[1]王 骅,周俊宏,李文泽,等.基于SSA-VMD-CNN-BiLSTM 的张力机轴承故障分级诊断[J].机械与电子,2026,44(08):26-34.
　WANG Hua,ZHOU Junhong,LI Wenze,et al.Multi-level Fault Diagnosis of Tensioner Bearings Based on SSA-VMD-CNN-BiLSTM[J].Machinery & Electronics,2026,44(08):26-34.
点击复制

基于SSA-VMD-CNN-BiLSTM 的张力机轴承故障分级诊断()
分享到：

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年08期
页码:
26-34
栏目:
智能检测
出版日期:
2026-08-25

文章信息/Info

Title:
Multi-level Fault Diagnosis of Tensioner Bearings Based on SSA-VMD-CNN-BiLSTM
文章编号:
1001-2257(2026)08-0026-09
作者:
王 骅1; 周俊宏1; 李文泽1; 王海燕1; 吴伟智2; 杨 先2
1.广东电网有限责任公司惠州供电局,广东 惠州 516001; 2.广东电网能源发展有限公司,广东 广州 510160
Author(s):
WANG Hua1; ZHOU Junhong1; LI Wenze1; WANG Haiyan1; WU Weizhi2; YANG Xian2
(1.Huizhou Power Supply Bureau,Guangdong Power Grid Co.,Ltd.,Huizhou 516001,China; 2.Guangdong Power Grid Energy Development Co.,Ltd.,Guangzhou 510160,China)
关键词:
张力机; 轴承; 故障诊断; 麻雀搜索算法; 双向长短期记忆网络
Keywords:
tensioner; bearing; fault diagnosis; sparrow search algorithm; bidirectional long short-term memory network
分类号:
TH133.3;TP183
文献标志码:
A
摘要:
针对新能源张力机轴承在低速重载、频繁启停和张力波动工况下故障特征微弱、非平稳性强以及多级分类识别困难的问题,构建了一种融合麻雀搜索算法(SSA)优化变分模态分解(VMD)、卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)的SSA VMD CNN BiLSTM 轴承故障诊断模型。该模型以本征模态分量(IMF)排列熵之和作为适应度函数,引入麻雀搜索算法对VMD模态数K 和惩罚因子α 进行自适应优化,以获得时频聚集性和故障敏感性更优的本征模态分量。随后,将优化后的IMF分量输入CNN提取局部深层特征,并通过BiLSTM 对故障特征序列的双向时序相关性进行建模,实现新能源张力机发电机主轴轴承正常、内圈故障、外圈故障和滚动体故障等状态的分类识别。实验结果表明,排列熵作为SSA 寻优适应度函数时模型诊断性能较优,所提出的SSA-VMD CNN-BiLSTM 模型测试集分类准确率达到98.33%,Macro F1值达到98.31%,平均运行时间为11.04 s,均优于CNN-BiLSTM 和SSA-VMD-CNN-LSTM 等对比模型。多指标评价、混淆矩阵、统计显著性检验和消融实验结果进一步表明,SSA-VMD、CNN 和BiLSTM 各模块能够分别增强故障敏感分量表达、局部特征提取和双向时序建模能力。
Abstract:
To address the challenges of the weak fault signatures,strong non-stationarity,and difficulty in multi-class fault classification for bearings in new-energy tensioners operating under low-speed heavy-load,frequent start-stop,and tension fluctuation conditions,this paper proposes a hybrid fault diagnosis model that integrates Sparrow Search Algorithm (SSA)-optimized Variational Mode Decomposition (VMD),Convolutional Neural Network (CNN),and Bidirectional Long Short-Term Memory (BiLSTM), denoted as SSA – VMD– CNN –BiLSTM.In the proposed model,the sum of permutation entropy of the intrinsic mode functions (IMFs) is adopted as the fitness function,and the SSA is introduced to adaptively optimize the number of decomposition modes K and penalty factor α in VMD,thereby yielding IMFs with superior time-frequency concentration and fault sensitivity.The optimized IMF components are then fed into a CNN to extract local deep features,followed by a BiLSTM network to model the bidirecional temporal dependencies of fault feature sequences,enabling the classification of four states of the generator spindle bearing in new energy tensioners:normal state,inner race fault,outer race fault,and rolling element fault.Experimental results show that using permutation entropy as the SSA fitness function yields the best diagnostic performance.The proposed model achieves a test-set classification accuracy of 98.33%,a Macro-F1 value of 98.31%,and an average running time of 11.04 s,outperforming comparison models,including the CNN-BiLSTM and SSA-VMD-CNN-LSTM.Multi-metricevaluation,confusion matrix analysis,statistical significance tests,and ablation experiments further demonstrate that the SSA-VMD,CNN,and BiLSTM modules respectively enhance fault sensitive component representation,local feature extraction,and bidirectional temporal modeling capability.

参考文献/References:

