Abstract: One-class support vector machine (1-SVM) has the ability to find outliers from a dataset without any class information, but it has been rarely applied to classification for it’s low classification precision resulted from the algorithm limits. By modifying the decision-function of 1-SVM, a decision-improved 1-SVM (1-DISVM) is presented to adjust the classification precision. Based on it, multi-classes classification models trained by single-class samples are designed. The 1-DISVM models are applied to a helicopter’s gearbox fault-identification. The experimental results show that this method can get rid of the influence of wrong samples to achieve precise classification with small fault samples, and this method has the merits of unsupervised learning, precise classification, easy to expand and low cost.

Key words: Fault identification, Support vector machine, Unsupervised learning Classification