A quantitatively distributed wear-measurement method for spur gears during micro-pitting and pitting tests

Lin, Jiachun; Chen, Teng; Bergstedt, Edwin; Li, Hanxiao; Shi, Zhaoyao; Olofsson, Ulf

doi:10.1016/j.triboint.2020.106839

Cited by 27 publications

(4 citation statements)

References 12 publications

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“…However, the acoustic emission sensor should be close enough to the engaging gears; otherwise, insufficient information will be acquired for gear wear monitoring. A profilometer was used in Lin et al 13 to measure the gear wear. Also, Resendiz-Ochoa et al 14 used infrared thermography to assess the gear wear, together with a supervised learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Also, Resendiz-Ochoa et al 14 used infrared thermography to assess the gear wear, together with a supervised learning algorithm. However, as introduced in Lin et al 13 and Resendiz-Ochoa et al, 14 special devices and designs are required for signal acquisition to do further wear analysis. In industrial practices, such as machine tools and robotics, which have compact structures, there might be no sufficient space for installing these special signal acquisition devices, limiting the practicality of these techniques.…”

Section: Introductionmentioning

confidence: 99%

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A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

Feng

Chen

et al. 2023

Structural Health Monitoring

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The gearbox is widely applied as the mechanical transmission system of intelligent manufacturing systems, such as machine tools and robotics. The harsh working environments make the gear surface prone to wear. The progression of surface wear can bring severe failures to the gear tooth, including gear tooth root crack, surface spalling of gear tooth, and tooth breaking, all of which could damage the whole transmission system. Hence, it is essential to monitor and evaluate the gear surface wear propagation. The gear wear has been proven highly relevant with the vibration second-order cyclostationary (CS2) characteristics. Therefore, this paper develops a novel cyclostationarity-based framework to monitor and evaluate gear wear propagation. More specifically, the squared envelope (SE) of the residual signal, removing deterministic components, is utilized to identify the gear wear distribution and its propagation trends, validated using the measured gear surface morphology. Moreover, a new CS2-based indicator is proposed to assess the severity of gear surface wear, achieving a high correlation with measured surface roughness: [Formula: see text] is more than 0.9. The developed cyclostationarity-based framework can comprehensively evaluate the degradation status of the gear system caused by surface wear, significantly benefiting the health management of the gear transmission system, which is of great practical value for the health management of intelligent manufacturing systems. A series of endurance tests are conducted to verify the effectiveness and superiority of the developed framework for gear wear monitoring compared with the conventional indicators.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

Feng

Chen

et al. 2023

Structural Health Monitoring

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“…Natural pitting propagation is a complex process, and it differs from artificially seeded pitting. The underlying reason is that other wear phenomena (such as abrasive wear) usually occur alongside pitting during natural pitting propagation, 15,16 and the interactions between those wear mechanisms and system dynamic properties make the generated vibrations quite complex. Therefore, up to now, only limited conventional indicators/techniques have been investigated and applied to monitor natural pitting progression.…”

Section: Introductionmentioning

confidence: 99%

A novel vibration indicator to monitor gear natural fatigue pitting propagation

Feng

et al. 2023

Structural Health Monitoring

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Fatigue pitting can reduce the gear surface durability and induce other severe failures, which will eventually lead to the complete loss of transmission function of the transmission system. Thus, monitoring fatigue pitting progression is vital to avoid unexpected economic losses and incidents. Thanks to the unique characteristics of the gear meshing process, there is a close relationship between the tribological features of fatigue pitting and gear vibration cyclostationarity. Based on the vibration cyclostationarity, this paper develops a novel second-order cyclostationary (CS2) fatigue pitting monitoring indicator, which can accurately assess the degradation status of the gear system and benefit subsequent health management. The advantage of the developed cyclostationary indicator in evaluating and monitoring the process of fatigue pitting propagation is demonstrated with the natural fatigue pitting progression test, through comparisons with other conventional indicators.

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“…Flodin and Andersson 15 were the first to apply the Archard model to tooth profile wear of gears and obtained the distribution pattern of the gear wear mechanism on the tooth profile, the shortcoming is that the proposed helical gear model is for light wear and no long-period wear theory study has been conducted. Lin et al 16 used a new algorithm to evaluate distributed wear accumulation during a test phase. And the real tooth profile was obtained.…”

Section: Introductionmentioning

confidence: 99%

Research on time-varying meshing stiffness of wind turbine gearbox considering tooth surface wear

Mo,

Hu,

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part J:

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The time-varying meshing stiffness (TVMS) is an important element of the gear system. In this paper, the TVMS of each gear set of a wind turbine gearbox is calculated based on the principle of potential energy method, where wear is the most likely failure in the operation of a wind turbine gearbox. Therefore, the Archard wear model is brought into it, and theoretically the analytical formula of stiffness considering tooth wear is derived, and the time-varying wear depth is deduced as the natural wind speed keeps changing and the input torque of the wind turbine gearbox keeps changing. The interrelationship between gear tooth wear and meshing pressure angle and number of meshes is studied in depth, and the TVMS of each gear set under wear is calculated using the modified tooth profile model. The results show that the tooth profile wear depth is related to the gear meshing angle, and the tooth profile wear depth becomes larger as the number of meshes increases, thus reducing the TVMS of the wind turbine, and the TVMS shows irregular fluctuations under time-varying wear.

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A quantitatively distributed wear-measurement method for spur gears during micro-pitting and pitting tests

Cited by 27 publications

References 12 publications

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

A novel vibration indicator to monitor gear natural fatigue pitting propagation

Research on time-varying meshing stiffness of wind turbine gearbox considering tooth surface wear

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