基于深度学习的风洞天平测力试验数据异常检测方法研究

Study on deep learning-based anomaly detection method for wind tunnel balance force data

风洞天平测力试验数据异常检测有助于分析试验异常原因、排查设备故障、改进试验方案,为解决目前人工检测时间成本高、效率低的问题,提出一种基于深度学习的异常检测框架。首先针对异常零样本问题,对风洞试验常见的异常类型及规律进行总结;然后为解决不同车次数据维度不同的问题,提出基于统计特征的标准化特征表示方案;最后利用神经网络学习异常特征,完成异常检测。试验结果表明:基于深度学习的异常检测方法对风洞异常数据检测的准确率和检出率分别达到了81.7%和72.6%,能够较好地识别孤立跳点异常和多点异常。

Anomaly detection for wind tunnel balance force data is beneficial to analyze anomaly reasons,improve test schema and troubleshoot equipment problems.To solve the high time cost and low-efficiency problems of the manual detection method,a deep learning-based anomaly detection method is proposed.To solve the problem of no abnormal data,we summarize the most common abnormal types in the wind tunnel test.For the problem that the dimensions of data in different experiments are different,a standardization scheme based on statistical characteristics is proposed.Finally,a deep learning model is utilized to learn abnormal features and detect abnormal data.Experimental results show that our deep learning-based anomaly detection method can achieve 81.7%accuracy and 72.6%recall,and has a good detection performance for isolated jump points and multipoint anomalies.