Aiming at the processes with dynamic and multimode characteristics, a fault detection strategy based on Local Neighbor Standardization (LNS) and Dynamic Principal Component Analysis (DPCA) was proposed. First, the K nearest neighbors set of each sample in training data set was found, then the mean and standard deviation of each variable were calculated. Next, the above mean and standard deviation were applied to standardize the current samples. At last, the traditional DPCA was applied in the new data set to determine the control limits of T ² and SPE statistics respectively for fault detection. LNS can eliminate the multimode characteristic of a process and make the new data set follow a multivariate Gaussian distribution; meanwhile, the feature of a outlier deviating from normal trajectory can also be maintained. LNS-DPCA can reduce the impact of multimode structure and improve the detectability of fault in processes with dynamic property. The efficiency of the proposed strategy was implemented in a simulated case and the penicillin fermentation process. The experimental results indicate that the proposed method outperforms the Principal Component Analysis (PCA), DPCA and Fault Detection based on K Nearest Neighbors (FD- KNN).

Aiming at the nonlinear and multi-mode features of batch processes, a fault detection method for batch process based on k Nearest Neighbors ( kNN) rule in Discriminated kernel Principle Component space, namely Dis-kPC kNN, was proposed. Firstly, in kernel Principal Component Analysis (kPCA), according to discriminating category labels, the kernel window width parameter was selected between within-class width and between-class width, thus the kernel matrix can effectively extract data correlation features and keep accurate category information. Then kNN rule was used to replace the conventional T ² statistical method in the kernel principal component space, which can deal with fault detection of process with nonlinear and multi-mode features. Finally, the proposed method was validated in the numerical simulation and the semiconductor etching process. The experimental results show that the kNN rule in discriminated kernel principle component space can effectively deal with the nonlinear and multi-mode conditions, improve the computational efficiency and reduce memory consumption, in addition, the fault detection rate is significantly better than the comparative methods.