Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck Crane
Vibration signal of plunger pump in truck crane is a typical nonlinear, nonstationary signal, and the multifractal features are a powerful tool for depicting the geometry features of such signals. The wavelet-leaders based multifractal features extraction method is an attractive tool, which has soli...
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2013-01-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1155/2013/676404 |
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doaj-def4b171e48645dc91aacee2b9ef2e742020-11-25T02:58:08ZengSAGE PublishingAdvances in Mechanical Engineering1687-81322013-01-01510.1155/2013/67640410.1155_2013/676404Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck CraneWenliao DuChunyan YangAnsheng LiLiangwen WangVibration signal of plunger pump in truck crane is a typical nonlinear, nonstationary signal, and the multifractal features are a powerful tool for depicting the geometry features of such signals. The wavelet-leaders based multifractal features extraction method is an attractive tool, which has solid theoretical mathematical support and a simplified calculation procedure. The wavelet-leaders based multifractal features extraction method is compared for the original signal and the denoised signal, and the statistical performance of the obtained features is also introduced based on block bootstrap technology. The effectiveness of the wavelet leaders based method is first verified for a traditional multifractal signal. Then, the application of the proposed method to the pump vibration signals of three working conditions of truck crane: lifting, rotating, and luffing is discussed. The result shows that the geometry features of vibration signals can be obtained with wavelet leaders multifractal analysis method, and the denoising process improves the multifractal features.https://doi.org/10.1155/2013/676404 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenliao Du Chunyan Yang Ansheng Li Liangwen Wang |
spellingShingle |
Wenliao Du Chunyan Yang Ansheng Li Liangwen Wang Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck Crane Advances in Mechanical Engineering |
author_facet |
Wenliao Du Chunyan Yang Ansheng Li Liangwen Wang |
author_sort |
Wenliao Du |
title |
Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck Crane |
title_short |
Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck Crane |
title_full |
Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck Crane |
title_fullStr |
Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck Crane |
title_full_unstemmed |
Wavelet Leaders Based Vibration Signals Multifractal Features of Plunger Pump in Truck Crane |
title_sort |
wavelet leaders based vibration signals multifractal features of plunger pump in truck crane |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8132 |
publishDate |
2013-01-01 |
description |
Vibration signal of plunger pump in truck crane is a typical nonlinear, nonstationary signal, and the multifractal features are a powerful tool for depicting the geometry features of such signals. The wavelet-leaders based multifractal features extraction method is an attractive tool, which has solid theoretical mathematical support and a simplified calculation procedure. The wavelet-leaders based multifractal features extraction method is compared for the original signal and the denoised signal, and the statistical performance of the obtained features is also introduced based on block bootstrap technology. The effectiveness of the wavelet leaders based method is first verified for a traditional multifractal signal. Then, the application of the proposed method to the pump vibration signals of three working conditions of truck crane: lifting, rotating, and luffing is discussed. The result shows that the geometry features of vibration signals can be obtained with wavelet leaders multifractal analysis method, and the denoising process improves the multifractal features. |
url |
https://doi.org/10.1155/2013/676404 |
work_keys_str_mv |
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