随机傅里叶特征相异度的故障检测算法

Fault detection approach using random Fourier feature dissimilarity

针对大规模非线性动态过程故障检测问题,提出随机傅里叶特征相异度(RFF–DISSIM)的故障检测方法.首先,利用RFF对原始数据进行映射,获得特征空间中的数据集;然后,在特征空间中应用滑动窗口技术并结合相异度指标对特征空间中的数据集进行过程状态监控.本文方法通过RFF快速捕获数据的非线性结构并结合相异度指标消除样本间自相关性的影响,有效地提高了过程监控性能.通过一个数值例子和连续搅拌釜反应器(CSTR)的仿真实验并与传统的核主元分析、动态主元分析等方法对比分析,仿真结果进一步证明了本文所提方法的有效性.

Aiming at the problem of fault detection in large-scale nonlinear dynamic processes,a fault detection approach based on random Fourier feature dissimilarity(RFF–DISSIM)is proposed.Firstly,the RFF is used to obtain the new dataset by mapping the original dataset into the feature space.Then,the sliding window technology and dissimilarity index are utilized to monitor the status of this process in the feature space.In this paper,the nonlinear structures of dataset are quickly captured by RFF,and the influence of autocorrelation between samples is effectively eliminated by dissimilarity index.The performance in process monitoring can be effectively improved through the combination of these two methods.The efficiency of the proposed strategy is implemented in a numerical case and the continuous stirred tank reactor system(CSTR).The experimental results indicate that the proposed approach has higher detection rate than conventional methods,such as kernel principal component analysis and dynamic principal component analysis.