Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machi...Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.展开更多
时间序列早期分类(ETSC)有两个矛盾的目标:早期性和准确率。分类早期性的实现,总是以牺牲它的准确率为代价。现有基于优化的多变量时间序列(MTS)早期分类方法,虽然在成本函数中考虑了错误分类成本和延迟决策成本,却忽视了MTS数据集样本...时间序列早期分类(ETSC)有两个矛盾的目标:早期性和准确率。分类早期性的实现,总是以牺牲它的准确率为代价。现有基于优化的多变量时间序列(MTS)早期分类方法,虽然在成本函数中考虑了错误分类成本和延迟决策成本,却忽视了MTS数据集样本之间的局部结构对分类性能的影响。针对这个问题,提出一种基于正交局部保持映射(OLPP)和成本优化的MTS早期分类模型(OLPPMOAE)。首先,使用OLPP将MTS样本前缀映射到低维空间,保持原数据集的局部结构;其次,在低维空间训练一组高斯过程(GP)分类器,生成训练集每个时刻的类概率;最后,使用粒子群优化(PSO)算法从这些类概率中学习停止规则中的最优参数。在6个MTS数据集上的实验结果表明,在早期性基本持平的情况下,OLPPMOAE的准确率显著高于基于成本的R1_C_(lr)(stopping Rule and Cost function with regularization term l_(1)and l_(2))模型,平均准确率能够提升11.33%~15.35%,调和均值(HM)能够提升4.71%~9.01%。因此,所提模型能够以较高的准确率尽早地分类MTS。展开更多
基金supported by Fundamental Research Funds for the Central Universities of China (Grant No. CDJZR10118801)
文摘Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.
文摘时间序列早期分类(ETSC)有两个矛盾的目标:早期性和准确率。分类早期性的实现,总是以牺牲它的准确率为代价。现有基于优化的多变量时间序列(MTS)早期分类方法,虽然在成本函数中考虑了错误分类成本和延迟决策成本,却忽视了MTS数据集样本之间的局部结构对分类性能的影响。针对这个问题,提出一种基于正交局部保持映射(OLPP)和成本优化的MTS早期分类模型(OLPPMOAE)。首先,使用OLPP将MTS样本前缀映射到低维空间,保持原数据集的局部结构;其次,在低维空间训练一组高斯过程(GP)分类器,生成训练集每个时刻的类概率;最后,使用粒子群优化(PSO)算法从这些类概率中学习停止规则中的最优参数。在6个MTS数据集上的实验结果表明,在早期性基本持平的情况下,OLPPMOAE的准确率显著高于基于成本的R1_C_(lr)(stopping Rule and Cost function with regularization term l_(1)and l_(2))模型,平均准确率能够提升11.33%~15.35%,调和均值(HM)能够提升4.71%~9.01%。因此,所提模型能够以较高的准确率尽早地分类MTS。
