A new multimode process monitoring method based on a hierarchical Dirichlet process-Hidden semi-Markov model with application to the hot steel strip mill process

With the expansion of modern plants and the growth of process complexities, industrial processes generally have multimodes, which are reflected in the multiple distributions and complex correlations among the process data. On the basis of single mode process monitoring methods, research on multimode process monitoring have attracted much attention. At present, most of it has been based on prior process knowledge, including the number of operating modes and the historical dataset for each mode, which are usually unavailable in many cases. This paper proposes a new multimode process monitoring method based on the hierarchical Dirichlet process (HDP) and a hidden semi-Markov model (HSMM). Firstly, HSMM is used to overcome the limitation of state durations in the traditional HMM. Then, HDP is introduced as a prior of infinite spaces solving the problem of missing mode information. Secondly, based on the HDP-HSMM framework, an automatic mode classification and identification strategy, including unknown mode identification, is established. Finally, a global-local monitoring strategy is put forward based on the Mahalanobis distance and the negative log-likelihood probability to give multimode process monitoring has been achieved. The effectiveness of the proposed method is verified on the Tennessee Eastman process and a real hot strip mill process (HSMP).