Using digital twins and other type models in manufacturing


Physics-based models incorporate the physi­cal and thermodynamics laws of how things work, while rules-based analysis is a kind of principles-driven analysis based on observation and domain expertise, including failure mode effect analysis (FMEA).

On the other hand, data-driven analytics include, first, statistical models based on tech­niques like linear and other type regression, and second, advanced analytics that include artificial intelligence and machine learning, often based on pattern recognition.

Roughly put, the domain of principles-driven analytics are the pumps, heat exchangers and myriad other equipment types whose workings are well understood by the engineering com­munity. Root-cause analyses are often part of the picture.

On the other hand, the realm of machine learning and artificial intelligence is the com­plex problems and to-be-discovered challenges native to process, plantwide and enterprise systems, where custom configurations of com­plex systems lead to unknowns.

Physics-based models continue to play a crucial role that is being augmented by a data-based approach, to bet­ter understand, for example, what a normal operating range is, or, drilling down in detail, to better understand the correlations involved. But while machine learning may uncover otherwise unrecognized correlations, it’s not necessarily able to recognize cause-and-effect relation­ships.

Tune into this webinar to learn more about how the age of analytics is unfolding.