This paper studies the remaining useful life (RUL) of lubricating oil based on condition monitoring (CM). Firstly, the element composition and content of the lubricating oil in use were quantitatively analyzed by atomic emission spectrometry (AES). Considering the large variety of oil data obtained through AES, the accuracy and efficiency of the RUL prediction model may be reduced. To solve this problem, a comprehensive parameter selection method based on information entropy, correlation analysis, and lubricant deterioration analysis is proposed to screen oil data. Then, based on a support vector machine (SVM), the RUL prediction model of lubricant was established. By comparing the experimental results with the output data of the prediction model, it is shown that the accuracy and efficiency of the SVM prediction model established after parameter screening have been significantly improved.

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In order to promote the accuracy of anomaly detection model under the condition of only a small number of labeled samples and large number of unlabeled samples, abnormal detection of One-class Support Vector Machine(SVM) based on ensemble cooperative Semi-supervised Learning is proposed. A kind of One-class SVM model which bring supervision with a small number of abnormal samples can classify samples with max interval. The semi-supervised learning methods easily suffer from the low accuracy because the mistake labeled sample are chosen as training sample set. Refer to the semi-supervision method of Tri-training, the K-Nearest Neighbour(KNN) and Naive Bayes classifier are used to uses to assist the One-class SVM based on ensemble cooperative Semi-supervised learning method which can classify the large number of unlabeled samples as accurate as possible. The weight is also given after ensemble cooperative Semi-supervised Learning. Then the proposed semi-supervised One-class SVM would be trained with the result and used to classify test samples. The experimental results on UCI dataset show that the proposed algorithm achieves higher classification accuracy with less labeled samples and it improves generalization performance and reduces the labelling cost.

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