摘要
在轴承故障诊断过程中,存在缺乏足量故障样本、变工况下信号分布差异等问题。虽然基于机器学习和深度学习方法的智能故障诊断方法的运用取得了许多成果,但该方法在应用过程中仍面临一些挑战,阻碍了智能故障诊断方法在实际工业场景下的应用。为此,提出了一种基于改进联合分布适应的轴承智能故障诊断方法(BIFD-IJDA)。首先,利用小波包变换对振动信号进行了分解与重构,再计算了重构信号的统计参数,构成了原始特征集;然后,设计了基于特征重要度与KL散度的迁移特征选取方法,对各统计参数特征进行了量化评估;采用了改进联合分布适应方法,对源域和目标域特征集进行了分布适应处理,降低了域间分布差异;最后,利用源域特征样本训练的故障诊断模型预测了目标域样本故障类别,采用美国凯斯西储大学实验台和机械故障模拟(MFS)实验台的轴承故障数据,开展了不同工况下的故障诊断实验。实验结果表明:该故障诊断方法在2种轴承故障数据下取得的最大故障诊断准确率分别为100%和96.29%,明显优于其他对比模型。研究结果表明:该故障诊断方法具有应用于实际工业场景的潜力。
In the process of bearing fault diagnosis,there are some problems,such as lack of sufficient fault samples and different signal distribution under off-design conditions.Although the application of intelligent fault diagnosis method based on machine learning and deep learning method has made many achievements,this method still faces some challenges in the application process,which hinders the application of intelligent fault diagnosis method in actual industrial scenes.Therefore,an bearing intelligent fault diagnosis method based on improved joint distribution adaptation(BIFD-IJDA)was proposed.Firstly,the vibration signal was decomposed and reconstructed by wavelet packet transform,and statistical parameters of construction signals were calculated to build original features set.Then,a transferable feature selection method based on feature importance and Kullback-Leibler(KL)divergence was designed to quantitatively evaluate each statistical feature.Next,the improved joint distribution adaptation method was used to adapt the distribution of the feature sets in the source and target domains to reduce the distribution differences between domains.Finally,the fault diagnosis model trained by the source domain feature samples was employed to predict the fault category of the target domain samples,and the fault diagnosis experiments under different working conditions were carried out using the bearing fault data from Case Western Reserve University test rig and mechanical fault simulation(MFS)test rig.The experimental results show that the maximum fault diagnosis accuracies of the fault diagnosis method under the two bearing fault data are respectively 100%and 96.29%,which is significantly better than other comparison models.The results show that it has the potential to be applied in actual industrial scenarios.
作者
潘晓博
葛鲲鹏
钱孟浩
赵衍
董飞
PAN Xiaobo;GE Kunpeng;QIAN Menghao;ZHAO Yan;DONG Fei(School of Information Engineering,Xuzhou University of Technology,Xuzhou 221008,China;School of Electronic Engineering,Yangzhou Polytechnic College,Yangzhou 225127,China;School of Internet,Anhui University,Hefei 230039,China)
出处
《机电工程》
CAS
北大核心
2023年第9期1354-1362,共9页
Journal of Mechanical & Electrical Engineering
基金
国家级大学生创新创业训练计划项目(202210357121)
江苏省建设系统科技项目(2018ZD077)
安徽省高校优秀科研创新团队项目(2022AH010005)。
关键词
轴承智能故障诊断变工况
故障样本数量不足
改进联合分布适应
迁移特征
邻域保持嵌入
迁移成分分析
bearing intelligent fault diagnosis variable working condition
insufficient number of fault samples
improved joint distribution adaptation
transferable feature
neighborhood preserving embedding(NPE)
transfer component analysis(TCA)
