Correlation-based spectral clustering for flexible process monitoring

The individuality of production devices should be taken into account when statistical models are designed for parallelized devices. In the present work, a new clustering method, referred to as NC-spectral clustering, is proposed for discriminating the individuality of production devices. The key idea is to classify samples according to the differences of the correlation among measured variables, since the individuality of production devices is expressed by the correlation. In the proposed NC-spectral clustering, the nearest correlation (NC) method and spectral clustering are integrated. The NC method generates the weighted graph that expresses the correlation-based similarities between samples, and the constructed graph is partitioned by spectral clustering. A new statistical process monitoring method and a new soft-sensor design method are proposed on the basis of NC-spectral clustering. The usefulness of the proposed methods is demonstrated through a numerical example and a case study of parallelized batch processes.

► A new clustering method based on the correlation among samples was developed.
► A multi-model softsensor design method based on the clustering method was proposed.
► A new fault detection system based on the clustering method was proposed.