System and method for predicting vehicle component failure and providing a customized alert to the driver

1. A method for predicting component failure in a vehicle and alerting a driver based thereon, the method comprising:

receiving sensor data from at least one vehicle sensor over a period of time;

processing covariance data using a model to detect an anomaly indicative of an upcoming component failure, the covariance data based on a plurality of characteristic data sets created from the sensor data;

determining a severity of the upcoming component failure based on at least one operating characteristic of the vehicle; and

providing an alert to the driver of the vehicle regarding the severity of the upcoming component failure.

2. The method of claim 1 , wherein

determining the severity of the upcoming component failure includes calculating an estimated time or distance remaining until the upcoming component failure based on the at least one operating characteristic of the vehicle.

3. The method of claim 2 , wherein

providing the alert to the driver includes notifying the driver of the estimated time or distance remaining until the upcoming component failure.

4. The method of claim 1 , further comprising

training the model to detect the anomaly using time-series data from a plurality of similar vehicles.

5. The method of claim 1 , further comprising

storing the model on a memory of the vehicle.

6. The method of claim 1 , wherein

the at least one operating characteristic includes at least one of: (i) average trip speed of the vehicle; (ii) average acceleration of the vehicle; (iii) average revolutions per minute; (iv) average torque experienced by the vehicle; (v) average oil temperature experienced by the vehicle; and (vi) average number of braking or acceleration events experienced by the vehicle.

7. The method of claim 1 , further comprising

creating at least one image using the sensor data,

wherein processing the covariance data using the model includes processing the at least one image using the predictive model.

8. A method for predicting component failure in a vehicle and alerting a driver based thereon, the method comprising: receiving sensor data from at least one vehicle sensor over a period of time; separating the sensor data into a plurality of characteristic data sets corresponding to different operating characteristics of the vehicle; processing covariance data based on a multiple of the characteristic data sets using a model to detect an anomaly indicative of an upcoming component failure; and providing an alert to the driver of the vehicle regarding the upcoming component failure.

9. The method of claim 8 , further comprising creating at least one image using at least one of the characteristic data sets, wherein processing the covariance data based on a multiple of the characteristic data sets using the model includes processing the at least one image using the model.

10. The method of claim 9 , wherein the at least one image is created using a multiple of the plurality of characteristic data sets.

11. The method of claim 8 , wherein

separating the sensor data into the plurality of characteristic data sets includes separating the sensor data into the plurality of characteristic data sets based on correspondence with at least one of: (i) trip speed; (ii) acceleration; (iii) revolutions per minute; (iv) torque; (v) oil temperature; and (vi) number of braking or acceleration events.

12. The method of claim 8 , further comprising

training the model to detect the anomaly using time-series data from a plurality of similar vehicles.

13. The method of claim 8 , further comprising

calculating an estimated time or distance remaining until the upcoming component failure,

wherein providing the alert to the driver includes notifying the driver of the estimated time or distance remaining until the upcoming component failure.

14. A method for predicting component failure in a vehicle and alerting a driver based thereon, the method comprising: receiving sensor data from at least one vehicle sensor over a period of time: separating the sensor data into a plurality of characteristic data sets corresponding to different operating characteristics of the vehicle; creating at least one image using covariance data based on a multiple of the plurality of characteristic data sets; processing the at least one image using a model to detect an anomaly indicative of an upcoming component failure; and providing an alert to the driver of the vehicle regarding the upcoming component failure.

15. A system for predicting component failure in a vehicle and alerting a driver based thereon, the system comprising:

at least one vehicle sensor configured to generate sensor data relating to at least one vehicle component over a period of time;

a memory storing at least one operating characteristic of the vehicle and at least one model that has been trained using time-series data from a plurality of other vehicles; and

a processor configured to execute instructions stored on the memory to: (i) process covariance data created from the sensor data using the model to detect an anomaly indicative of an upcoming component failure; (ii) determine a severity of the upcoming component failure based on the at least one operating characteristic of the vehicle; and (iii) provide an alert to the driver of the vehicle regarding the severity of the upcoming component failure.

16. The system of claim 15 , wherein

at least one of the processor and the memory is located within the vehicle.

17. The system of claim 15 , wherein

the processor is configured to cluster the sensor data in a plurality of characteristic data sets corresponding to different operating characteristics of the vehicle, and to process at least one of the characteristic data sets using the model to detect the anomaly indicative of the upcoming component failure.

18. The system of claim 15 , wherein

the vehicle includes a display, and

the processor provides the alert to the driver by altering the display based on the severity of the upcoming component failure.