System and method for estimating engine oil health

• US 9,714,931 B2
• Filed: 09/03/2014
• Issued: 07/25/2017
• Est. Priority Date: 09/03/2014
• Status: Active Grant

• Current Assignee: GE Global Sourcing LLC (Westinghouse Air Brake Technologies Corporation)
• Original Assignee: General Electric Company
• Inventors: Prabhu, Rahul Srinivas, Shanmukh, Saratchandra, Madyastha, Venkatesh Kattigari, Adhiachari, Subramani, Vijay, Sethuraman, Kayal, Partho
• Primary Examiner(s): McCall, Eric S
• Assistant Examiner(s): Keramet-Amircola, Mohammed E
• Application Number:
  US14/476,378
• Publication Number:
  US 20160061805A1
• Time in Patent Office: 1,056 Days
• Field of Search: 7311455
• CPC Class Codes: G01N 33/2817 using a test engine testing of engines G01M15/00
• CPC Class Codes: G01N 33/2888 Lubricating oil characteristics, e.g. deterioration lubricating properties G01N33/30
• CPC Class Codes: G01N 33/30 for lubricating properties

Abstract

A system includes a sensor that may measure one or more engine oil parameters to assess engine oil health of an engine and a processor communicatively coupled to the sensor and that may receive a signal from the sensor. The signal is representative of a real-time measurement of the one or more engine oil parameters. The processor may also estimate the one or more engine oil parameters over time via an adaptive predictive model associated with the one or more engine oil parameters to generate estimated data and reconcile the real-time measurement and the estimated data to generate an integrated engine oil degradation model and predict engine oil remaining useful life based on the integrated engine oil degradation model and one or more condemn limits associated with the one or more engine oil parameters.

Claims

1. A system, comprising:

a sensor configured to measure one or more engine oil parameters to assess engine oil health of an engine; and

a processor communicatively coupled to the sensor and configured to;

receive a signal from the sensor, wherein the signal is representative of a real-time measurement of the one or more engine oil parameters;

estimate the one or more engine oil parameters over time via an adaptive predictive model associated with the one or more engine oil parameters to generate estimated data;

reconcile the real-time measurement and the estimated data to generate an integrated engine oil degradation model and predict engine oil remaining useful life based on the integrated engine oil degradation model and one or more condemn limits associated with the one or more engine oil parameters,

wherein the adaptive predictive model self-tunes depending on engine oil grade.

2. The system of claim 1, wherein the processor comprises a tangible, non-transitory, machine-readable media storing the adaptive predictive model, the integrated engine oil degradation model, and the condemn limits.

3. The system of claim 1, wherein the processor is configured to apply a filter to reconcile the real-time measurement and the model data.

4. The system of claim 3, wherein the filter comprises a Kalman filter or a particle filter.

5. The system of claim 1, wherein the processor is configured to triangulate the real-time measurement, the estimated data, and sensor calibration data to validate the adaptive predictive model and the integrated engine oil degradation model.

6. The system of claim 1, wherein the one or more engine oil parameters comprise a total base number parameter, a soot loading parameter, a viscosity parameter, a moisture content parameter, or a combination thereof.

7. The system of claim 1, wherein the engine operational parameters comprise engine duty cycle, engine revolutions per minute, engine load, or a combination thereof.

8. The system of claim 1, wherein the processor is configured to provide the integrated engine oil degradation model as an input to the adaptive predictive model.

9. The system of claim 1, wherein the processor is configured to display an indication of the engine oil remaining useful life.

10. The system of claim 1, wherein the processor is configured to determine engine oil top-up parameters based on the estimated data.

11. A system, comprising:

a processor, comprising;

one or more tangible, non-transitory, machine-readable media collectively storing one or more sets of code; and

one or more processing devices configured to execute the one or more sets of code to predict health of engine oil associated with an engine, wherein the one or more sets of code comprises instructions for;

receiving a signal from a sensor, wherein the signal is representative of a real-time measurement of the one or more engine oil parameters;

modeling each of the one or more engine oil parameters over time based on engine operational parameters;

reconciling the real-time measurement with the respective model data for each of the one or more engine oil parameters; and

predicting the health of the engine oil based on an adaptive integrated engine oil degradation model and condemn limits for each of the one or more engine oil parameters,

wherein the adaptive integrated engine oil degradation model self-tunes depending on engine oil grade.

12. The system of claim 11, wherein the instructions are configured to apply a Kalman filter to reconcile the real-time measurement and the model data.

13. The system of claim 11, wherein the instructions are configured to apply a Kalman filter to tune the adaptive integrated engine oil degradation model.

14. The system of claim 11, wherein the engine oil parameters comprise a total base number parameter, a soot loading parameter, a viscosity parameter, and a moisture content parameter.

15. The system of claim 11, wherein the integrated engine oil model comprises a total base number model, a soot model, a viscosity model, and a moisture content model.

16. The system of claim 11, wherein the engine operational parameters comprise an engine duty cycle, engine revolutions per minute, engine loading, or a combination thereof.

17. A method, comprising:

measuring a plurality of engine oil parameters associated with engine oil health with one or more sensors configured to measure an engine oil sample;

transmitting sensed data from the one or more sensors to a processor communicatively coupled to the one or more sensors;

modeling each of the plurality of engine oil parameters based on an operational parameter of an engine associated with the engine oil sample to generate model data;

reconciling the sensed data with the respective model data for each of the plurality of engine oil parameters; and

predicting a condition of the engine oil sample based on predetermined limits for each of the plurality of engine oil parameters,

wherein the adaptive model of engine oil degradation adapts to a grade of the engine oil sample.

18. The method of claim 17, comprising applying a Kalman filter to reconcile the sensed data and the model data, wherein the Kalman filter is configured to tune an adaptive model of engine oil degradation.

19. The method of claim 17, wherein the condition comprises a condemn condition if at least one of the plurality of engine oil parameters is in a range that is outside the predetermined limits.

20. The method of claim 17, wherein the plurality of engine oil parameters comprise a total base number parameter, a soot loading parameter, a viscosity parameter, and a moisture parameter.

21. The method of claim 17, wherein the operational parameter comprises an engine duty cycle, engine revolutions per minute, engine loading, or a combination thereof.

22. The method of claim 17, comprising determining an oil top-up volume based on the model data.