Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it...

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Main Authors: Grzegorz Żak, Agnieszka Wyłomańska, Radosław Zimroz
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2017/3698370
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spelling doaj-b81c709a3c95482a84e90d18a69274d62020-11-24T22:31:25ZengHindawi LimitedShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/36983703698370Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable DistributionGrzegorz Żak0Agnieszka Wyłomańska1Radosław Zimroz2Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław University of Science and Technology, Na Grobli 15, 50-421 Wrocław, PolandKGHM Cuprum Research & Development Center, Ul. Sikorskiego 2-8, 53-659 Wrocław, PolandDiagnostics and Vibro-Acoustics Science Laboratory, Wrocław University of Science and Technology, Na Grobli 15, 50-421 Wrocław, PolandThe authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.http://dx.doi.org/10.1155/2017/3698370
collection DOAJ
language English
format Article
sources DOAJ
author Grzegorz Żak
Agnieszka Wyłomańska
Radosław Zimroz
spellingShingle Grzegorz Żak
Agnieszka Wyłomańska
Radosław Zimroz
Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution
Shock and Vibration
author_facet Grzegorz Żak
Agnieszka Wyłomańska
Radosław Zimroz
author_sort Grzegorz Żak
title Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution
title_short Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution
title_full Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution
title_fullStr Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution
title_full_unstemmed Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution
title_sort data-driven iterative vibration signal enhancement strategy using alpha stable distribution
publisher Hindawi Limited
series Shock and Vibration
issn 1070-9622
1875-9203
publishDate 2017-01-01
description The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.
url http://dx.doi.org/10.1155/2017/3698370
work_keys_str_mv AT grzegorzzak datadriveniterativevibrationsignalenhancementstrategyusingalphastabledistribution
AT agnieszkawyłomanska datadriveniterativevibrationsignalenhancementstrategyusingalphastabledistribution
AT radosławzimroz datadriveniterativevibrationsignalenhancementstrategyusingalphastabledistribution
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