基于支持向量机的民航发动机故障检测研究

将支持向量机用于民航PW4056发动机故障检测研究。首先,对3个发动机巡航数据偏差进行研究,分析得到故障检测应采用短期偏差;其次,由于模型参数对检测准确率影响很大,文中采用验证法进行模型参数选择,并分析了模型参数对检测准确率的影响;最后,对检测模型的输出进行了分析,并定义了异常指数来衡量发动机故障严重程度,其中检测模型的训练和验证采用了发动机真实运行数据。研究表明,该发动机故障检测模型有效可行,准确率达到90%,但要获得更高的检测准确率,还需进一步提高数据质量。

Civil Aviation Engine Fault Detection Using Support Vector Machines

Support vector machines (SVM) can be used for novelty detection. This paper applies SVM to detect the faults of PW4056 engine. First, three deviations of engine cruise data are analyzed, then short term deviation is chosen as the input of the detection model. Second, model selection is conducted using validation method, and the effect of model parameters on detection accuracy is analyzed. Finally, the result of detection model is studied and novelty index is defined to measure the severity of fault. The training and validation of detection model use engine run data, and the study shows that this model is feasible and the accuracy reaches 90%. However, the quality of data must be improved if the better result is wanted.