Conditional random field for monitoring multimode processes with stochastic perturbations

Multimode process monitoring techniques have been successfully applied in various industrial sys-tems. However, the transition processes between different modes have not been well handled. This paper considers the monitoring problem of multimode processes with stochastic perturbations, in which fre-quent mode switches caused by stochastic perturbations are taken into account. Contrary to the widely used assumption of instant mode switches, the dynamics during the transition process caused by perturbations are considered. To cope with transient characteristics, a mode identification algorithm based on the conditional random field is proposed. Compared with traditional multimode process monitoring methods, the assumption of independence between adjacent observations is not required, which improves the mode identification accuracy. In addition, an index called log conditional probability ratio is pro-posed, and its Mahalanobis distance is used for fault detection. The fault detectability of the proposed method is analyzed, with a sufficient condition and a necessary condition of detecting a sensor fault derived, respectively. The effectiveness of mode identification and fault detection is demonstrated by a numerical example and a continuous stirred tank reactor simulation. (c) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.