[1]李 竹,和 丹,刘 晖,等. 基于MESIHRD-CNN 的驱动桥冲击异响主客观评价方法研究[J].机械与电子,2026,44(03):1-10.
　LI Zhu,HE Dan,LIU Hui,et al. Subjective-objective Assessment Method for Impact-induced Abnormal Noise in Drive Axles Based on MESIHRD-CNN[J].Machinery & Electronics,2026,44(03):1-10.
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 基于MESIHRD-CNN 的驱动桥冲击异响主客观评价方法研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年03期
页码:
1-10
栏目:
研究与设计
出版日期:
2026-03-25

文章信息/Info

Title:
 Subjective-objective Assessment Method for Impact-induced Abnormal Noise in Drive Axles Based on MESIHRD-CNN
文章编号:
1001-2257(2026)03-0001-10
作者:
 李 竹; 和 丹; 刘 晖; 徐婉钰
 (西安工程大学机电工程学院,陕西 西安 710048)
Author(s):
 LI Zhu; HE Dan; LIU Hui; XU Wanyu
 (College of Mechanical and Electrical Engineering,Xi’an Polytechnic University,Xi’an 710048,China)
关键词:
 ; 驱动桥; 齿轮啮合冲击; 解卷积; 声品质; 主客观评价
Keywords:
drive axle; gear meshing shock; deconvolution; sound quality; subjective-objective evaluation
分类号:
U463.218
文献标志码:
A
摘要:
 针对某型号驱动桥中存在冲击异响问题,开展异响预测方法研究。首先,采集驱动桥噪声信号并深入剖析异响特征,明确异响产生的内在机理。然后,采用等级评分法对86组噪声样本开展主观评价试验,为后续研究奠定主观评价基础。针对齿轮啮合冲击与桥壳固有频率卷积干扰问题,提出最小包络噪谐比解卷积方法(MESIHRD),用于消除齿轮啮合冲击与桥壳固有频率发生卷积带来的影响。随后,计算多维客观参数并进行相关性分析,验证了所提解卷积方法在识别齿轮啮合冲击上的有效性。最后,将MESIHRD与卷积神经网络(CNN)结合构建主客观评价模型,在特征层面对比解卷积特征与原始特征参数,在模型上对比BP神经网络(BPNN)、支持向量回归(SVR)和长短期记忆网络(LSTM)。研究结果表明,经所提解卷积处理后的特征参数与CNN 模型相结合,在驱动桥冲击异响预测中表现最佳,具有显著的工程应用价值。
Abstract:
 Aiming at the problem of impact induced noise in a specific type of drive axle,the method for predicting such noise is studied.At first,the noise signal from drive axle was collected,and the noise characteristics were thoroughly analyzed to clarify its underlying generation mechanism.Subsequently,a subjective evaluation test was conducted on 86 groups of noise samples using the rank scoring method,which laid a foundation for the subjective evaluation of the follow up study.To tackle the problem of convolutional interference between gear mesh impacts and natural frequency convolution of axle housing,a novel Minimum Envelope Spectrum Interference to Harmonic Ratio Deconvolution (MESIHRD) is proposed.This method eliminates the effects resulting from the convolution of gear mesh impacts and the natural frequency convolution of axle housing.Following this,multi dimensional objective parameters are calculated and subjected to correlation analysis,which verified the effectiveness of the proposed deconvolution method in identifying gear meshing impacts.Finally,a subjective and objective evaluation model is constructed by integrating MESIHRD with a convolutional neural network (CNN).Comparisons were made at the feature level between deconvolved features and original feature parameters,and at the model
level against Backpropagation Neural Network (BPNN),Support Vector Regression (SVR),and Long Short Term Memory (LSTM) networks.The results demonstrate that the combination of characteristic parameters processed by the proposed deconvolution method and the CNN model yields the best performance in predicting impact induced noise in the drive axle,showing significant potential for engineering applications.

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备注/Memo

备注/Memo:
 收稿日期:2025-09-06
基金项目:陕西省秦创原“科学家+工程师”队伍项目(2023KXJ 129);陕西省自然科学基础研究计划项目(2025JC YBMS 770)
作者简介:李 竹 (1999-),男,四川成都人,硕士研究生,研究方向为故障诊断和汽车NVH;和 丹 (1986-),男,陕西宝鸡人,博士,硕士研究生导师,研究方向为故障诊断和汽车NVH,通信作者,E-mail:hedan0425@xpu.edu.cn。
更新日期/Last Update: 2026-04-29