Abstract: Systems and apparatuses include one or more processing circuits comprising one or more memory devices configured to store instructions thereon that cause one or more processors to: receive electronic field performance analytics (eFPA) information related to a component of the vehicle; receive vehicle information including operating conditions and historical vehicle information of the vehicle; receive fleet information including fleet usage and fleet vehicle types of the fleet of vehicles; develop a prognostic model using a machine learning engine that receives the eFPA information, the vehicle information, and the fleet information; determine a failure probability using the prognostic model; compare the failure probability to a predetermined threshold; determine a remaining life of the component when the failure probability is equal to or greater than the threshold; and generate a report identifying the component and the remaining life.

Abstract: Systems and apparatuses include one or more processing circuits comprising one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to: receive vehicle information including operating conditions and historical vehicle information, develop a vehicle model using a machine learning engine that receives the vehicle information, determine a fleet failure probability based on the vehicle model and fleet information including fleet usage and fleet vehicle types, and determine a predictive maintenance schedule based on the fleet failure probability.