Vibration characteristics of the gear shaft-bearing system with compound defects under variable operating conditions

Dai, Peng; Wang, Jianping; Yan, Shuping; Huang, Shengzhou; Martínek, Radek; Liu, Jun

doi:10.1177/14644193211055021

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…When the gear pair is operated under the condition of misalignment and center distance error, the meshing force between the gear teeth will be deflected, as shown in Figure 3(a) and Figure 3(b). Then the meshing stiffness of the gear pair can be quantified using the potential energy method [34], and the cantilever beam model is shown as Figure 3(c).…”

Section: Model Of the Gear Pair And Meshing Stiffnessmentioning

confidence: 99%

Dynamic Model Used for Describing the Coupled Vibration of Gear Pair and Bearing in the Gearbox

Su¹,

Dai²,

Shu³

et al. 2022

Preprint

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The fault diagnosis of the gearbox is primarily based on the analysis of the harmonic response. However, the generation mechanism of the harmonic response of the gear pair is still not distinctly explained in a healthy gearbox. Therefore, this paper is dedicated to establishing a dynamic model of the gearbox for this issue. In this model, the contact force of the bearing and the meshing stiffness of the gear pair are determined by the Hertz theory and the potential energy method, then the shaft motion is used as the connection to establish the coupling relationship between the gear pair and the bearing. After this dynamic model is confirmed by experiments and existing models, the coupling relationship inside the gearbox and the harmonic response of the gear pair are studied using this model. The result demonstrates that a closed control loop is formed due to the vibration transmission between the gear pair, the bearing and the shaft, and there is a strong coupled vibration between the gear pair and the bearing in the gearbox. Due to the generation of the center distance error, misalignment angle and disorganized meshing process of the gear pair, the meshing stiffness is altered, which excites the harmonic response of the gear pair. When the speed of the gearbox is increased, although the vibration amplitude of the gearbox is increased, the system predominantly vibrates at the fundamental frequency, and the harmonic of the gear pair is also shifted to the low frequency band.

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Section: Model Of the Gear Pair And Meshing Stiffnessmentioning

confidence: 99%

Dynamic Model Used for Describing the Coupled Vibration of Gear Pair and Bearing in the Gearbox

Su¹,

Dai²,

Shu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, early fault features are weak and easily obscured by noise [ 8 , 9 ]. Complex working conditions, such as speed fluctuation and load, add challenges to fault diagnosis [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions

Dong,

Zheng,

Wen

et al. 2024

Sensors

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Recent advancements in applications of deep neural network for bearing fault diagnosis under variable operating conditions have shown promising outcomes. However, these approaches are limited in practical applications due to the complexity of neural networks, which require substantial computational resources, thereby hindering the advancement of automated diagnostic tools. To overcome these limitations, this study introduces a new fault diagnosis framework that incorporates a tri-channel preprocessing module for multidimensional feature extraction, coupled with an innovative diagnostic architecture known as the Lightweight Ghost Enhanced Feature Attention Network (GEFA-Net). This system is adept at identifying rolling bearing faults across diverse operational conditions. The FFE module utilizes advanced techniques such as Fast Fourier Transform (FFT), Frequency Weighted Energy Operator (FWEO), and Signal Envelope Analysis to refine signal processing in complex environments. Concurrently, GEFA-Net employs the Ghost Module and the Efficient Pyramid Squared Attention (EPSA) mechanism, which enhances feature representation and generates additional feature maps through linear operations, thereby reducing computational demands. This methodology not only significantly lowers the parameter count of the model, promoting a more streamlined architectural framework, but also improves diagnostic speed. Additionally, the model exhibits enhanced diagnostic accuracy in challenging conditions through the effective synthesis of local and global data contexts. Experimental validation using datasets from the University of Ottawa and our dataset confirms that the framework not only achieves superior diagnostic accuracy but also reduces computational complexity and accelerates detection processes. These findings highlight the robustness of the framework for bearing fault diagnosis under varying operational conditions, showcasing its broad applicational potential in industrial settings. The parameter count was decreased by 63.74% compared to MobileVit, and the recorded diagnostic accuracies were 98.53% and 99.98% for the respective datasets.

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Mesh stiffness modeling and dynamic simulation of spur gears with body crack under speed fluctuation condition

Jiang,

Liu,

Zhang

et al. 2024

Mechanics Based Design of Structures and Machines

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Vibration characteristics of the gear shaft-bearing system with compound defects under variable operating conditions

Cited by 5 publications

References 39 publications

Dynamic Model Used for Describing the Coupled Vibration of Gear Pair and Bearing in the Gearbox

Dynamic Model Used for Describing the Coupled Vibration of Gear Pair and Bearing in the Gearbox

Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions

Mesh stiffness modeling and dynamic simulation of spur gears with body crack under speed fluctuation condition

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