Multi-dimensional evaluation and prediction of vibration comfort in electric loaders using ACO-Transformer

  • Ruxue Dai School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
  • Jian Zhao LiuGong Global R&D Center, Liuzhou Liugong Excavator Co., Ltd., Liuzhou 545027, China
  • Qingli Sui LiuGong Global R&D Center, Liuzhou Liugong Excavator Co., Ltd., Liuzhou 545027, China
  • Weidong Zhao LiuGong Global R&D Center, Liuzhou Liugong Excavator Co., Ltd., Liuzhou 545027, China
  • Weiping Ding School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
  • Haibo Huang School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Article ID: 3523
DOI: https://doi.org/10.59400/sv3523
Keywords: ACO-Transformer; operator comfort; vibration prediction

Abstract

Engineering machinery plays a vital role in supporting modern economic development. The electric loader represents a key innovation driven by environmental protection and the pursuit of sustainable development. However, the absence of engine masking effects in electric machinery makes structural vibration and impact noise more pronounced. To address this issue, this study proposes an ant colony optimization-Transformer (ACO-Transformer) model that integrates the ant colony algorithm with the Transformer framework to accurately and efficiently evaluate the vibration comfort of electric engineering machinery. An improved objective evaluation method for vibration was employed to extract objective data from four measurement points, while 34 subjective scores were obtained through a structured subjective evaluation protocol. The combined analysis of subjective and objective data demonstrated the validity of incorporating additional vibration measurement points. Using these datasets, the ACO-Transformer model was developed to establish a mapping between multi-dimensional objective vibration parameters and subjective comfort ratings. Results indicate that the proposed model achieved high prediction accuracy (MAPE = 6.22%) and strong generalization performance (RMSE = 6.11). This study offers a novel approach for evaluating and predicting the vibration comfort of engineering machinery cabins.

Published
2025-08-29
How to Cite
Dai, R., Zhao, J., Sui, Q., Zhao, W., Ding, W., & Huang, H. (2025). Multi-dimensional evaluation and prediction of vibration comfort in electric loaders using ACO-Transformer. Sound & Vibration, 59(4), 3523. https://doi.org/10.59400/sv3523
Issue
Vol. 59 No. 4 (2025)
Section
Article

Copyright (c) 2025 Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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