Managing state of health and remaining useful life regarding a vehicle

1 . 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.

2 . The method of claim 1 , wherein at least one second system of the vehicle includes the at least one first system, the method further comprising quantifying, by the at least one processor and based at least in part on the corresponding likelihood, a second state of at least one second system.

3 . The method of claim 2 , wherein performing the prognostication is based at least in part also on the second state.

4 . The method of claim 2 , wherein performing the at least one action is based at least in part also on the second state.

5 . The method of claim 2 , wherein the at least one second system corresponds to the vehicle.

6 . The method of claim 1 , wherein performing the at least one action comprises specifying a maintenance opportunity for the vehicle.

7 . The method of claim 6 , further comprising extending, after performance of maintenance corresponding to the maintenance opportunity, the at least one threshold for the vehicle, the at least one threshold relating to at least one of the SOH or the RUL.

8 . The method of claim 6 , wherein specifying the maintenance opportunity comprises selecting a length of time for initiating the maintenance opportunity.

9 . The method of claim 1 , wherein the information is specific to the vehicle.

10 . The method of claim 1 , wherein performing the at least one action comprises notifying a service provider about vehicle maintenance.

11 . The method of claim 1 , wherein performing the at least one action comprises notifying an operator of the vehicle.

12 . The method of claim 1 , wherein the method is performed using multiple processors, wherein at least one of the multiple processors is located onboard the vehicle, and wherein at least another one of the multiple processors is located in a cloud separate from the vehicle.

13 . A system comprising:

a first system component configured to:

receive 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;

detect, 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; and

provide the telemetry data to multiple failure mode components associated with the respective conditions of the at least one first component and 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;

a second system component configured to determine, 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; and

a third component configured to:

predict, using the degree of degradation of the at least one first system and application of one or more of 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, 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

identify 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 perform, 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.

14 . The system of claim 13 , wherein an entirety of the system is implemented onboard the vehicle.

15 . The system of claim 13 , wherein a portion of the system is implemented onboard the vehicle, and wherein a remaining portion of the system is implemented in a cloud separate from the vehicle.

16 . The system of claim 13 , wherein an entirety of the system is implemented in a cloud separate from the vehicle, and wherein the system receives the telemetry data generated by the multiple sensors by a wireless communication sent from the vehicle to the cloud.