正交信号校正的自回归模型及其在动态过程监测中的应用

针对采样数据的自相关性, 提出一种基于自回归(AR) 模型的动态过程建模方法. 首先, 利用正交信号校正(OSC) 消除用于AR模型回归的两数据集间的正交不相关信号; 然后, 在处理过的数据上进行偏最小二乘(PLS) 回归建模. 该方法对模型潜隐成分和残差信息同时进行在线监测, 并借鉴贝叶斯推理方法将多个监测指标进行融合, 以易化触发故障警报的决策过程. 最后通过在田纳西-伊斯曼(Tennessee Eastman, TE) 过程上的仿真实验验证了所提出方法的有效性.

Orthogonal signal correction based auto-regression model with application to dynamic process monitoring

With respect to the auto-coorelation existed in sampled data, a dynamic process modeling method based on auto-regression(AR) model is proposed. Firstly, orthogonal signal correction(OSC) is used to eliminate the orthogonal uncorrelated components from the two datasets used for regresing AR model. Then, the partial least square(PLS) is employed to get the AR model on the basis of preprocessed process datasets. The latent components extracted by the regression model as well as the residuals are together monitored online. With the involvement of Bayesian inference, the resulted multiple monitoring statistics are combined into a single probabilistic index, and triggering fault alarm can thus be simplified. The simulation experiment on the Tennessee Eastman(TE) benchmark process demonstrates the effectiveness of the proposed method.