Abstract: A method for creating predictive damage models includes receiving a first predictive damage model, identifying latent space between a first and a second domain asset, building a regression model from first domain asset projected source data, creating target dependent variables of a second model, applying classification or regression techniques to determine a function expressing the dependent variables, determining data points from the function to develop a second regression model, applying the second regression model to data points to predict target dependent variables, evaluating the second predictive damage model using the predicted target dependent variables, performing a sensitivity study to determine a directionality parameter of the second predictive damage model, and if the results are within an acceptable predetermined range, providing maintenance or servicing recommendations generated by the second predictive model to a user platform display, else repeating the process by rebuilding the regression mo

Abstract: A method for creating predictive damage models includes receiving a first predictive damage model, identifying latent space between a first and a second domain asset, building a regression model from first domain asset projected source data, create target dependent variables of a second model, applying classification or regression techniques to determine a function expressing the dependent variables, determining data points from the function to develop a second regression model, applying the second regression model to data points to predict target dependent variables, evaluating the second predictive damage model using the predicted target dependent variables, performing a sensitivity study to determine a directionality parameter of the second predictive damage model, and if the results are within an acceptable predetermined range, providing maintenance or servicing recommendations generated by the second predictive model to a user platform display, else repeating the process by rebuilding the regression mode

Abstract: The present embodiments related to a machinery failure evaluation system and associated method. The system may receive time-series data associated with a piece of machinery. An anomaly associated with the piece of machinery may automatically be determined by comparing the time-series data with a model associated with the piece of machinery. Furthermore, it may be determined that the anomaly is not a known fault based on performing a lookup of known failure modes. In a case that the anomaly is not a known fault, an alert associated with an unknown failure mode may be transmitted.