Abstract

This paper examines the suitability of unsupervised machine learning methods for image analysis, within the innovative visual analytics framework for process monitoring, and proposes a set of performance metrics that evaluate accuracy for visual analytics. The effectiveness of the proposed method is demonstrated via a case study using real industrial data from a steam boiler.