Conditional random field for monitoring multimode processes with stochastic perturbations. Issue 12 (August 2020)

• Record Type: Journal Article
• Authors: Zhang, Hanwen; Shang, Jun; Yang, Chunjie; Sun, Youxian
• Is Part Of: Journal of the Franklin Institute. Volume 357:Issue 12(2020)
• Page Start: 8229
• Page End: 8251
• Publication Date: 2020-08
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2020.05.039
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: Multimode process monitoring techniques have been successfully applied in various industrial systems. 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 frequent 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 proposed, 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.