Abstract: A system and method for labelling normal and abnormal regions in data related to a paper machine for web break prediction and labelling individual parameters for root cause analysis, using machine learning models, includes using the machine learning models in real-time to predict breaks in the paper web, analyzing root cause for the breaks in the paper web, and estimating a time to break. An auto-data-labeling framework helps in adaptive learning for autonomous model improvement of the deployed model, transfer learning, shortlisting parameters and automating feasibility study.

Abstract: The invention relates to a method and system to generate control logic for performing industrial processes with a controller in a process plant. The method includes receiving a control narrative comprising one or more control requirements of the industrial process, and extracting a plurality of control entities and a plurality of set points, from the control narrative using one or more sets of predetermined regular expressions and one or more models. The method further includes identifying a set of inputs, outputs and control elements from the plurality of control entities using a domain dictionary, detecting a plurality of actions from the control narrative using an intent classifier, identifying a relationship between the set of inputs, outputs and control elements, the plurality of set points, and the plurality of actions, and generating based on the relationship identified the control logic for the controller to perform the process.