Gear tooth surface damage diagnosis based on analyzing the vibration signal of an individual gear tooth

Fan, Qingrong; Zhou, Qi; Wu, Chaoqun; Guo, Min

doi:10.1177/1687814017704356

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…In the present work, it is proposed to fuse the HI value extracted from the same type of sensor but from different positions on the gearbox. In the literature, researchers 38,39 used the principal component analysis (PCA) approach for the parameter fusion. However, the fusion made based on the PCA algorithm loses interpretability.…”

Section: Resultsmentioning

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

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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: Resultsmentioning

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

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“…Hence, the TSA time domain waveform is cleaner/purer compared to the original signal. 107 Some signal processing techniques are developed 108–110 in which tachometer signal information can be extracted from the vibration signal itself and hence TSA can also be performed without the need of a separate tachometer sensor.…”

Section: Data-driven Approachesmentioning

confidence: 99%

“…Another way for HI space selection is to fuse all the indicators in such a way that it reduces the dimensionality of the extracted indicator space and at the same time retains the sensitivity or variability of all the indicators. Algorithms such as PCA, 107,[189][190][191][192][193][194][195] ICA, 191 and Mahalanobis distance (MD) 196 are widely used for indicator fusion/compression.…”

Section: Processing/selection Of the Hismentioning

confidence: 99%

A review on diagnostic and prognostic approaches for gears

Kundu

Darpe

Kulkarni

2020

Structural Health Monitoring

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Prognostics and health management has become a significant part of component life-cycle in modern industries. The prognostics and health management framework is implemented in the industries to identify the fault type, assess fault severity, and predict the future state or remaining useful life to optimize the maintenance activities. Three significant aspects of a prognostics and health management framework are diagnostics, prognostics, and decision making. This article presents a review of different types of diagnostic and prognostic approaches (i.e. physics-based, data-driven, and hybrid approaches) developed for the gears. The flow of information between diagnostics and prognostics parts of the framework is briefly discussed. Regarding the physics-based approaches, this article discusses different physics-based diagnostic and prognostic models developed for different types of gear failure modes such as crack, pitting, and wear. In the data-driven approaches, the article attempts to summarize the data processing techniques used for extracting fault-related information from the recorded raw vibration signal, health indicators developed for different kinds of gear failure modes, processing/selection approaches for best health indicators, fault classification, and fault prognostic models particularly developed for the gear. The article discusses how a hybrid approach can be developed by the integration of a data-driven diagnostics approach and a physics-based prognostics approach. Finally, uncertainty quantification of prognostic approaches, performance evaluation metrics, decision-making strategies, and future research and development perspectives are discussed. This article focuses on the diagnostic and prognostic approaches developed for gears, given the fact that these approaches for other components such as bearing and batteries are reviewed in the past.

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“…These statistical values are used for the detection of faults in gears by wear, using vibration signals with outstanding results. For example, Fan et al [35] reported a gear damage diagnosis and classification using support vector machines based on statistical parameters, in the time domain.…”

Section: Design and Development Of The Upgrade Kitmentioning

confidence: 99%

A Case about the Upgrade of Manufacturing Equipment for Insertion into an Industry 4.0 Environment

García-Garza

Ahuett–Garza

López

et al. 2019

Sensors

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Industry 4.0 is a synonym for the confluence of technologies that allows the integration of information technology, data science, and automated equipment, to produce smart industrial systems. The process of inserting new technologies into current conventional environments involves a wide range of disciplines and approaches. This article presents the process that was followed to identify and upgrade one station in an industrial workshop to make it compatible with the more extensive system as it evolves into the Industry 4.0 environment. An information processing kit was developed to upgrade the equipment from an automated machine to an Industry 4.0 station. The kit includes a structure to support the sensor and the data processing unit; this unit consisted of a minicomputer that records the data, graded the performance of the components, and sent the data to the cloud for storage, reporting, and further analysis. The information processing kit allowed the monitoring of the inspection system and improved the quality and speed of the inspection process.

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Gear tooth surface damage diagnosis based on analyzing the vibration signal of an individual gear tooth

Cited by 14 publications

References 22 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

A review on diagnostic and prognostic approaches for gears

A Case about the Upgrade of Manufacturing Equipment for Insertion into an Industry 4.0 Environment

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