摘要
针对航空齿轮箱故障诊断中采集到的振动信号包含复杂噪声干扰和冗余成分的问题,提出了基于自适应变分模态分解的齿轮箱故障诊断方法。利用综合评价指标完成变分模态分解(VMD)中分解层数K值的自适应选取,通过设置相关系数和能量熵的阈值,筛选同时大于阈值的分量作为包含主要能量且与原信号更加相似的分量进行重构,实现信号的降噪和特征增强。利用结合精细复合多尺度散布熵(RCMDE)对降噪后的信号进行特征提取,充分提取反映振动信号不同时间尺度复杂程度的非线性特征组成特征向量。使用粒子群算法(PSO)优化的核极限学习机(KELM)对所提取的特征进行识别。通过实验验证,该模型10次测试的平均准确率可达95.04%。与其他特征提取和模式识别方法进行对比,所提方法具有更高的诊断准确率,为航空齿轮箱的故障诊断提供了新的方法。
Aiming at the problem that the vibration signals collected in the fault diagnosis of aviation gearboxes contain complex noise interference and redundant components,a gearbox fault diagnosis method based on adaptive variational modal decomposition(AVMD)is proposed.Firstly,the adaptive selection of the K value in the variational modal decomposition(VMD)is accomplished using the comprehensive evaluation index.By setting the thresholds of correlation coefficient and energy entropy,the components that are simultaneously larger than the thresholds are filtered to be reconstructed as the components that contain the main energy and are more similar to the original signal.In this way,noise reduction and feature enhancement of the signal are realized.Secondly,the RCMDE is utilized to extract features from the noise-canceled signal.The nonlinear features reflecting the complexity of the vibration signal at different time scales are fully extracted to form the feature vector.Finally,the extracted features are identified using Kernel Extreme Learning Machine(KELM)optimized by Particle Swarm Algorithm(PSO).The model is experimentally validated to have an average accuracy of 95.04%over ten tests.And compared with other feature extraction and pattern recognition methods,the proposed method has higher diagnostic accuracy.It provides a new method for the fault diagnosis of aviation gearboxes.
作者
谢锋云
汪淦
赏鉴栋
樊秋阳
朱海燕
XIE Fengyun;WANG Gan;SHANG Jiandong;FAN Qiuyang;ZHU Haiyan(School of Mechanical Electronical and Vehicle Engineering,East China Jiaotong University,Nanchang 330013,China;China Life-Cycle Technology Innovation Center of Intelligent Transportation Equipment,East China Jiaotong University,Nanchang 330013,China)
出处
《推进技术》
EI
CAS
CSCD
北大核心
2024年第9期218-227,共10页
Journal of Propulsion Technology
基金
国家自然科学基金(52265068)
江西省自然科学基金(20224BAB204050,20224BAB204040)
载运工具与装备教育部重点实验室项目(KLCEZ2022-02)
江西省教育厅项目(GJJ2200627)。
关键词
航空齿轮箱
故障诊断
信号降噪
自适应变分模态分解
粒子群算法
核极限学习机
Aviation gearbox
Fault diagnosis
Signal denoising
Adaptive variational mode decomposition
Particle swarm optimization
Kernel extreme learning machine
