基于核主成分分析的铁谱磨粒特征提取方法研究

KPCA-based Technique for Debris Feature Extraction

针对铁谱分析的磨粒识别过程中存在原始磨粒特征描述指标参数多、非线性突出的问题,提出基于核主成分分析的磨粒特征提取方法,介绍该方法的原理与算法。结合某柴油发动机故障检测与分析系统中铁谱磨粒自动识别的应用实例,并与传统主成份分析方法进行对比分析,结果表明该方法在进行样本非线性特征参数指标综合以及特征维数压缩方面具有可行性和有效性。

To deal with the problem of Debris＇ feature extraction with the characteristic of large scale number in description parameter and nonlinear relationship among these parameters, a KPCA-based method is presented. Based on the detailed introduction of algorithm, a real case was studied for the purpose of constructing a debris automatic recognition sub-system which can be served for diesel engine fault detection and analysis system. The results of experimental research and a comparison with linear PCA demonstrate that KPCA-based approach is feasible and valid for synthesizing nonlinear characteristic parameter and reducing feature dimension.