基于Parzen窗的油液原子光谱数据半监督FCM聚类研究

提出了一种基于Parzen窗的半监督模糊C-均值(Semi-supervised Fuzzy C-Means Based on Parzen window,PSFCM)聚类算法。根据训练样本确定出模糊C-均值(Fuzzy C-Means,FCM)的初始聚类中心;利用Parzen窗法计算出测试样本对各类状态的隶属度后,重新定义了隶属度迭代公式。通过齿轮箱磨损实验台模拟了齿轮箱的2种典型磨损故障并采集了油样。选取实验油样光谱分析数据中代表性元素Fe,Si,B的浓度值作为分析数据集的3维特征量,分别进行了FCM聚类和PSFCM聚类分析。聚类结果为:FCM聚类的正确率为48.9%,而融入了监督信息的PSFCM聚类的正确率为97.4%。实验说明,将PSFCM算法引入到油液原子光谱分析,降低了对人为经验和大量故障数据的依赖,提高了齿轮箱磨损故障诊断的准确度。

Research on oil atomic spectrometric data semi-supervised fuzzy C-means clustering based on Parzen window

A Parzen window based semi-supervised fuzzy c-means (PSFCM) clustering algorithm was presented. The initial clustering centers of fuzzy c-means (FCM) were determined with training samples. The membership iteration of FCM was redefined after the membership degrees of testing samples relatively to each state were calculated using Parzen window. Two typical faults of gear box were simulated through the gear box bed in order to acquire the lubricant samples. Concentration of Fe, Si and B, which were the representative elements, was selected as the three-dimensional feature vectors to be analyzed with FCM and PSFCM clustering methods. The clustering results were that the correct ratio of FCM was 48.9%, while that of PSFCM was 97.4% because of integrating with supervised information. Experimental results also indicated that it can reduce the dependence of the experience and lots of faults data to introduce PSFCM into oil atomic spectrometric analysis. It was of great help in improving the wear faults diagnosis ratio.