Abstract: A classifier is executed including an unsupervised artificial intelligence model and a supervised artificial intelligence model. The classifier is configured to receive run-time input data, and process the run-time input data using the unsupervised artificial intelligence model and an outlier detection algorithm to determine whether the run-time input data is an outlier as compared to training input data. Responsive to determining that the run-time input data is not an outlier, the classifier determines a predicted response label for the run-time input based on the run-time input data processed using the supervised artificial intelligence model. Responsive to determining that the run-time input data is an outlier, the classifier refrains from determining the predicted response label for the run-time input based on the run-time input data processed using the supervised artificial intelligence model, and instead outputs a prompt for user input of a user-curated response label for the run-time input.

Abstract: A system may include a set of components that make up an engineered system configured to generate real-time data representing a set of real-time operational behaviors associated respectively with the set of components. The system may include a predictive model configured to predict a normal operational behavior associated with a component of the set of components relative to other normal operational behaviors associated respectively with other components of the set of components. The system may include a processor configured to receive the set of real-time operational behaviors, the set of real-time operational behaviors including a real-time operational behavior associated with the component, and to categorize the real-time operational behavior associated with the component as normal or anomalous based on the predictive model. The system may include an output device configured to output an indication of fault in response to the processor categorizing the real-time operation behavior as anomalous.