改进的四子空间方法及其在电厂设备状态监测中的应用

摘要

As a usual status monitoring method, four subspace method is only applicable under the condition that process data follows Gaussian process. However, most of industrial data are non-Gaussian, which makes the application of the four subspace method rather limited. This paper uses a kernel density estimation method to improve the traditional four subspace method, and designs a four subspace status monitoring method based on the kernel density estimation, which is suitable for general distributions. Finally, using the real data of high temperature superheater in some electric power plant, the empirical results show that the improved four subspace method is more universal, and it can significantly improve the status monitoring effect.%四子空间方法作为常用的状态监测方法,需要假设过程变量服从高斯分布,实际中大部分的工业数据并不服从高斯分布,这使得四子空间方法的应用范围非常有限.基于此,本文使用核密度估计方法来改进传统的四子空间方法,得到了适用于一般分布下的基于核密度估计的四子空间状态监测方法.最后,利用电厂高温过热器的实际数据进行检验.结果表明改进的四子空间方法更为普适,状态监测效果也有很大的提高.