Abstract: Systems, methods, and computer program products for monitoring a health condition of a tool. Operational data is collected from a machine while the machine is operating in a predetermined manner with the tool in each of at least two known health conditions. A plurality of features is extracted from the operational data, a training dataset is generated from the extracted features, and an analytic model is trained using the training dataset. The analytic model can then be used to determine the health condition of the tool by providing features extracted from operational data received from one or more field machines to the analytic model. The analytic model may then determine a health condition of the tool in the field machine based on like features extracted from the operational data from the one or more field machines.

Abstract: Systems, methods, and computer program products for monitoring a health condition of a tool. Operational data is collected from a machine while the machine is operating in a predetermined manner with the tool in each of at least two known health conditions. A plurality of features is extracted from the operational data, a training dataset is generated from the extracted features, and an analytic model is trained using the training dataset. The analytic model can then be used to determine the health condition of the tool by providing features extracted from operational data received from one or more field machines to the analytic model. The analytic model may then determine a health condition of the tool in the field machine based on like features extracted from the operational data from the one or more field machines.

Abstract: Systems, methods, and computer program products for remaining useful life prediction. Operational data is collected from a test machine until a component fails, and a training dataset generated from the operational data. The training dataset is used to define and validate a prediction model. Operational data received from one or more field machines is provided to the prediction model. The prediction model then predicts the remaining useful life of the component of the field machine. To reduce the time-to-failure of the component in the test machine, the component may be repeatedly subjected to an accelerated wear cycle. The prediction model may be defined by extracting features from the training dataset. Like features may be extracted from the field dataset and provided to the prediction model as part of the prediction process. The operational data received from the field machines may be used to generate an updated prediction model.