A Novel Fault Assessment Method Integrating Global and Local Information for Industrial Processes

Feature extraction methods in the industrial process may affect the performance of process monitoring and fault assessment. In order to reduce the influence of model preference, a novel fault assessment method integrating global and local structure information is proposed by using the strategy of parallel extraction and weighted combination. First, kernel sparse locally linear embedding and kernel principal component analysis models are constructed to handle nonlinear system states and describe the local and global information in parallel. Second, the fault assessment indicators are obtained through support vector data description models. Then, a comprehensive fault assessment index is established by the weighted fusion method, which can keep the global and local neighbor information of the original sample space, and thus more feature information is included. Finally, the feasibility and effectiveness of the proposed assessment method are illustrated by the application in the simulation process of Tennessee Eastman Process, and the results show that the fusion of two basic models can bring a model with better generalization performance than a single model.