Method and system for adaptive learning of models in manufacturing systems

Abstract

In applications such as adaptive learning of physics-based and data-driven models associated with industrial plants, the models are corrected periodically by taking into consideration the dynamic changes occurring in plant conditions and related data. However, accuracy of adaptive learning depends on accuracy of ground truth data being used as reference data. The disclosure herein generally relates to data preprocessing, and, more particularly, to a method and system for ground truth profile correction and instance selection. The system performs a ground truth profile correction for ground truth profiles having a Profile Deviation Index (PDI) value exceeding a threshold of distortion, to reduce the PDI value, and in turn reduce the distortion in the ground truth profiles. Further, the system performs a data instance selection to identify and remove outliers, and the data that remains after the data instance selection may be then used for applications such as model generation or retuning.