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.

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 Experimental Investigation on Cavitating Flow Induced Vibration Characteristics of a Low Specific Speed Centrifugal Pump

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Bo Gao ◽  
Pengming Guo ◽  
Ning Zhang ◽  
Zhong Li ◽  
Minguan Yang
Keyword(s):  
Frequency Band ◽  
Low Frequency ◽  
Local Maximum ◽  
Cavitation Number ◽  
Cavitating Flow ◽  
Vibration Characteristics ◽  
Vibration Energy ◽  
Stable Operation ◽  
Centrifugal Pumps ◽  
Number Range

Cavitating flow developing in the blade channels is detrimental to the stable operation of centrifugal pumps, so it is essential to detect cavitation and avoid the unexpected results. The present paper concentrates on cavitation induced vibration characteristics, and special attention is laid on vibration energy in low frequency band, 10–500 Hz. The correlation between cavitating evolution and the corresponding vibration energy in 10–500 Hz frequency band is discussed through visualization analysis. Results show that the varying trend of vibration energy in low frequency band is unique compared with the high frequency band. With cavitation number decreasing, vibration energy reaches a local maximum at a cavitation number much larger than the 3% head drop point; after that it decreases. The varying trend is closely associated with the corresponding cavitation status. With cavitation number decreasing, cavitation could be divided into four stages. The decreasing of vibration energy, in particular cavitation number range, is caused by the partial compressible cavitation structure. From cavitation induced vibration characteristics, vibration energy rises much earlier than the usual 3% head drop criterion, and it indicates that cavitation could be detected in advance and effectively by means of cavitation induced vibration characteristics.

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.

scholarly journals Fast Texture Synthesis in Adaptive Wavelet Packet Trees

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Ying-Shen Juang ◽  
Hsi-Chin Hsin ◽  
Tze-Yun Sung ◽  
Carlo Cattani
Keyword(s):  
High Frequency ◽  
Texture Synthesis ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Computation Time ◽  
Wavelet Packet Transform ◽  
Synthesis Process ◽  
Adaptive Wavelet ◽  
Hierarchical Trees ◽  
Speed Up

Wavelet packet transform known as a substantial extension of wavelet transform has drawn a lot of attention to visual applications. In this paper, we advocate using adaptive wavelet packet transform for texture synthesis. The adaptive wavelet packet coefficients of an image are organized into hierarchical trees called adaptive wavelet packet trees, based on which an efficient algorithm has been proposed to speed up the synthesis process, from the low-frequency tree nodes representing the global characteristics of textures to the high-frequency tree nodes representing the local details. Experimental results show that the texture synthesis in the adaptive wavelet packet trees (TSIAWPT) algorithm is suitable for a variety of textures and is preferable in terms of computation time.

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.

A New Adaptive Image Denoising Method Based on Wavelet Packet Transform and Neighbor Dependency

2013 ◽  
Vol 433-435 ◽  
pp. 301-305
Author(s):  
Bin Wen Huang ◽  
Yuan Jiao
Keyword(s):  
High Frequency ◽  
Wavelet Packet ◽  
Estimation Method ◽  
Low Frequency ◽  
Adaptive Threshold ◽  
Difficult Problem ◽  
Wavelet Packet Transform ◽  
Denoising Method ◽  
Image Edges ◽  
Orthogonal Wavelet Transform

In image processing, removal of noise without blurring the image edges is a difficult problem. Aiming at orthogonal wavelet transform and traditional thresholds shortage, a new adaptive threshold image de-noising method which is based on wavelet packet transform and neighbor dependency is proposed. Low frequency part and high frequency part can be decomposed at the same time in wavelet packet transform and the information contained in wavelet coefficients is redundant. Using this kind of relativity in wavelet packet coefficients, we use a new variance neighbor estimation method and then neighbor dependency adaptive threshold is produced. From the experiment result, we see that compared with traditional methods, this method can not only effectively eliminate noise, but can also well keep original images information and the quality after image de-noising is very well.

scholarly journals Wavelet Transform for IoT – A Signal Processing Perspective

2021 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Energy Correlation ◽  
Reconstructed Signal ◽  
Generalized Framework ◽  
Frequency Components ◽  
Initial Results

<div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div>

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