[1] 张永鑫,宋晓庆,王志阳.低速重载滚动轴承状态 PSO-SVM 识别方法研究[J].机械设计与制造,2025(10):273-278. [2] Song Baoye,Liu Yiyan,Fang Jingzhong,et al.An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples[J].Neurocomputing,2024,574:127284. [3] 许一凡,雪增红,郭军,等.基于特征增强时间序列和 Transformer-BiLSTM 的低速重载轴承故障诊断[J].机电工程,2025,42(9):1659-1668. [4] 王楠,孙凤久,陈长征.基于小波分析的低速重载轴承故障诊断[J].东北大学学报(自然科学版),2005,26(1):281-283. [5] 毛欢,魏志刚,刘迎松,等.基于VMD-PE-MCKD低速重载滚动轴承故障特征提取[J].噪声与振动控制,2022,42(4):152-157. [6] Gharehchopogh F S,Namazi M,Ebrahimi L,et al.Advances in sparrow search algorithm:a comprehensive survey[J].Archives of Computational Methods in Engineering,2023,30(1):427-455. [7] Wang Mengjiao,Wang Wenjie,Zeng Jinfang,et al.An integrated method based on sparrow search algorithm improved variational mode decomposition and support vector machine for fault diagnosis of rolling bearing[J].Journal of Vibration Engineering and Technologies,2022,10(8):2893-2904. [8] Lv Jie,Sun Wenlei,Wang Hongwei,et al.Coordinated approach fusing RCMDE and sparrow search algorithm-based SVM for fault diagnosis of rolling bearings[J].Sensors,2021,21(16):5297. [9] Li Zhen,Chen Jinglong,Zi Yanyang,et al.Independence oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive[J].Mechanical Systems and Signal Processing,2017,85:512-529. [10] Zhou Junbo,Xiao Maohua,Niu Yue,et al.Rolling bearing fault diagnosis based on WGWOA-VMD-SVM[J].Sensors,2022,22(16):6281. [11] Wang Xiangkun,Li Jiahong,Jing Zhe,et al.Fault diagnosis method of rolling bearing based on SSA-VMD and RCMDE[J].Scientific Reports,2024,4(1):30637. [12] Liang Kaiwei,Qin Na,Huang Deqing,et al.Convolutional recurrent neural network for diagnosis of high-speed train bogie[J].Complexity,2018,12(10):1-13. [13] 赵小强,罗维兰,梁浩鹏.基于SIR多级残差连接密集网络的轴承故障诊断[J].兰州理工大学学报,2022,48(6):46-54. [14] Hu Pan,Zhao Cunsheng,Huang Jicheng,et al.Intelligent and small samples gear fault detection based on wavelet analysis and improved CNN[J].Processes,2023,11(10):2969. [15] 杜占涛,纪爱敏,陈曦晖,等.基于ISVD 多级降噪和 SVM 的轴承故障诊断研究[J].机电工程,2022,39(5):567-577. [16] Yan Jin,Zhou Fubing,Zhu Xu,et al.AFSA-FastICA-CEEMD rolling bearing fault diagnosis method based on acoustic signals[J].Mathematics,2025,13(5):884-884. [17] 康佳垚,张文亮,赵雅强,等.基于逐次变分模态分解和融合网络的滚动轴承故障诊断[J].中国测试,2026,52(3):120-126. [18] 王祎颜,王衍学,姚家驰.基于VMD-CNN BiLSTM的轴承故障多级分类识别[J].机电工程,2024,41(9):1554-1564. [19] Sun Weiqing,Wang Yue,You Xingyi,et al.Optimization of variational mode decomposition-convolutional neural network-bidirectional long short term memory rolling bearing fault diagnosis model based on improved dung beetle optimizer algorithm[J].Lubricants,2024,12(7):239. [20] Xue Jiankai,Shen Bo.A novel swarm intelligence optimization approach:sparrow search algorithm[J].Systems Science and Control Engineering,2020,8(1):22-34.

相似文献/References:

[1]韩海燕,李娜娜,尚雪梅.颗粒间距及相对运动对轴承油膜压力的影响[J].机械与电子,2019,(03):27.
　HAN Haiyan,LI Nana,SHANG Xuemei.Effects of Particles Distance and Relative Motion on Oil Film Pressure of Bearing[J].Machinery & Electronics,2019,(08):27.
[2]吉 莉,卯 杰.电机转子轴承拆卸工装的研制与应用[J].机械与电子,2019,(05):46.
　.Development and Application of Disassembly Tooling for Electronic Rotationg Shaft Bearing[J].Machinery & Electronics,2019,(08):46.
[3]孟庆宇.基于 ＥＥＭＤ- ＳＶＭ 的自动扶梯主驱动轴轴承故障诊断方法研究[J].机械与电子,2020,(05):51.
　Research on Fault Diagnosis Method for Main Drive Shaft Bearing of Escalator Based on EEMD-SVM [J].Machinery & Electronics,2020,(08):51.
[4]魏常庆,张 皓.铁路货车轮毂对轴承压装力-位移曲线的影响研究[J].机械与电子,2024,42(05):76.
　WEI Changqing,ZHANG Hao.Research on the Influence of Railway Freight Car Hub on the Force Displacement Curve of Bearing Press Mounting[J].Machinery & Electronics,2024,42(08):76.
[5]骆东松,马立东.基于深度学习的风电机组轴承诊断系统[J].机械与电子,2024,42(07):69.
　LUO Dongsong,MA Lidong.Deep Learning based Bearing Diagnosis System for Wind Turbines[J].Machinery & Electronics,2024,42(08):69.
[6]武 逵,王城宇,万书亭.基于 IGWO-MCKD-ROMP 的齿轮箱轴承故障特征提取方法[J].机械与电子,2025,(01):3.
　WU Kui,WANG Chengyu,WAN Shuting.Fault Feature Extraction Method of Gearbox Bearings Based on IGWO-MCKD-ROMP[J].Machinery & Electronics,2025,(08):3.
[7]陈万胜,谭 震,高永军,等.矿用切带机主轴轴承故障诊断方法研究[J].机械与电子,2026,44(04):53.
　CHEN Wansheng,TAN Zhen,GAO Yongjun,et al.Research on Fault Diagnosis Method for Main Shaft Bearings of Mining Band Cutting Machines[J].Machinery & Electronics,2026,44(08):53.

备注/Memo

备注/Memo:
收稿日期:2026-04-28 基金项目:广东电网有限责任公司科技项目(031300KC23120022(GDKJXM20231447)) 作者简介:王 骅 (1990-),男,高级工程师,研究方向为电气工程及电力基建等。
更新日期/Last Update: 2026-08-28