Clustered Manifold Approximation and Projection for Semisupervised Fault Diagnosis and Process Monitoring

With increasing demands on product quality and safety requirements, modern industrial processes are highly instrumented and the data collected are being utilized to monitor and diagnose processes. In many cases, the process records include labels that indicate process operating conditions or prior knowledge of the sample points, which can be used to improve diagnostic performance. For this reason, semisupervised methods that can utilize both labeled and unlabeled data are recently gaining interest. In this article, we propose a novel manifold learning-based semisupervised process monitoring method, named Clustered Manifold Approximation and Projection (CMAP). In CMAP, a data manifold is approximated ahead of projection, where the distance on the manifold is defined by the pairwise interaction between the data points induced by metric and nonmetric attributes. This allows simultaneous uti…