[1]沈鹏,王书豪,索红光,等.基于改进DenseNet-BiGRU模型的滚动轴承故障诊断方法[J].郑州大学学报(工学版),2027,48(XX):1-9.
　SHEN Peng,WANG Shuhao,SUO Hongguang,et al.Bearing Fault Diagnosis Method Based on Improved DenseNet-BiGRU Network[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.
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基于改进DenseNet-BiGRU模型的滚动轴承故障诊断方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
48
期数:
2027年XX
页码:
1-9
栏目:
出版日期:
2027-12-10

文章信息/Info

Title:
Bearing Fault Diagnosis Method Based on Improved DenseNet-BiGRU Network
作者:
沈鹏1，王书豪1，索红光2，白宇2，陈江义1
1.郑州大学 机械与动力工程学院,河南 郑州 450001;2.华润新能源投资有限公司河南分公司,河南 郑州 450000
Author(s):
SHEN Peng1, WANG Shuhao1, SUO Hongguang2, BaiYu2, CHEN Jiangyi1
1.School of Mechanical and Power Engineering, Zhengzhou University, Henan 450001, China; 2.Henan Branch of CRRC New Energy Investment Co., Ltd., Henan 450000, China
关键词:
滚动轴承; 密集连接网络; 双向门控循环单元; 注意力机制
Keywords:
rolling bearing;  densely connected convolution network;  bidirectional gated recurrent unit;  attention mechanism
分类号:
TH133.33TP18
文献标志码:
A
摘要:
针对现有滚动轴承故障预测模型在噪声及变工况下适应能力不足、依赖人工特征提取导致主观性强等问题，提出了一种基于改进DenseNet-BiGRU的联合故障预测模型。该模型通过融合密集连接网络的特征重复利用的优势与双向门控循环单元的时序建模能力，并且在模型之间增加通道注意力机制，构建了端到端的故障预测框架。实验采用凯斯西储大学滚动轴承数据集，在理想条件下最高预测准确率达到了99.55%；在加入多种噪声干扰(信噪比范围-9dB~9dB)的情况下，平均预测准确率保持在91.71%，相较于常见的故障预测模型提升显著。此外，也验证了模型跨工况实验条件(转数为1772/1750/1730 r/min)下平均预测准确率99.18%强于其他的模型预测结果。实验结果表明，该联合模型能准确预测滚动轴承故障类型，在不同噪声强度和跨工况下均表现出优于常见模型的预测性能，验证了其鲁棒性与跨工况适应能力，为故障诊断领域提供了新的解决方案。
Abstract:
To address the shortcomings of existing rolling bearing fault prediction models, such as their insufficient adaptability to noise and variable operating conditions, and their reliance on manual feature extraction which leads to strong subjectivity, a joint fault prediction model based on an improved DenseNet-BiGRU is proposed. This model combines the advantages of feature reuse in dense connection networks and the time series modeling capabilities of bidirectional gated recurrent units, and adds a channel attention mechanism between the models to construct an end-to-end fault prediction framework. The experiments used the Case Western Reserve University rolling bearing dataset. Under ideal conditions, the highest prediction accuracy reached 99.55%. When various noise interferences were added (signal-to-noise ratio range -9dB to 9dB), the average prediction accuracy remained at 91.71%, showing a significant improvement compared to common fault prediction models. Additionally, the model’s average prediction accuracy of 99.18% under cross-operating condition experiments (rotational speeds of 1772/1750/1730 r/min) was superior to the prediction results of other models. The experimental results demonstrate that this joint model can accurately predict the fault types of rolling bearings and exhibits superior prediction performance under different noise intensities and load conditions, verifying its robustness and adaptability to cross-operating conditions, providing a new solution for the field of fault diagnosis.

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备注/Memo

备注/Memo:
收稿日期:2026-03-14;修订日期:2026-05-23基金项目:河南省重大科技专项(22110022010)作者简介:沈鹏(1985— ) ,男,河南郑州人,郑州大学讲师,博士,主要从事机器视觉及智能检测设备开发研究,E-mail:shenpengmtr@ 163. com。通信作者:陈江义(1974— ) ,男,湖北仙桃人,郑州大学教授,博士,主要从事多领域建模与仿真、数字化设计、机构学研究,E-mail:cjy1974@ zzu. edu. cn。
更新日期/Last Update: 2026-06-12