Abstract
A fault detection model based on One-Class Support Vector Machine (OC-SVM), a new SVM method, is established to solve the large difference in sample size between the normal data and fault data of reefer containers. During the model training process, only the normal samples are needed to be learned, and an accurate identification of abnormal can be achieved, which may solve the problem of lack of fault samples in practice. By comparison experiments between different kernel functions and kernel parameter optimization, a fault detection model of reefer containers based on OC-SVM is established, and the test results show that the model has a high recognition rate against abnormal and low false alarm rate.
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