A time‐varying meshing stiffness model for gears with mixed elastohydrodynamic lubrication based on load‐sharing

Gu, Yingkui; Chen, Ronghua; Qiu, Guangqi; Huang, Peng

doi:10.1002/qre.3487

Cited by 3 publications

(5 citation statements)

References 50 publications

(106 reference statements)

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“…In EHL conditions, the load distribution between the gear meshing surfaces is shared by the oil film and the asperities of the gear's rough surface. To improve the modeling accuracy, the time-varying meshing stiffness (TVMS) of the gear pairs under EHL is calculated based on load-sharing concept in the previous research [23]. The TVMS calculation model considers that contact load between the meshing surfaces of the gear pair is distributed among the asperities and the lubricating film under mixed EHL, and the TVMS is calculated using the potential energy method.…”

Section: Dynamic Modeling Of Pgt Based On Focmentioning

confidence: 99%

“…The contact stiffness and time-varying friction coefficient of the gear pair are determined through load-sharing analysis. The TVMS calculation is found in [23]. in details.…”

Section: Dynamic Modeling Of Pgt Based On Focmentioning

confidence: 99%

“…h is the oil film thickness. The oil film pressure and thickness are calculated by the equations ( 7) and (33) in [23].…”

Section: Dynamic Modeling Of Pgt Based On Focmentioning

confidence: 99%

“…The meshing damping ratio of gears ς = 0.10 [28] is taken in simulation. Usually, the coefficient of friction is small under mixed EHL conditions, and it is set as 0.05 [23]. The tooth backlash is 0.5 µm [18].…”

Section: Experiments B: Verification and Analysis Of The Proposed Dyn...mentioning

confidence: 99%

“…To investigate the influence of temperature, the speed of the gear is set to 1000 rpm, the load of the carrier is 50 N•m, and the viscosity of the lubricating oil at different temperatures is calculated by the equation (44) in [23]. The other parameters are set as previously.…”

Section: Experiments B: Verification and Analysis Of The Proposed Dyn...mentioning

confidence: 99%

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Dynamic analysis of planetary gear transmission based on Lagrange interpolation polynomials

Chen,

Gu,

Qiu

et al. 2024

Meas. Sci. Technol.

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This paper proposes a novel dynamic model considering manufacturing errors and eccentricity errors to analyze the dynamics of planetary gear transmission (PGT). The dynamic model is established based on the fractional-order calculus (FOC) and solved by an enhanced fourth-order Lagrange interpolation polynomials (LIP) method. Three numerical examples and the vibration experiments of planetary gear transmission are employed for verification. The comparison results indicate that the proposed solution method has higher solution accuracy and efficient than the existing algorithms in solving fractional equations, and the relative errors of the proposed solution method in three examples are 0.32%, 0.78% and 0.16%, respectively. The proposed dynamic model of PGT has better agreement with the experimentally measured signal compared with the integer-order dynamic model, and the maximum error and average error of the characteristic frequency amplitude between the proposed dynamic model and the measured signal are 4.76% and 3.57%, respectively. The proposed method contributes to the theoretical foundation for the signal monitoring of PGT.

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Section: Dynamic Modeling Of Pgt Based On Focmentioning

confidence: 99%

“…The contact stiffness and time-varying friction coefficient of the gear pair are determined through load-sharing analysis. The TVMS calculation is found in [23]. in details.…”

Section: Dynamic Modeling Of Pgt Based On Focmentioning

confidence: 99%

“…h is the oil film thickness. The oil film pressure and thickness are calculated by the equations ( 7) and (33) in [23].…”

Section: Dynamic Modeling Of Pgt Based On Focmentioning

confidence: 99%

Section: Experiments B: Verification and Analysis Of The Proposed Dyn...mentioning

confidence: 99%

Section: Experiments B: Verification and Analysis Of The Proposed Dyn...mentioning

confidence: 99%

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Dynamic analysis of planetary gear transmission based on Lagrange interpolation polynomials

Chen,

Gu,

Qiu

et al. 2024

Meas. Sci. Technol.

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Advanced reliability and safety methodologies and novel applications (Selected papers of the international conference of QR2MSE2023)

Huang,

Li,

2024

Quality & Reliability Eng

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Modern engineering systems tend to be more complicated than ever before, facilitated by novel design concepts and advancements in new technologies such as sensors, materials, and structural (or functional) integrity. Reliability and safety investigations are to find the most feasible, economical, and easy-to-follow solutions for failure prevention and performance improvement with the assistance of operation state modeling and analysis, failure and reliability analysis with environmental issues, maintenance strategy planning, and so forth.The demand for state-of-the-art solutions in these areas is surging, providing feasible approaches to guide both performance and profitability enhancement. In response, this special issue showcases original research that addresses newly emerged reliability and safety challenges in modern engineering systems. It contributes innovative ideas to academia and presents the latest and most intriguing applications in engineering practice.This special issue publishes novel, and the most representative reliability and safety achievements presented at the 2023

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A Signal Generation Fault Diagnosis Method for Planetary Reducer of High‐Pressure Grinding Roll

Chen,

Gu,

Qiu

et al. 2024

Quality & Reliability Eng

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This paper proposes a signal generation fault diagnosis method for the challenge of insufficient training samples in the planetary reducer of high‐pressure grinding roll (HPGR). The expression of the vibration response signal is derived, and a fault vibration signal model is established for the planetary reducer of HPGR. To generate the expanded samples, an adaptive fox optimization (AFO) algorithm is employed for optimizing the parameters of the fault vibration signal model, so that the simulated signal matches the measured signal. Before that, the measured vibration signal of the planetary reducer of HPGR is preprocessed by a tunable Q‐factor wavelet transform (TQWT). Finally, the residual convolution neural and long short‐term memory network with attention mechanism (ResCNN‐LSTM‐ATT) is utilized for feature extraction from collected signals, and the hierarchical fault classifier is developed for the fault classification. The experimental results show that the AFO algorithm has better performance, and the signals generated by the optimized fault vibration signal model are in good agreement with the measured signals. The classification accuracy of normal signals reaches 99.70%, which shows good diagnosis accuracy in both known fault classification and unknown fault identification. Compared to other methods, the proposed method has better diagnostic accuracy.

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A time‐varying meshing stiffness model for gears with mixed elastohydrodynamic lubrication based on load‐sharing

Cited by 3 publications

References 50 publications

Dynamic analysis of planetary gear transmission based on Lagrange interpolation polynomials

Dynamic analysis of planetary gear transmission based on Lagrange interpolation polynomials

Advanced reliability and safety methodologies and novel applications (Selected papers of the international conference of QR2MSE2023)

A Signal Generation Fault Diagnosis Method for Planetary Reducer of High‐Pressure Grinding Roll

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