Abstract: An electronic control module (ECM) on-board a machine executes a reduced-order model configured to generate virtual sensor data associated with a machine component based on sensor data measured by actual sensors. The ECM can also execute a machine learning model configured to generate offset data for the sensor data and/or the virtual sensor data, based on operating conditions, an age of the machine component, and/or other factors that the reduced-order model may not consider. The sensor data and/or the virtual sensor data, adjusted based on the offset data, can indicate whether the machine component has failed or is predicted to fail at a future point in time, and/or a remaining usable life of the machine component.

Abstract: A method for providing improved composite work cycle damage estimates includes constructing a damage rate basis matrix, performing a D-optimal row selection calculation on the damage rate basis matrix, selecting, based on the D-optimal row selection calculation, a finite number of strain measurement device locations on the machine, extracting a target percentile damage rate for each of the one or more strain measurement devices, and using the extracted damage rates to solve for the unknown coefficients and verify the weightings assigned to the machine operations.

Abstract: A method for providing improved composite work cycle damage estimates includes constructing a damage rate basis matrix, performing a D-optimal row selection calculation on the damage rate basis matrix, selecting, based on the D-optimal row selection calculation, a finite number of strain measurement device locations on the machine, extracting a target percentile damage rate for each of the one or more strain measurement devices, and using the extracted damage rates to solve for the unknown coefficients and verify the weightings assigned to the machine operations.

Abstract: A method for aligning composite work cycle seventies to target percentile severity responses includes selecting a set of machines to be monitored for severity, applying an operation classification corresponding to one or more machine operations performed by each machine in the set of machines, applying a severity indexing system to the set of machines, constructing a distribution of severity responses associated with an operation classification, receiving data from one or more performed damage simulations or tests and mapping a selected range of test event seventies for the operation classification to corresponding target actual seventies for the operation based upon performance of the operation by the machine.

Abstract: A method for generating a training plan for an operator of a machine to perform an operation is provided. The method includes receiving data associated with one or more functional parameters of the machine. The functional parameters include at least one of an operation parameter, an operator attribute parameter, and an environmental parameter. The method includes identifying a value of each of the functional parameters based on the data. Further, the method includes determining, in real-time, the training plan based on the identification of the functional parameters. The method includes communicating the instruction to the operator for performing the operation on the machine based on the training plan. The method includes monitoring the operation for being in conformance with the training plan. The method includes generating an alarm, when the operation performed by the operator deviates from the instructions of the training plan.

Abstract: Systems and methods for aggregating classifications of predictive operations of a machine may include one or more sensors in communication with a processor. Such systems and methods may be implemented to receive on-board machine time series data, process the data, segment the processed data, resolve the segmented data, and characterize the segmented data. Additionally, such systems and methods may be implemented to determine profile percentage breakdowns of a machine, performance reports, and productivity reports, and inform composite work cycles.