Abstract: A system for determining a cleaning schedule for an asset is provided. In some aspects, the system can include a plurality of sensors arranged to monitor an asset and a computing system including at least one data processor and memory storing instructions, which when executed by the at least on data processor causes the at least one data processor to perform operations. In some aspects, the operations performed by the processor can include receiving, from the plurality of sensors, the data characterizing the operational efficiency of the asset, determining an operational efficiency of the asset determining an operational efficiency threshold characterizing an undesirable operating efficiency, determining, using an optimization algorithm, a cleaning schedule for the asset based on the operational efficiency of the asset and the operational efficiency threshold and providing the cleaning schedule.

Abstract: A method for detecting machines engaged in anomalous activity can include receiving telematics data from a plurality of sensors on each of a plurality of machines and determining one or more activity types for each machine over a series of activity time periods based on the associated telematics data for each machine. The method can also include calculating a proportion of the activity time periods in which each machine was engaged in one or more selected activities. The method further includes extracting one or more features for each machine for each of the series of activity time periods from the associated telematics data for each machine. One or more of the plurality of machines engaged in anomalous activity can be identified based on at least the proportion and the one or more extracted features for the machines.

Abstract: A method for forecasting part sales, including collecting sales data for a part over a series of sales time periods and collecting activity data for a plurality of activity types over a series of activity time periods for a plurality of machines including the part. A mean activity time can be calculated for each activity type for each time period in the series of activity time periods based on the collected activity data. An activity probability density function of the mean activity times for each activity type is created and a machine learning model is trained using an expectation of activity derived from the probability density functions for each activity type and the collected sales data. Machine activity data for a set of machines can be fed into the trained model to derive a part sales probability density function for the set of machines.

Abstract: A method for estimating warranty costs for an individual machine can include training a warranty cost model. The method can also include receiving telematics data from a plurality of sensors on an individual machine and determining one or more activity types for the individual machine based on the associated telematics data. A mean activity time can be calculated for each activity type. The mean activity time for each activity type can be fed into the trained warranty cost model to provide a predicted warranty cost for the individual machine and a corresponding probability of the predicted warranty cost from the trained warranty cost model.

Abstract: A method for forecasting part sales, including collecting sales data for a part over a series of sales time periods and collecting activity data for a plurality of activity types over a series of activity time periods for a plurality of machines including the part. A mean activity time can be calculated for each activity type for each time period in the series of activity time periods based on the collected activity data. An activity probability density function of the mean activity times for each activity type is created and a machine learning model is trained using an expectation of activity derived from the probability density functions for each activity type and the collected sales data. Machine activity data for a set of machines can be fed into the trained model to derive a part sales probability density function for the set of machines.