Managing state of health and remaining useful life regarding a vehicle

A method comprising:

• receiving, by at least one processor that performs operations by executing instructions, telemetry data generated by multiple sensors of a vehicle, the multiple sensors arranged to generate the telemetry data regarding at least one first component of the vehicle;
• detecting, with multiple failure mode components associated with respective conditions of the at least one first component based on the telemetry data received from the multiple sensors, one or more failure modes of the at least one first component by determining corresponding likelihoods of one or more conditions associated with a respective failure mode component exists, wherein each of the multiple failure mode components is configured to determine a corresponding likelihood that a respective condition exists with regard to the at least one first component;
• providing, by the at least one processor, the telemetry data to multiple failure mode components associated with the respective conditions of the at least one first component, wherein each failure mode component includes executable instructions to detect one or more failure modes of the at least one first component by determining the corresponding likelihoods of the one or more conditions associated with the respective failure mode component currently exists, wherein each of the multiple failure mode components is configured to determine the corresponding likelihood that a respective condition exists with regard to the at least one first component;
• determining, by the at least one processor and based at least in part on the corresponding likelihoods, a degree of degradation of at least one first system of the vehicle from identification of a first state of the at least one first system of the vehicle, the at least one first system including the at least one first component;
• predicting, using the degree of degradation of the at least one first system and application of one or more physics of failure, pattern recognition, trend analysis, extrapolation, interpolation, and probability estimation, i) a state of health (SOH) for the at least one first system, and ii) a remaining useful life (RUL) for the at least one first system, performing, by the at least one processor and based at least in part on the first state, a prognostication that implements a model to predict i) a state of health (SOH) for the at least one first system, and ii) a remaining useful life (RUL) for the at least one first system, wherein the SOH and the RUL are based on the corresponding likelihoods;
• and
• identifying a root cause relating to the at least one first component of the vehicle based at least in part on the SOH and the RUL, and performing, by the at least one processor and based at least in part on the SOH and the RUL, at least one action including changing at least one threshold with regard to the at least one first component of the vehicle, wherein performing the at least one action comprises presenting information relating to a fleet of vehicles, the fleet including the vehicle, on a graphical user interface, the information based on the SOH and the RUL, and wherein the graphical user interface is configured for performing a hierarchical drilldown using the identified root cause relating to the at least one first component of the vehicle.