基于马氏距离局部离群因子方法的复杂化工过程故障检测

为了满足实际的生产需要,复杂化工过程往往包含多个运行模态。同时过程的复杂性使得同一模态下的数据分布是一种高斯分布和非高斯分布混合存在的不确定情况。数据的多模态分布特性以及同一模态下数据分布的不确定性使得传统多元统计监控(MSPM)方法很难给出令人满意的结果。针对这一问题,本文提出一种新的马氏距离局部离群因子(MDLOF)方法进行故障检测。通过利用马氏距离挖掘变量局部结构中包含的有用信息,并对样本的邻域密度加以考虑,形成对数据分布具有鲁棒性的基于密度的监控指标。最后通过数值仿真例子及Tennessee Eastman过程验证其有效性。

Fault detection of complex chemical processes using Mahalanobis distance-based local outlier factor

Complex chemical processes often have multiple operating modes to meet the changes of production conditions.Meanwhile, process complexity makes the within-mode data usually follow an uncertain combination of Gaussian and non-Gaussian distributions.The multimode characteristics and the uncertainty of data distribution within one single mode make the conventional multivariate statistical process monitoring(MSPM)methods unsuitable for fault detection.In this paper, a novel Mahalanobis distance-based local outlier factor(MDLOF)method was proposed.The local structure of variables was taken into account by employing Mahalanobis distances, and the local density of the surrounding neighborhoods was also considered.A density-based monitoring statistics which was robust regardless of data distribution was obtained by this way.Finally, the validity and effectiveness of MDLOF were illustrated through a numerical example and the Tennessee Eastman process.