APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT)

LIANG Chuang; CHEN ChangZheng; LIU Ye; JIA XinYing

doi:10.16579/j.issn.1001.9669.2023.04.002

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APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT)

更新时间：2023-09-25

- APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT)
- Journal of Mechanical Strength Issue 4, Pages: 771-777(2023)
- 作者机构：
  
  1. 沈阳工业大学机械工程学院
  2. 华晨宝马汽车有限公司
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2023.04.002
  CLC：
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LIANG Chuang, CHEN ChangZheng, LIU Ye, et al. APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT). [J]. Journal of Mechanical Strength (4):771-777(2023)
DOI：

LIANG Chuang, CHEN ChangZheng, LIU Ye, et al. APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT). [J]. Journal of Mechanical Strength (4):771-777(2023) DOI： 10.16579/j.issn.1001.9669.2023.04.002.

摘要

针对滚动轴承故障诊断过程中标签样本不足的问题，结合特征选择与二次挖掘，提出了基于半监督拉普拉斯分值(Semi Supervised Laplace Score, SSLS)和核主元分析(Kernel Principal Component Analysis, KPCA)的滚动轴承故障诊断模型。SSLS将半监督思想应用于拉普拉斯分值特征选择方法中，利用少量的有标签样本和大量无标签样本，结合KPCA对故障特征进行二次挖掘。同时，将粒子群优化的支持向量机(Particle Swarm Optimization-based Support Vector Machine, PSO-SVM)算法用于故障分类。最后，将该模型应用于实验数据分析过程。结果表明，该模型在减少样本标记工作量的同时，仍能在滚动轴承故障分类中保持较高的准确率，验证了所建立模型的有效性和工程实用性。

Abstract

Aiming at the problem of insufficient labeled samples in the process of rolling bearing fault diagnosis, a rolling bearing fault diagnosis model based on semi supervised Laplace score（SSLS） and kernel principal component analysis（KPCA） is proposed by combining with the idea of feature selection and secondary mining. SSLS applies the semi supervised idea to the Laplace score feature selection method, uses a small number of labeled samples and a large number of unlabeled samples, and combines KPCA to excavate fault features for a second time. At the same time, particle swarm optimization-based support vector machine（PSO-SVM） algorithm is used for fault classification. Finally, the model is applied to the process of experimental data analysis. The results show that the model can not only reduce the workload of sample marking, but also maintain a high accuracy in rolling bearing fault classification, which verifies the effectiveness and engineering practicability of the model.

关键词

特征选择半监督拉普拉斯分值核主元分析粒子群优化的支持向量机故障诊断

Keywords

Feature selectionSemi supervised Laplace scoreKernel principal component analysisParticle swarm optimization-based support vector machineFault diagnosis

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