Statistical process monitoring based on a multi-manifold projection algorithm

• A novel linear dimensionality reduction technique called MMP is proposed.
• The neighborhood information is embedded in both local and global phases.
• The superiority of MMP is investigated through two case studies.
• MMP-based monitoring scheme outperforms other regular monitoring methods.

Considering that the global and local structures of process data would probably be changed in some abnormal states, a multi-manifold projection (MMP) algorithm for process monitoring and fault diagnosis is proposed under the graph embedded learning framework. To exploit the underlying geometrical structure that contains both global and local information of sampled data, the global graph and local graph are designed to characterize the global and local structures, respectively. A unified optimization framework, i.e. global graph maximum and local graph minimum, is then constructed to extract meaningful low-dimensional representations for high-dimensional process data. In the proposed MMP, the neighborhood embedding is used in both global and local graphs and the extracted features are faithful representations of the original data. The feasibility and validity of the MMP-based process monitoring scheme are investigated through two case studies: a simple simulation process and the Tennessee Eastman process. The experimental results demonstrate that the whole performance of MMP is better than those of some traditional preserving global or local or global and local feature methods.