Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM
Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM
复制标题
基于改进局部约束线性编码和自适应PSO优化SVM的轴承故障诊断
DOI：
10.1155/2017/7257603
复制
发表时间：
2017-08
影响因子：
--
通讯作者：
Dong Guangming
中科院分区：
文献类型：
--
作者：
Yuan Haodong;Chen Jin;Dong Guangming
提出了一种基于改进的局部约束线性编码（LLC）和自适应PSO优化支持向量机（SVM）的轴承故障诊断方法。在传统的LLC中，每个特征通过使用固定数量的基来编码，而不考虑特征的分布和基的权重。针对这些问题，提出了一种基于自适应加权基的LLC改进算法。首先，利用小波包节点能量提取初步特征。然后，实现了基于类K-SVD算法的字典学习。随后，基于学习的字典的LLC代码可以使用改进的LLC算法求解。最后，利用自适应粒子群优化算法优化的支持向量机对具有鉴别能力的LLC码进行分类，实现轴承故障诊断。在词典学习阶段，还采用了其他方法，如选择样本本身作为词典和-均值法进行比较。实验结果表明，LLC码能有效地提取轴承故障特征，改进的LLC码性能优于传统的LLC码。分类K-SVD学习的字典达到最佳性能。此外，自适应粒子群优化支持向量机可以大大提高分类精度相比，使用默认参数的支持向量机和线性支持向量机。
A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO) is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and -means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.
登录
查看更多内容
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
--
发表时间：
2007
期刊：
--
影响因子：
--
作者：
Roger B. Grosse;Helen Kwong
通讯作者：
Roger B. Grosse;Helen Kwong
影响因子：
1.6
作者：
Li, Hui;Zhang, Yuping;Zheng, Haiqi
通讯作者：
Zheng, Haiqi
影响因子：
--
作者：
Jianwei Cui;Mengxiao Shan;Ruqiang Yan;Ya-hui Wu
通讯作者：
Jianwei Cui;Mengxiao Shan;Ruqiang Yan;Ya-hui Wu