Neighborhood Variational Bayesian Multivariate Analysis for Distributed Process Monitoring With Missing Data

Conventional methods for distributed monitoring commonly assume that complete process measurements are available. However, the problem of missing data is often encountered in the monitoring of large-scale multiunit processes. This paper proposes an approach based on a neighborhood variational Bayesian principal component analysis (NVBPCA) and canonical correlation analysis (CCA) for the efficient distributed monitoring of multiunit processes in the presence of missing data. Missing observations for a local unit are reconstructed through NVBPCA by considering information from both local and neighboring units. A CCA-based local monitor, which identifies the status of the local unit and the type of a detected fault using information from both the local and neighboring units, is then developed. The NVBPCA–CCA approach has a better performance since its missing data handling and local monitor construction consider information from both the local and neighboring units. The efficiency of the proposed monitoring method is demonstrated through its application in a numerical example and an industrial tail gas treatment process.

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