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

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.

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.

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.

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.

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.

scholarly journals Wavelet Packet Transform-Assisted Least Squares Support Vector Machine for Gear Wear Degree Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hongmin Wang ◽  
Liang Chan
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Speed ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Least Square ◽  
Signal Spectrum ◽  
Support Vector ◽  
Particle Swarm Algorithm

Wear degree detection of gears is an effective way to prevent faults. However, due to the interference of high-speed meshing vibration and environmental noise, the weak vibration signal generated by the gear is easily covered by the noise, which makes it difficult to detect the degree of wear. To address this issue, this paper proposes a novel gear wear degree diagnosis method based on local weighted scatter smoothing method (LOWESS), wavelet packet transform (WPT), and least square support vector machine (APSO-LSSVM) optimized by adaptive particle swarm algorithm. According to the low signal-to-noise ratio characteristic of gear vibration signal, LOWESS is first used to preprocess the signal spectrum. Then, the characteristic parameters used to characterize gear wear are extracted from different decomposition depths by WPT and, finally, combined with APSO-SVM to diagnose the degree of gear wear. Compared with the basic least squares support vector machine, the improved method has better performance in sample classification. The experimental results show that the method in this paper can effectively reduce the diagnosis error caused by background noise, and the diagnosis accuracy reaches 98.33%, which can provide a solution for the health status monitoring of gears.

Study of Vibration Characteristics of the Reciprocating Compressor on the Offshore Platform Based on Harmonic Wavelet Packet Transform

2014 ◽  
Vol 875-877 ◽  
pp. 2107-2112
Author(s):  
Ye Hua Huang ◽  
Guo Hua Dai
Keyword(s):  
Wavelet Packet ◽  
Low Frequency ◽  
Vibration Signal ◽  
Axial Direction ◽  
Wavelet Packet Transform ◽  
Vibration Characteristics ◽  
Offshore Platform ◽  
Vibration Energy ◽  
Reciprocating Compressor ◽  
Harmonic Wavelet

In order to determine the vibration characteristics of the reciprocating compressor on the offshore platform, the vibration signal from the reciprocating compressor on the offshore platform was investigated by applying the harmonic wavelet packet transform. The energy variation of vibration signal under the different frequencies was discussed. It was shown that the vibration energy of the reciprocating compressor in the horizontal and vertical directions is mainly concentrated in the low frequency of 25Hz and 50Hz, and the vibration energy of other frequency is small and smooth. In the axial direction, the vibration energy of the reciprocating compressor extends to the medium-high frequency, and the large energy appears in the 225 Hz. Therefore, the harmonic wavelet packet transform can be used to research the vibration characteristics of the reciprocating compressor on the offshore platform.

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