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首页> 外文期刊>International Journal of Modelling, Identification and Control >Gearbox incipient fault diagnosis using feature sample selection and principal component analysis
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Gearbox incipient fault diagnosis using feature sample selection and principal component analysis

机译：使用特征样本选择和主成分分析的变速箱早期故障诊断

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摘要

Monitoring and diagnostic systems have played an important role in modern industry. Many intelligent or signal processing methods have been successfully applied in the manufacturing process. Inspired by the research of kernel function approximation (KFA), a novel kernel principal component analysis (KPCA) method is proposed in this paper and applied in gearbox incipient fault detection. The proposed KPCA is realised by feature sample selection and principal component analysis, which can also be called FSS-PCA. The key issues studied in this paper are non-linear feature extraction, optimal feature sample selection and diagnostic performance assessment. Firstly, the integral operator Gaussian kernel function is used to realise the non-linear map from the raw input space of gearbox vibration features to a high dimensional space, where appropriate feature samples are selected to construct the feature subspace. Then PC A is used to classify two kinds of gearbox running conditions: normal and tooth crack. The quantity of selected samples is much fewer than that of whole sample sets, which has quickly expedited the computation process. Experiment results indicate the effectiveness of FSS-PCA for gearbox incipient fault diagnosis.
机译：监视和诊断系统在现代工业中发挥了重要作用。许多智能或信号处理方法已成功应用于制造过程中。在核函数近似（KFA）研究的启发下，本文提出了一种新的核主成分分析（KPCA）方法，并将其应用于齿轮箱早期故障检测。提出的KPCA是通过特征样本选择和主成分分析实现的，也可以称为FSS-PCA。本文研究的关键问题是非线性特征提取，最佳特征样本选择和诊断性能评估。首先，使用积分算子高斯核函数来实现从变速箱振动特征原始输入空间到高维空间的非线性映射，在其中选择适当的特征样本来构建特征子空间。然后使用PC A对变速箱的两种运行条件进行分类：正常和齿裂。所选样本的数量远少于整个样本集的数量，这很快加快了计算过程。实验结果表明，FSS-PCA在变速箱早期故障诊断中的有效性。

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