Multimode Process Monitoring Based on Common and Unique Subspace Decomposition

Multimode process monitoring is critical in practical industrial processes, as it directly affects the quality and safety of product. The disadvantages of existing multimode process monitoring methods are as follows. 1) The operation mode isolation is not accurate since the interference from the common information in the original data space. 2) The coordinated operation mode isolation strategy is not considered in the traditional method. 3) The local spatial properties are ignored in multisubspace methods. In this article, a multimode process monitoring algorithm called common and unique subspace decomposition (CUSD) is proposed. Advantages of the proposed method are as follows. 1) The unique mode subspace containing the unique information for each mode and the common mode subspace containing the common information between different modes are decomposed in order to eliminate the influence from the common information. 2) The local and global spatial properties are exhaustively extracted in each subspace by the fusion of the principal components and the manifold information. 3) A coordinated Bayesian inference strategy based on the extracted properties in the decomposed unique mode subspace is proposed for operation mode isolation purpose. Simulations on three different processes have validated that there is an approximately 20% improvement of average operation mode isolation accuracy in the unique mode subspace of CUSD than in the original data space.