Switching Conditional Random Field Approach To Process Operating Mode Diagnosis For Multi-Modal Processes

Accurate diagnosis of process modes for industrial processes is critical to safe and reliable operation of processes. The hidden Markov models (HMMs) have been widely employed to solve the real-time process mode diagnosis problems for multi-modal processes. However, restricted by the inherent conditional independence assumptions, the process mode diagnosis performance of HMMs tends to be degraded as these assumptions can be easily broken in reality. Alternatively, the conditional random field (CRF) model has been proposed in the context of process monitoring and proven to outperform the HMMs. In this work, we extend the CRF framework to the mode diagnosis of the processes that have multiple operating conditions, by designing a new framework, termed as, a switching CRF (SCRF). In the proposed framework, multiple linearchain CRF models are proposed which have the capability to switch between each other in accordance with a scheduling variable that is indicative of the operating conditions. The expectation-maximization algorithm is employed for parameter estimation. To validate the performance, a numerical example is employed. The results demonstrate that the proposed SCRF approach shows superior diagnosis performance to the linearchain CRF based method.