| 引用本文: | 潘巍巍,宋彦萍,于达仁.齿轮裂纹程度识别的有序分类算法[J].哈尔滨工业大学学报,2016,48(7):156.DOI:10.11918/j.issn.0367-6234.2016.07.026 |
| PAN Weiwei,SONG Yanping,YU Daren.Gear crack level identification using ordinal classification[J].Journal of Harbin Institute of Technology,2016,48(7):156.DOI:10.11918/j.issn.0367-6234.2016.07.026 |
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| 摘要: |
| 为识别齿轮裂纹的严重程度信息，提出一种基于有序分类的故障严重程度识别方法. 将故障严重程度识别问题视为不同严重程度之间存在序结构，并且部分特征和故障严重程度之间存在单调依赖关系的有序分类问题，从有序分类出发，建立有序分类的故障严重程度识别模型. 研究故障严重程度识别中的特征评价和特征选择问题, 利用排序互信息指标区分原始特征集中的单调特征和非单调特征，提出单调特征和非单调特征混合存在情况下的有序分类特征选择算法. 齿轮裂纹程度识别实验结果表明：提出的有序分类特征选择算法可以降低特征空间维数，能选择出分类能力强的故障特征子集，提高了故障严重程度识别的准确性.
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| 关键词: 有序分类 特征选择 故障诊断 严重程度 齿轮裂纹程度识别 |
| DOI：10.11918/j.issn.0367-6234.2016.07.026 |
| 分类号:O235；TH165 |
| 文献标识码:A |
| 基金项目:福建省自然科学基金(2015J01278) |
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| Gear crack level identification using ordinal classification |
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PAN Weiwei1,2, SONG Yanping2, YU Daren2
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(1.School of Applied Mathematics, Xiamen University of Technology, Xiamen 361024, Fujian, China; 2. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)
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| Abstract: |
| A fault severity level identification method based on ordinal classification is proposed to identify the gear crack levels. The fault level identification is regarded as ordinal classification in which there are ordinal structures between different severity levels and some features have monotonic relationship with the severity levels. The feature evaluation and feature selection for fault severity level identification based on ordinal classification are discussed. Ranking mutual information is utilized to distinguish monotonic features and non-monotonic features of the original feature set, and then a feature selection algorithm is designed for ordinal classification when monotonic features are mixed with non-monotonic features. The experimental results demonstrate that the designed algorithm can select the features with high classification ability for classifying the crack fault severity. A fault severity recognition model is constructed using ordinal classification. The proposed feature selection algorithm can reduce the dimension of feature space, select the features with strong classification ability and improve the accuracy of fault severity level identification.
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| Key words: ordinal classification feature selection fault diagnosis severity level gear crack level identification |
