论文标题
基于正交局部性保存投影的过程监视，并具有最大似然估计
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
论文作者
论文摘要
通过整合两种强大的密度降低和固有维度估计方法，引入了一种新的数据驱动方法，称为OLPP-ME（正交局部性保留投影最大的可能性估计），以进行过程监视。 OLPP用于降低维度，该维度可提供比保留投影更好的地方保留能力更好的地方。然后，采用MLE来估计OLPP的内在维度。在拟议的OLPP-MLE中，定义了两种用于故障检测的新静态措施$ t _ {\ scriptScriptStryle {olpp}}}^2 $和$ {\ rm spe} _ {\ scriptScriptScriptScriptstyle {olpp}} $。为了降低算法的复杂性和忽略数据分布，使用核密度估计来计算阈值以进行故障诊断。三个案例研究证明了所提出方法的有效性。
By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection $T_{\scriptscriptstyle {OLPP}}^2$ and ${\rm SPE}_{\scriptscriptstyle {OLPP}}$ are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.
