A multi-objective parameter updating method for high-fidelity dynamic modeling of gear transmission systems

• A high-fidelity dynamic model of the gear transmission system is constructed. • Multi-objective function integrates time and frequency-domain information. • Sensitivity analysis identifies key parameters influencing vibration responses. • NSGA-II updates gear transmission system dynamic model parameters. Dynamic models are widely used due to their strong interpretability and physical consistency, making them effective tools for analyzing and predicting system behavior. A high-fidelity dynamic model can accurately represent the system’s dynamic response. However, many existing approaches depend on idealized assumptions, which often cause notable discrepancies between simulated and measured vibration responses. Moreover, most current optimization methods emphasize time-domain features, while the role of frequency-domain characteristics remains insufficiently explored. Therefore, this paper presents a parameter updating approach for the dynamic model, aiming to improve model fidelity and predictive reliability. First, a dynamic model of the gear transmission system is established based on the finite element method. Nonlinear excitations such as geometric eccentricity and gear backlash are introduced to improve model accuracy. Sensitivity analysis is then conducted to identify the key parameters influencing the system’s dynamic response. Finally, a multi-objective optimization of these key parameters is performed using the NSGA-II. Experimental validation on a single-stage gear test rig demonstrates the effectiveness and practicality of the proposed approach in reducing response discrepancies and improving model accuracy.

Authors

Institutions

Publication Details

Journal
Mechanism and Machine Theory
Published
2026-01-27
DOI
https://doi.org/10.1016/j.mechmachtheory.2026.106366
Primary Topic
Gear and Bearing Dynamics Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A multi-objective parameter updating method for high-fidelity dynamic modeling of gear transmission systems

Hong Guan, Caizi Fan, Hui Ma, Chao Zhao et al.
Mechanism and Machine Theory
Gear and Bearing Dynamics Analysis
article

A multi-objective parameter updating method for high-fidelity dynamic modeling of gear transmission systems

Hong Guan, Caizi Fan, Hui Ma, Chao Zhao, Yongchao Zhang, Jiaqi Liu, Tianyu Zhao
article en

Abstract

• A high-fidelity dynamic model of the gear transmission system is constructed. • Multi-objective function integrates time and frequency-domain information. • Sensitivity analysis identifies key parameters influencing vibration responses. • NSGA-II updates gear transmission system dynamic model parameters. Dynamic models are widely used due to their strong interpretability and physical consistency, making them effective tools for analyzing and predicting system behavior. A high-fidelity dynamic model can accurately represent the system’s dynamic response. However, many existing approaches depend on idealized assumptions, which often cause notable discrepancies between simulated and measured vibration responses. Moreover, most current optimization methods emphasize time-domain features, while the role of frequency-domain characteristics remains insufficiently explored. Therefore, this paper presents a parameter updating approach for the dynamic model, aiming to improve model fidelity and predictive reliability. First, a dynamic model of the gear transmission system is established based on the finite element method. Nonlinear excitations such as geometric eccentricity and gear backlash are introduced to improve model accuracy. Sensitivity analysis is then conducted to identify the key parameters influencing the system’s dynamic response. Finally, a multi-objective optimization of these key parameters is performed using the NSGA-II. Experimental validation on a single-stage gear test rig demonstrates the effectiveness and practicality of the proposed approach in reducing response discrepancies and improving model accuracy.

Mechanism and Machine TheoryVol. 220
Dalian Ocean University (CN), Foshan University (CN), Northeastern University (CN)
Openalex Percentile: Top 77%
Gear and Bearing Dynamics Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A multi-objective parameter updating method for high-fidelity dynamic modeling of gear transmission systems — Hong Guan, Caizi Fan, et al. · Mechanism and Machine Theory (2026) | TGRS Research Map | TGRS