使用基于注意力的 CNN-BiLSTM 深度学习框架预测工作条件下汽车衬垫的摩擦性能

IF 1.5 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Xiaojing Yin, Sen Zhang, Yu Zhang, Zaixiang Pang, Bangcheng Zhang
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引用次数: 0

摘要

在长期运行过程中,汽车摩擦片的逐渐退化过程会严重影响机械设备的预期性能。此外,摩擦特性与多阶段退化过程之间的内在关联性大多被忽视,导致在工况多因素影响下的结果预测不够准确。本文提出了一种使用 CNN-BiLSTM-Att 模型的新型预测方法来克服这一问题。该模型使用 CNN 提取处理数据中的摩擦特征,并结合 BiLSTM 评估隐藏在摩擦数据中的时间序列特征。为了提高预测精度,该模型中加入了注意力机制,其优点是可以自动为隐层状态分配适当的权重,以区分不同数据特征的重要性。与其他机器学习算法相比,该方法具有较高的预测精度,可为制动提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Friction performance prediction of automotive pads under operating conditions using attention-based CNN-BiLSTM deep learning framework

In long-term operation, the gradual degradation process of automotive friction pads significantly affects the expected performance of mechanical equipment. In addition, the intrinsic correlations between friction properties and the multi-stage degradation process have been mostly ignored, leading to less accurate prediction of results under multifactorial influences on working conditions. In this paper, we propose a novel prediction method using the CNN-BiLSTM-Att model to overcome the problem. The model uses CNN to extract the friction features in the processed data, and combines with BiLSTM to evaluate the time series features hidden in the friction data. To improve the prediction accuracy, the attention mechanism is fed into the proposed model, which has the advantage of automatically assigning appropriate weights to the hidden layer states to distinguish the importance of different data features. Compared with other machine learning algorithms, the method has high prediction accuracy and can provide reference for braking.

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来源期刊
Journal of Mechanical Science and Technology
Journal of Mechanical Science and Technology 工程技术-工程:机械
CiteScore
2.90
自引率
6.20%
发文量
517
审稿时长
7.7 months
期刊介绍: The aim of the Journal of Mechanical Science and Technology is to provide an international forum for the publication and dissemination of original work that contributes to the understanding of the main and related disciplines of mechanical engineering, either empirical or theoretical. The Journal covers the whole spectrum of mechanical engineering, which includes, but is not limited to, Materials and Design Engineering, Production Engineering and Fusion Technology, Dynamics, Vibration and Control, Thermal Engineering and Fluids Engineering. Manuscripts may fall into several categories including full articles, solicited reviews or commentary, and unsolicited reviews or commentary related to the core of mechanical engineering.
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