-SVM

2021 ◽  
Author(s):  
Qingzhen Zheng ◽  
Guangsheng Chen ◽  
Anling Jiao
Keyword(s):  
Wavelet Packet ◽  
Dimensional Accuracy ◽  
Vibration Signal ◽  
Pso Algorithm ◽  
Milling Process ◽  
Wavelet Packet Transform ◽  
Wavelet Packets ◽  
Limiting Factor ◽  
Novel Method ◽  
Traditional Approaches

Abstract Chatter has become the mainly limiting factor in the development of rapid and stable machining of machine tools, which seriously impacts on surface quality and dimensional accuracy of the finished workpiece. In this paper, a novel method of chatter recognition was proposed based on the combination of wavelet packet transform (WPT) and PSO-SVM in milling. The collected vibration signal was pre-processed by wavelet packet transform (WPT), and the wavelet packets with rich chatter information were selected and reconstructed. The selected wavelet packets can reduce the redundant noise and useless information. a combination of 10 time-domain and 4 frequency-domain feature parameters were obtained through calculating the reconstructed vibration signals. Compared to three methods of k-fold cross validation (k-CV), genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the input parameters of SVM, the experiment results were shown that the PSO algorithm has is characterized by high accuracy. The proposed approach can recognize the stable, chatter and transition states more accurately than the other traditional approaches.

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

2009 ◽  
Author(s):  
Young-Sun Hong ◽  
Gil-Yong Lee ◽  
Young-Man Cho ◽  
Sung-Hoon Ahn ◽  
Chul-Ki Song
Keyword(s):  
Wavelet Packet ◽  
Position Effect ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Wavelet Packet Transform ◽  
Bevel Gear ◽  
Diagnostic Methods ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

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.

Fan Fault Feature Extraction Based on Wavelet Packet Transform

2014 ◽  
Vol 668-669 ◽  
pp. 999-1002
Author(s):  
Xin Li ◽  
Pan Feng Guo
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Research Topic ◽  
Signal Characteristic ◽  
Manufacturing Enterprises ◽  
Important Position ◽  
Fault Feature Extraction

Fan occupies the important position in many industry, it give rise to that fault diagnosis become the new hot research topic, also is the urgent demand of many manufacturing enterprises. This paper based on the theory of wavelet packet transform, selecting wavelet packet transform and energy spectrum to wavelet de-noising and fault feature extraction the fan vibration signal. And use the MATLAB get the fan vibration signal characteristic vector, lay the foundation for the fan fault diagnosis.

Main frequency band of blast vibration signal based on wavelet packet transform

2019 ◽  
Vol 74 ◽  
pp. 569-585 ◽  
Author(s):  
Guan Chen ◽  
Qi-Yue Li ◽  
Dian-Qing Li ◽  
Zheng-Yu Wu ◽  
Yong Liu
Keyword(s):  
Frequency Band ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Blast Vibration ◽  
Main Frequency

Research on Blasting Vibration Signal Denoising Based on Lifting Scheme

2015 ◽  
Vol 713-715 ◽  
pp. 647-650 ◽  
Author(s):  
Quan Min Xie ◽  
Huai Zhi Zhang ◽  
Ying Gao ◽  
Hong An Cao ◽  
Sheng Qiang Guo ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Mechanical Vibration ◽  
Lifting Scheme ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Signal Denoising ◽  
Blasting Vibration ◽  
Lifting Wavelet

Considering lifting scheme and traditional wavelet packet transform principle, The optimal lifting wavelet packet threshold denoising algorithm was introduced. Experimental blasting vibration signal was decomposed by optimal lifting wavelet packet, and noise components in blasting vibration measured signals were filtered successfully. Research shows that, lifting wavelet package transform can effectively remove noise components, and it laid an important foundation for lifting algorithm will be introduced into the analysis field of blasting vibration effects and other mechanical vibration signal.

