A novel approach to process operating mode diagnosis using conditional random fields in the presence of missing data

Diagnosis of process operating modes is an important aspect of process monitoring. Due to its ability to model process transitions, the Hidden Markov Model (HMM) is widely used as a tool for operating mode diagnosis. However, it suffers from certain drawbacks due to its inherent assumptions. Hence, to address these issues and improve the operating mode diagnosis performance, we introduce the Conditional Random Field (CRF), which is a discriminiative probabilistic model based approach. Further, to deal with the missing measurement problem that commonly occurs in industrial datasets, a marginalized CRF framework is proposed in this paper and the related inference algorithms are developed under this newly designed framework. Validation studies performed on a simulated continuous stirred tank reactor (CSTR) system and an experimental hybrid tank system demonstrate that the proposed CRF based algorithms have superior performances compared to the existing approaches.