Influence of Optimal Tooth Modifications on Dynamic Characteristics of a Vehicle Gearbox

Ullah, Najeeb; Cong, T.C.; Huan, B.; Yucheng, H.

doi:10.15282/ijame.16.1.2019.16.0478

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…, (i=1,2,3,4) (10) The above equations of dimensionless parameters are plugged into Eq. ( 1), and the following differential Eq.…”

Section: Model Descriptionmentioning

confidence: 99%

“…According to the method of tooth modification approach, Ullah [10] carried out the dynamic simulation of transmission error, safety factor and radial acceleration of the gearbox system and proposed the theoretical basis for the gear tooth micro-modification. Some research focuses on the dynamic load response of gear pairs under parametric excitation.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Dynamics of a Multi-shaft Gear System in Parameter-state Space

Yang

2023

Int. J. Automot. Mech. Eng.

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A nonlinear dynamic model of a 12-degree-of-freedom multi-shaft gear system is established, which includes nonlinear factors such as gear backlash, bearing clearance and time-varying mesh stiffness. The bifurcation diagrams and the maximum dynamic load coefficient diagrams that describe the dynamics of the gear transmission system are simulated by using the Runge-Kutta method, combined with three Poincaré mapping. The mutual transition of the adjacent period one motion through the grazing bifurcation and saddle-node bifurcation form a hysteresis zone where two types of impact motion coexist. The correlation between the dynamic response and the gear backlash under the parameter-state space is investigated, and it is verified that the extreme parameter conditions lead to abnormal vibration phenomena such as jumping, mesh-apart and chaotic motion. The results show that, near the critical value of ω = 0.7164 for grazing bifurcation, the meshing gear pair undergoes a jump in relative micro-displacement and dynamic load, increasing system impact vibration and a decrease in transmission efficiency, which is an undesirable parameter interval. In the initial stage of dynamic designing, the backlashes can be selected through the internal characteristics and transition mechanism of periodic motions

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“…, (i=1,2,3,4) (10) The above equations of dimensionless parameters are plugged into Eq. ( 1), and the following differential Eq.…”

Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics of a Multi-shaft Gear System in Parameter-state Space

Yang

2023

Int. J. Automot. Mech. Eng.

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“…Therefore, an approach that collects signal data at different running times may be more reliable and convincing for the industrial environment. Ullah et al [11] investigated the reduction of contact stresses and better load distribution in the gearbox by optimising the gearing under different operating conditions. Accurate interpretation of the measured vibration signal under different operational conditions requires deep understanding and expertise.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Reddy Mutra,

D Mallikarjuna Reddy,

Amarnath

et al. 2023

Int. J. Automot. Mech. Eng.

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This paper presents a gearbox defect diagnosis based on vibration behaviour. In order to record the vibration response under various circumstances, an industrial gearbox was used as the basis for an experimental setup. The signals resulting from gear wear were processed using an empirical mode decomposition for two operating time intervals (zero-hour running time and thirty-hour running time). The first three intrinsic mode functions and the corresponding frequency response were detected. The ten statistical parameters most sensitive to gear wear were selected using an evaluation method based on Euclidean distance. Using the identified features, an artificial neural network (ANN) was trained to track the gearbox for the selected future data set. The neural network received its input from the statistical parameters, and its output was the number of gearbox running hours. To achieve faster convergence, the radial basis function and the backpropagation neural network were compared. The superiority of the proposed strategy is demonstrated by comparing the performance of ANN. For monitoring the condition of industrial gears, the proposed strategy is found to be effective and trustworthy.

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