Two-dimensional Bayesian monitoring method for nonlinear multimode processes

Nonlinear and multimode are two common behaviors in modern industrial processes, monitoring research studies have been carried out separately for these two natures in recent years. This paper proposes a two-dimensional Bayesian method for monitoring processes with both nonlinear and multimode characteristics. In this method, the concept of linear subspace is introduced, which can efficiently decompose the nonlinear process into several different linear subspaces. For construction of the linear subspace, a two-step variable selection strategy is proposed. A Bayesian inference and combination strategy is then introduced for result combination of different linear subspaces. Besides, through the direction of the operation mode, an additional Bayesian combination step is performed. As a result, a two-dimensional Bayesian monitoring approach is formulated. Feasibility and efficiency of the method are evaluated by the Tennessee Eastman (TE) process case study.

► A two-dimensional Bayesian method is proposed for process monitoring. ► Nonlinear and multimode behaviors can be addressed simultaneously. ► Nonlinearity of the process is approximated by several linear subspaces. ► Bayesian inference is carried out through two directions of the process data. ► The effectiveness of proposed method is evaluated through the TE benchmark process.