Gear Fault Diagnosis Based on Optimal Morlet Wavelet Filter and Autocorrelation Enhancement

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F15%3A00240259" target="_blank" >RIV/68407700:21220/15:00240259 - isvavai.cz</a>

Výsledek na webu

<a href="http://papers.sae.org/2015-01-0212" target="_blank" >http://papers.sae.org/2015-01-0212</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4271/2015-01-0212" target="_blank" >10.4271/2015-01-0212</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Gear Fault Diagnosis Based on Optimal Morlet Wavelet Filter and Autocorrelation Enhancement

Popis výsledku v původním jazyce

An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars. The results show the ability of the proposed method to enhance the capability of condition monitoring systems by identifying gear faults at early stages.

Název v anglickém jazyce

Gear Fault Diagnosis Based on Optimal Morlet Wavelet Filter and Autocorrelation Enhancement

Popis výsledku anglicky

An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars. The results show the ability of the proposed method to enhance the capability of condition monitoring systems by identifying gear faults at early stages.

Druh

D - Stať ve sborníku

CEP obor

JT - Pohon, motory a paliva

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.

Název statě ve sborníku

SAE Technical Paper 2015-01-1718

ISBN

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ISSN

0148-7191

e-ISSN

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Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

SAE International

Místo vydání

Warrendale, PA 15096

Místo konání akce

Detroit

Datum konání akce

21. 4. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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