The CMS executes regularly scheduled measurements of values which are relevant to wear monitoring. This results in a data pool, which is evaluated together with the respective process data. The ultimately relevant differentiators are obtained automatically by targeted selection from a wide range of statistical parameters. Each record represents a point in a feature space, which can then be captured visually. Multiple measurements of operating conditions form clusters. They initially represent different operating conditions. The first appearance of machine wear will change the form and location of the clusters. As a consequence, this can then be detected in the fingerprint and the diagnosed change is reported.
This is how the CMS expands its database step-by-step with measurements of various wear and operating conditions and monitors all previous classification features continuously. When they no longer represent the current situation, the differentiators of the data fingerprints will be recalculated and an adjusted set of specific features is established.