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Research Papers: Gas Turbines: Structures and Dynamics

Application of Cyclo-Nonstationary Indicators for Bearing Monitoring Under Varying Operating Conditions

[+] Author and Article Information
Konstantinos Gryllias

Department of Mechanical Engineering,
Faculty of Engineering,
Flanders Make,
KU Leuven,
Celestijnenlaan 300, Box 2420,
Leuven 3001, Belgium
e-mail: konstantinos.gryllias@kuleuven.be

Simona Moschini

Department of Mechanical Engineering,
Faculty of Engineering,
Flanders Make,
KU Leuven,
Celestijnenlaan 300, Box 2420,
Leuven 3001, Belgium
e-mail: s.moschini@v2i.be

Jerome Antoni

LVA EA677,
INSA-Lyon,
University of Lyon,
Villeurbanne F-69621, France
e-mail: jerome.antoni@insa-lyon.fr

Contributed by the Structures and Dynamics Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 30, 2017; final manuscript received July 8, 2017; published online September 19, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 140(1), 012501 (Sep 19, 2017) (6 pages) Paper No: GTP-17-1243; doi: 10.1115/1.4037638 History: Received June 30, 2017; Revised July 08, 2017

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health-monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring, and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time–frequency domain, and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclo-stationary, which is a particular class of nonstationary stochastic processes. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclo-stationarity (ICNS) in order to cover the speed-varying conditions. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN.

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References

Figures

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Fig. 1

Kinematics of the gearbox

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Fig. 2

Instantaneous rotating frequency of the shaft L4

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Fig. 5

Envelop order spectrum of the filtered signal

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Fig. 6

Indicators of cyclo-nonstationarity of second order on L5

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Fig. 7

Indicators of cyclo-nonstationarity of third order on L5

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Fig. 8

Indicators of cyclo-nonstationary of fourth order on L5

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Fig. 9

Indicators of cyclo-nonstationarity of second order on L1

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Fig. 10

Indicators of cyclo-nonstationarity of third order on L1

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Fig. 11

Indicators of cyclo-nonstationarity of fourth order on L

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Fig. 12

Indicators of cyclo-nonstationarity of second order on L4

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Fig. 13

Indicators of cyclo-nonstationarity of third order on L4

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Fig. 14

Indicators of cyclo-nonstationarity of fourth order on L4

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