基于单分类算法OSVM船用燃气轮机状态评估

针对船舶综合监控系统中存储有海量设备正常运行时的数据没有得到充分利用，此外设备退化时的故障数据难以获取，无法训练传统的多分类退化检测模型，提出利用单分类算法OSVM来建立模型,从而实现退化检测，在该过程中只需用正常样本数据来训练模型，并在一个经过实船数据验证过的模拟器产生的数据集上进行了实验。结果显示，只需要400个正常样本就可训练出准确的退化检测模型，该模型在精确度，召回率，特异性，正确率，AUC五个指标都有很好表现，此外，该退化检测模型有很好的扩展性，也可被用于其他机械设备的状态评估中。

Condition Evaluation of Marine Gas Turbine Based on Single Classification Algorithm OSVM

In view of the large amount of data stored in the integrated monitoring and control system of ships in normal operation is not fully utilized, in addition, it is difficult to obtain the fault data when the equipment is decayed, and it is unable to train the traditional multi classification decay detection model, a one-class algorithm OSVM is proposed to build the model, so as to realize the decay detection. In this process, only normal sample data is used to train the model, the experiment is carried out on a data set generated by a simulator which has been verified by real ship data. The results show that only 400 normal samples are needed to train an accurate decay detection model. The model has good performance in Precision, recall, Specificity, accuracy and AUC. In addition, the decay detection model has good expansibility and can also be used in the condition evaluation of other mechanical equipment.