Abstract: An improved Principal Component Analysis (PCA) method was proposed with the synchronous multi-dimensional data stream anomaly analysis techniques. In this method, the problem of the original data stream variation tendency was mapped to the eigenvector space, and the steady-state eigenvector was solved, then the abnormal changes of the synchronous multi-dimensional data stream could be diagnosed by the relationship between the instantaneous eigenvector and the steady-state eigenvector. This method was applied to the abnormality diagnosis of the tunnel strain monitoring data stream, and the real-time monitoring and warning system for the tunnel strain was realized by using VC++. The experimental results show that the proposed method can reflect the changes of the aperiodic variables timely and realize the anomaly monitoring and early warning for multi-dimensional data stream effectively.

Key words: Principal Component Analysis (PCA), multi-dimensional data stream, abnormality diagnosis, steady-state eigenvector, real-time warning system

摘要： 针对同步多维数据流异常分析技术提出了一种改进的主元分析(PCA)方法。将原始数据流空间的变化趋势映射到特征向量空间内，求解稳态特征向量，以瞬时特征向量与稳态特征向量之间的关系作为判据来对同步多维数据流进行异常变化诊断。将该方法应用于某隧道应变监测数据的异常诊断中，并利用VC++实现了隧道应变实时监测预警系统。实验结果表明，使用该方法能够实时反映非周期性监控变量的变化情况，较好地实现同步多维数据流的异常诊断。

关键词: 主元分析, 多维数据流, 异常诊断, 稳态特征向量, 实时预警系统