A correlation coefficient based vibration indicator for detecting natural pitting progression in spur gears

Kundu, Pradeep; Darpe, Ashish K.; Kulkarni, Makarand S.

doi:10.1016/j.ymssp.2019.04.058

Cited by 59 publications

(27 citation statements)

References 17 publications

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“…The increase in AM doesn’t have any effect on the carrier signal or GMF, but increase in FM tends to decrease the gear carrier signal amplitude. 30 Hence, from Figures 6 and 7, due to FM of the carrier signal, the amplitude of GMF is reduced for fault conditions.…”

Section: The Hi For Gear Tooth Pitting Degradation Assessmentmentioning

confidence: 94%

“…The residual vibration signal is more sensitive to the defect severity, and hence, it is expected that the correlation of the residual signal with reference to the healthy stage gear vibration signal decreases with increase in defect severity. 30 The correlation coefficient value obtained for the residual vibration signal can be used as a quantifier or a degradation parameter for gear condition assessment and is termed henceforth as correlation coefficient of residual vibration signal (CCR). The correlation between the residual vibration signals can be calculated as…”

Section: The Hi For Gear Tooth Pitting Degradation Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

Kundu

Darpe

Kulkarni

2019

Structural Health Monitoring

Self Cite

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This article presents an ensemble decision tree–based random forest regression methodology for remaining useful life prediction of spur gears subjected to pitting failure mode. The random forest regression methodology does not require an elaborate statistics background knowledge and has an inbuilt health indicator selection capability compared to other existing data-driven remaining useful life prediction approaches. A correlation coefficient parameter based on the residual vibration signal is used for monitoring and detecting the pitting progression in spur gears. The effectiveness of the correlation coefficient of the residual vibration signal is assessed over the other existing health indicators for pitting fault progression. To show the inbuilt best health indicator selection capability of the random forest regression model, initially, eight indicators (existing seven and the correlation coefficient of the residual vibration signal) were used for model training. In addition, the effect of fusing the vibration sensor data from multiple positions on the gearbox on prediction accuracy of the random forest regression model is also evaluated. The accuracy in the remaining useful life prediction is found to increase after fusing the correlation coefficient of the residual vibration signal based health indicator derived from the accelerometers located at multiple positions on the gearbox in comparison to data from a single accelerometer. Furthermore, the accuracy of the proposed methodology is tested and proven using five accelerated run-to-failure experimental data collected from the specially built test rig.

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Section: The Hi For Gear Tooth Pitting Degradation Assessmentmentioning

confidence: 94%

Section: The Hi For Gear Tooth Pitting Degradation Assessmentmentioning

confidence: 99%

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

Kundu

Darpe

Kulkarni

2019

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

show abstract

“…FCM algorithm steps are as follows: The single component signal is related to the parts that produce vibration impact [33], while the signal residue contains noise and hidden information [34]. The combination provides the possibility to extract the features of different components [35].…”

Section: B Principle Of Fcmmentioning

confidence: 99%

Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features

Wang

Zhao³

et al. 2021

IEEE Access

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“…A correlation coefficient based parameter for detecting the natural progression of pitting fault in spur gear was presented in [7].A novel spur gear dynamical model, validated by various experimental tests, to analytically investigate the effects of tooth pitting and spalling on the vibration responses of a gear transmission was built in [8],while study focuses on the nonlinear dynamic and vibration characteristics of spur gear pair with local spalling defect to explore the spalling mechanism [9]. The periodic change of the time-varying meshing stiffness which results in torsional vibration should be paid more attention to when studying the dynamics of gearbox.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Modelling and Simulation of Spur Gears with Flank Pitch Error

Tao¹,

Tian²,

Tang³

et al. 2021

Preprint

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Gearbox is commonly regarded as the most important power section of wind turbines which has been widely valued for its high malfunction rate. Gear fault researches mainly include wearing, pitting, spalling, breakage, falling off, etc, while little attention was paid to tooth Flank Pitch Error(FPE). Taking a single-stage parallel shaft spur gear as the research object, an 8-DOF gear transmission model and the FPE model were established in this paper and the gear’s time-varying meshing stiffness (TVMS) models with & without tooth FPE were obtained respectively, which the dynamic models with various tooth FPE values under different rotating speeds were simulated after. The simulation results showed that the TVMS mathematical model proposed in the paper under tooth FPE is practical at both low and high rotating speeds. Under the FPE model, side-bands are formed around each multiple of meshing frequency whose peaks are distributed by a fixed fault characteristic frequency ffp interval. The gearbox vibrates severely as the tooth FPE values and rotational speed grow. The peak value of the vibration signal is about 3 times that in case of fault-free state when the FPE value reaches 0.001rad, thus the impact of FPE on gearboxes cannot be neglected.

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A correlation coefficient based vibration indicator for detecting natural pitting progression in spur gears

Cited by 59 publications

References 17 publications

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features

Dynamical Modelling and Simulation of Spur Gears with Flank Pitch Error

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