Abstract: There is provided an automated data driven method for predicting one or more operational states, such as wear or degradation, of a mechanical system over time. The method has the steps of collecting data on the mechanical system from a data recording device, preprocessing the collected data, selecting a training data set that represents a base condition for statistical comparison, fitting a statistical model to the training data set to relate a predicted response to nuisance variables at the base condition, and using an output model to predict what an observed response would have been at the base condition and calculating the difference between the observed response and the predicted response to predict the one or more operational states of the mechanical system.

Abstract: There is provided an automated data driven method for predicting one or more operational states, such as wear or degradation, of a mechanical system over time. The method comprises the steps of collecting data on the mechanical system from a data recording device, preprocessing the collected data, selecting a training data set that represents a base condition for statistical comparison, fitting a statistical model to the training data set to relate a predicted response to nuisance variables at the base condition, and using an output model to predict what an observed response would have been at the base condition and calculating the difference between the observed response and the predicted response to predict the one or more operational states of the mechanical system.