Linear and exponential fault-assistant feature extraction methods for process monitoring

Abstract Feature extraction is very important for dimension reduction and facilitation of the final monitoring accuracy. In practice, both the normal data and fault data can be easily collected and stored by the advanced sensing and computer technologies. The measured fault data can also be employed to improve monitoring performance, which was not yet fully researched in the past. This work proposes novel fault-assistant monitoring models of both linear and exponential versions. In these two versions of models, the measured normal and fault data are used together to extract the common features, and then special features can be further extracted based on the normal residuals. Then the process data can be divided into common, special, and residual subspaces. The monitoring statistics can be correspondingly defined for fault detection. The numerical case and two industrial cases are applied to show the efficiency and feasibility of the proposed method.