Particle Swarm Optimization and Adaptive Wavelet Theory for Vibration Signal Application in Fault Diagnosis of Gearbox System

2013 ◽  
Vol 791-793 ◽  
pp. 958-961
Author(s):  
Han Xin Chen ◽  
Yan Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Pso Algorithm ◽  
Economic Losses ◽  
Early Age ◽  
Wavelet Theory ◽  
Swarm Optimization ◽  
Adaptive Wavelet ◽  
Novel Method

Gearbox system is widely used in mechanical industry,but serious failure is always occurred in the gearbox system. So it is very necessary to diagnose the fault of gearbox in the early-age avoiding economic losses. In this paper, a novel method for extracting the characteristic information from the vibration signal of gearbox system based on the particle swarm optimization (PSO) algorithm and adaptive wavelet theory is proposed.

Application of Wavelet Packet Transform for Detection of Ball Bearing Race Fault

2009 ◽  
Vol 626-627 ◽  
pp. 511-516
Author(s):  
Dong Yun Wang ◽  
Wen Zhi Zhang ◽  
Wei Ping Lu ◽  
J.W. Du
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Ball Bearing ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Rolling Ball ◽  
Bearing Race ◽  
Artificial Neural

In this study, a fault diagnosis system is proposed for rolling ball bearing race using wavelet packet transform(WPT) and artificial neural network(ANN)technique. Vibration signal from ball bearings having defects on inner race and outer race is considered and the extraction method of feature vector based on wavelet packet transform with frequency band energy is used. The vibration signal is decomposed into the individual frequency bands. The variations of the signal energy in these bands reflect the different fault locations. Further, the artificial neural network is proposed to develop the diagnostic rules of the data base in the present fault identification system. The experimental work is performed to evaluate the effect of fault diagnosis in a rolling ball bearing platform under different fault conditions. The experimental results indicate the effectiveness of the proposed method in fault bearing identification.

The Properties of Multidimensional Semi-Orthogonal Frame Wavelet Packets

2010 ◽  
Vol 439-440 ◽  
pp. 896-901
Author(s):  
Qing Jiang Chen ◽  
Yu Ying Wang
Keyword(s):  
Matrix Theory ◽  
Wavelet Packet ◽  
Wavelet Packets ◽  
Analysis Method ◽  
Time Frequency ◽  
Dilation Matrix ◽  
Novel Method ◽  
Popular Subject ◽  
Vector Valued ◽  
Orthogonal Frame

Wavelet analysis has become a popular subject in scientific research during the past twenty years. In this work, we introduce the notion of vector-valued multiresolution analysis and vector-valued multivariate wavelet packets associated with an integer-valued dilation matrix. A novel method for constructing multi-dimen- -sional vector-valued wavelet packet is presented. Their characteristics are researched by means of operator theory, time-frequency analysis method and matrix theory. Three orthogonality formulas concerning the wavelet packets are established. Orthogonality decomposition relation formulas of the space are derived by constructing a series of subspaces of wavelet packets. Finally, one new orthonormal wavelet packet bases of are constructed from these wavelet packets.

scholarly journals The Experimental Study on Interaction of Vibration and Dynamic Force in Precision Milling Process

2021 ◽  
Author(s):  
Weicheng Guo ◽  
Miaoxian Guo ◽  
Yi Ye ◽  
Xiaohui Jiang ◽  
Chongjun Wu
Keyword(s):  
Machine Tool ◽  
Interaction Effect ◽  
Dynamic Load ◽  
Manufacturing Process ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Milling Process ◽  
Machining Process ◽  
Dynamic Force ◽  
Coupling Vibration

Abstract A good understanding of the dynamic characteristic in milling of aerospace aluminum, especially the coupling vibration caused by the interaction of the manufacturing process and the machine tool, helps promote the machining precision and surface quality of aerospace structural components. This paper is devoted to proposing the interaction theory of the vibration and dynamic force, which is verified in the milling of Al 7075-T651 by consideration both the machine tool load and machining process dynamic load. First, through detailed analysis of the interaction effect of vibration and the dynamic force, the dynamic milling process is simplified to theoretically model the dynamic interaction in the precision manufacturing process under non-chatter condition. Then, the dynamic process force, which is the key source of the interaction, is modeled and obtained based on wavelet packet transform preprocess; the Frequency Response Function (FRF) of machine tool is regarded as the interaction link between the dynamic force load and the vibration response; the machine tool non-cutting vibration is transformed as a special dynamic load superposed on the response. Finally, the interaction vibration is calculated applying interaction effect model, the predicated results obtained in interaction effect approach match well with the vibration signal directly obtained in the test.

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