基于伪标签半监督核局部Fisher判别分析轴承故障诊断

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (17) : 1-9.

论文

陶新民,任超,徐朗,何庆,刘锐,邹俊荣

作者信息 +

Bearing fault diagnosis based on semi-supervised kernel local Fisher discriminant analysis using pseudo labels

TAO Xinmin, REN Chao, XU Lang, HE Qing, LIU Rui, ZOU Junrong

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文章历史 +

摘要

针对轴承故障诊断应用中多特征融合导致的维度高、相关性强、信息冗余等问题，提出一种基于伪标签半监督核局部Fisher判别分析(Semi-supervised Kernel Local Fisher Discriminant Analysis, SS-KLFDA)轴承故障诊断方法。为了能利用大量无标签样本提高算法判别性能，该方法首先采用密度峰值聚类算法对样本进行聚类分析得到伪标签，然后通过增加规范化项到局部FDA算法的类内散度矩阵和类间散度矩阵中，以此保持无标签样本的聚类结构一致性，最后通过局部FDA算法来保持有标签样本类间散度最大化和类内散度最小化并求解最佳投影向量。为了能适应非线性数据降维，本文进一步给出了基于核的伪标签半监督局部Fisher判别算法。实验部分通过同其他流行降维算法在不同维度、不同特征集合以及不同分类算法条件下进行轴承故障诊断性能对比，结果表明本文提出的基于伪标签半监督核局部Fisher判别分析方法的分类精度明显优于其他降维算法，投影后的系数向量具有更好的区分能力，使得故障

Abstract

In order to solve the problems of high-dimensionality, strong-relevance, and information redundancy due to the results of information fusion in bearing fault diagnosis domains, a novel bearing fault diagnosis approach based on semi-supervised kernel local fisher discriminant analysis (SS-KLFDA) using pseudo labels is presented in this paper. In the proposed approach, to sufficiently utilize a large amount of unlabeled samples to improve the discriminantperformance, we firstly adopt density peak clustering to acquire the pseudo cluster labels for unlabeled samples and then add regularization terms into both the local within-class and local between-class scatter matrices to preserve the local cluster structure for the unlabeled data. Finally, the added regularization terms are incorporated into the local within-class and local between-class scatter matrices concerning labeled samples to formulate the objective function and the final projection matrix are obtained by maximizing the objective function. In addition, in order to accommodate for nonlinearly separate problems, we further give a kernelized version of SS-KLFDA using pseudo labels in this study. In the experiment, the proposed approach is compared with other existing dimensionality reduction methods under different reduced dimensions, different feature sets, and different combined classifiers scenarios. The results show that the proposed approach could greatly increase the classification accuracies of all combined classifiers and possesses the best discrimination capacity among the dimension-reduced features, which can effectively improve the fault diagnosis performance.

导出引用

陶新民,任超,徐朗,何庆,刘锐,邹俊荣. 基于伪标签半监督核局部Fisher判别分析轴承故障诊断[J]. 振动与冲击, 2020, 39(17): 1-9

TAO Xinmin, REN Chao, XU Lang, HE Qing, LIU Rui, ZOU Junrong. Bearing fault diagnosis based on semi-supervised kernel local Fisher discriminant analysis using pseudo labels[J]. Journal of Vibration and Shock, 2020, 39(17): 1-9

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