Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures

Quality- or output-related fault detection has attracted much attention in recent years. Several approaches have been developed to solve this issue based on postprocessing schemes. However, further studies find that these methods gradually lose their functions when amplitudes of quality-unrelated faults increase; in addition, they still consume a relatively large amount of calculation load in practice. In this brief, we propose a new structure of preprocessing-modeling-postprocessing, within which modified orthogonal projections to latent structures (MOPLS) method is developed. Compared with the previous approaches, the new method significantly improves the performance of quality-related fault detection. In addition, it reduces the number of required latent variables, thus it has a quite lower computational load than the previous ones. A numerical example and the Tennessee Eastman process are used to verify the effectiveness of the proposed approach.