Abstract: Systems and methods are disclosed herein for identifying and predicting maintenance events with respect to assets, such as mobile machinery. A computing platform can include control circuit(s) (on-board and/or remote) configured to acquire machine fault data and machine utilization data. A feature set can be generated by transforming the machine fault data (e.g., by fusing the machine fault data with machine utilization data), normalizing the data, generating embeddings, and/or executing imputation algorithms to fill in missing values. A classifier model can be trained using the transformed machine fault data in conjunction with labeled maintenance event data to enable the trained classifier model to learn to recognize utilization-specific fault code patterns. The trained classifier model can generate inferences and predictions regarding machinery maintenance events without the use of maintenance data.

Abstract: A technique is directed to methods and systems for managing electric vehicle charging. The electric vehicle management system can determine a battery state of charge using a data driven model and send geolocation push notifications regarding battery charging states to the electric vehicle, operators, and/or fleet managers. The electric vehicle management system can determine the routes for available chargers, the transit time, battery charging time and rate, and peak load costs for a charging an electric vehicle. A user can access the electric vehicle management system via an application on a user device.

Abstract: The system obtains SOH of a first component, where the SOH indicates a difference between a current state of the first component and a state of a rated new component. The system obtains historical use of the first component. Based on the SOH and the historical use, the system determines a future SOH of the first component. The system obtains an indication of a requested SOH of the first component. Based on the indication of the requested SOH and the future SOH of the first component, the system determines a first time indicating when the first component will need to be replaced. The system determines a second time indicating when the second component is available to replace the first component, where the second time occurs before the first time. The system requests a delivery of the second component when the second time is substantially close to the first time.