Real-Time Mode Diagnosis for Processes With Multiple Operating Conditions Using Switching Conditional Random Fields

Over the past few decades, hidden Markov models (HMMs) have been widely employed for real-time diagnosis of processes with multiple modes. Recently, the conditional random field (CRF) model has also been applied for process monitoring and proven to perform superior to the HMMs. In this paper, a new framework, termed as a switching CRF (SCRF), is designed to diagnose the modes of processes that have multiple operating conditions. In the proposed framework, multiple linear-chain CRF models are allowed to switch between each other according to a scheduling variable that reflects the real-time operating conditions. The expectation-maximization algorithm is employed for the parameter estimation of SCRF. For validation, two case studies, namely a continuous stirred tank reactor system and an experimental hybrid tank system, are employed. The results demonstrate that the proposed SCRF approach shows superior diagnosis performances to the linear-chain CRF model and the multiple HMMs for the processes with multiple operating conditions.