Abstract

A fault detection model based on One-Class Support Vector Machine was established to solve the large difference in sample size between the normal data and fault data of refrigerated containers. During the model training process, only the normal samples were needed to be learned, and an accurate identification of abnormal was 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 refrigerated containers based on One-Class Support Vector Machine was established, and the test results show that the model has a high recognition rate against abnormal of 97.4% and zero false alarm rate.

Keywords:

Fault detection, one-class svm, parameter optimization, refrigerated container,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.