A novel online discriminative dynamic feature analysis (ODDFA) algorithm is formulated and then employed for dynamic process monitoring. Different from traditional multivariate analytical algorithms which derive representative signature inherited in a dataset given from the normal operating condition, the proposed ODDFA algorithm only seeks for a pair of projecting directions, which could be discriminative to the deviation between the online sampled data and the normal operating dataset. From the standpoint of its formulation, the ODDFA algorithm is activated online with the availability of stacking online time-serial samples into a matrix form, and the latent feature resulted from a two-dimensional projection could be extremely discriminative to the inconsistency inherited in the online time-serial samples. Therefore, the utilization of the ODDFA algorithm in dynamic statistical process monitoring is always a preferable choice in contrast to other counterparts, as demonstrated through comparisons in monitoring two classical dynamic processes.

提出了一种新颖的在线判别动态特征分析（ODDFA）算法，然后将其用于动态过程监控。与传统的多元分析算法不同，该算法从正常操作条件得出的数据集中继承了代表性特征，所提出的ODDFA算法仅寻找一对投影方向，这可以区别在线采样数据和正常操作之间的偏差。数据集。从其制定的角度来看，ODDFA算法是在线激活的，并且可以将在线时间序列样本堆叠为矩阵形式，而二维投影所产生的潜在特征可能会极大地区别在线中继承的不一致性。时间序列样本。所以，