[Oil atomic spectrometric feature selection by Parzen window based vague sets theory]

Large quantity and ambiguity of oil atomic spectrometric information greatly affects the applicable efficiency and accuracy in fault diagnosis. A novel method for choosing less and effective spectrometric features is presented. Based on gearbox test bed, we simulated the normal wear state and two typical faults to acquire the lubricant samples. The three wear states are regarded as three vague sets, and spectrometric feature values are vague values on vague sets. Based on similarity between vague values, mean vague sensibility (MVS) is defined to describe the sensitive degree of spectrometric feature to wear state. Besides, the membership degrees of vague sets greatly depend on human experience. The probability density distribution of spectrometric data of three wear states was estimated with Parzen window. Combined with Bayesian formula, the range of vague sets membership was calculated. Experimental results verify that the proposed method is of efficient help in choosing high fault-sensitive features from so many spectrometric features.