The Application of Correlation Dimension in Gearbox Condition Monitoring

Jiang, Jiahao; Chen, J.; Qu, Lei

doi:10.1006/jsvi.1998.2161

Cited by 77 publications

(37 citation statements)

References 11 publications

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“…Because of these impulses there is a significant difference between the signals from bearings with and without defects. It is relatively easy to identify defects in bearings using [8,11,[14][15][16]18]. Our aim in this paper is to use the method of recurrence along with some statistical parameters to analyze the severity of defects in bearings.…”

Section: Data Collectionmentioning

confidence: 99%

Sequential Recurrence Analysis of Experimental Time Series of a Rotor Response with Bearing Outer Race Faults

Kwuimy

Samadani

Kappaganthu

et al. 2014

Mechanisms and Machine Science

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Rolling elements bearings are one of the most common components used in expensive, high precision and critical machines such as gas turbines, rolling mills and gyroscopes. They can be subjected to various defects which could lead to catastrophic results. This includes inner and outer race defects and hence it is of interest to analyze the system response under such defects. A better understanding of the system performance under such defects can be beneficial when performing system diagnostics and system design. In this paper we are focused on the outer race defect and perform a comparative nonlinear time series analysis of a healthy system and a defective system. We consider various levels of outer race defects. The analysis is based on the recurrence properties of the system in its reconstructed state space. After determining the appropriate time lags through the average mutual information technique and the corresponding embedding dimensions through the false neighbor technique, we perform a sequential analysis of the system by subdividing the time series into bins and investigating the system response through recurrence quantification analysis parameters along with the entropy. This contributes to the enhancement of the science of diagnostics of outer race defects by analyzing the signature of various recurrence quantification analysis parameters as the system goes from a healthy state to a severely defective state.

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Section: Data Collectionmentioning

confidence: 99%

Sequential Recurrence Analysis of Experimental Time Series of a Rotor Response with Bearing Outer Race Faults

Kwuimy

Samadani

Kappaganthu

et al. 2014

Mechanisms and Machine Science

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show abstract

“…The estimation of correlation dimension for data generated by the Lorenz model. The values of embedding dimension and lag time are (m, t)=(11,12), (13,10), (16,8), (21,6). …”

Section: Phase Space Reconstructionmentioning

confidence: 99%

“…The differential equations are solved numerically using a fourth order Runge-Kutta integration with a desired accuracy of 10 À6 : Figure 1 (11,12), (13,10), (16,8), (21,6). window lengths that are equal.…”

Section: Phase Space Reconstructionmentioning

confidence: 99%

See 1 more Smart Citation

The Application of Pseudo-Phase Portrait in Machine Condition Monitoring

Wang¹,

Lin²

2003

Journal of Sound and Vibration

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“…Logan and Mathew [6] introduced the correlation dimension for the diagnosis of rolling element-bearing defects, the results showing that the correlation dimension can classify three major rolling element bearing faults: outer race fault, inner race fault and roller fault. Correlation dimension for gearbox condition monitoring is introduced in reference [7], the results showing that the state spaces of di!erent operating conditions construct into various low-dimensional attractors due to di!erent kinematics mechanisms.…”

Section: Introductionmentioning

confidence: 99%