Method for simplified real-time diagnoses using adaptive modeling

• US RE45,815 E1
• Filed: 11/14/2008
• Issued: 12/01/2015
• Est. Priority Date: 05/28/2004
• Status: Active Grant

• Current Assignee: OL Security LLC (Intellectual Ventures LLC)
• Original Assignee: Ramsle Technology Group GmbH LLC (Intellectual Ventures LLC)
• Inventors: Schroedl, Stefan, Nitsche, Christof, Weiss, Wolfgang
• Primary Examiner(s): STEELMAN, MARY J
• Application Number:
  US12/291,990
• Time in Patent Office: 2,573 Days
• Field of Search: 702/35, 702/41, 702/66, 702/67, 702/71, 702/81, 702/84, 702/113, 702/145, 702/182, 702/183, 702/181, 703/7, 703/8, 703/17, 703/18
• US Class Current: 1/1
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 3/0053 relating to fuel cells
• CPC Class Codes: B60L 58/30 for monitoring or controlling fuel cells
• CPC Class Codes: B60W 50/0205 Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: Y02T 90/34 Fuel cell powered electric vehicles [FCEV]

Abstract

A method for on-board real-time diagnostics of a mobile technical system using an adaptive technique to approximate stationary characteristic curves resulting from a workshop test. This adaptive technique uses observed non-stationary normal driving data to eliminate confounding variables.

Claims

1. A method for on-board real-time diagnostics of a system, said method comprising the steps:

providing a reference model containing predefined operating conditions and predefined confounding variables of said system and outputting a reference characteristic;

measuring real-world operating conditions and real-world confounding variables of said system and outputting a plurality of actual system output variables, wherein the real-world confounding variables comprise variables indicating a state of an environment of the system;

providing an adaptive model input with said real-world operating conditions and said real-world confounding variables in a first phase and inputting said predefined operating conditions and said predefined confounding variables in a second phase;

providing a first comparator for comparing said plurality of actual system output variables with an output of said adaptive model;

providing feedback means for feeding the output of said first comparativecomparator directly to an input of said adaptive model during said first phase;

providing a second comparator to compare the output of said adaptive model during the second phase with said reference characteristic output of said reference model, wherein the reference characteristic comprises a characteristic output curve for the system based on the reference model, and wherein the output of the adaptive model comprises a predicted output curve for the system based on the adaptive model;

providing a diagnostics module receiving the output of said second comparator during said second phase in order to output a diagnosis of said system.

2. The method according to claim 1, further including the step of switching between said first phase and said second phase wherein said first phase is a training phase and said second phase is a diagnostics phase.

3. The method according to claim 1, wherein said reference characteristic is a series of measured response functions generated by a stationary test of said system.

4. The method according to claim 3, wherein said measured response function provides a polarization curve generated by a stationary test of a fuel cell powertrain.

5. The method according to claim 3, wherein said measured response function provide a speed/torque curve generated by a stationary test of an internal combustion engine.

6. The method according to claim 1, wherein said system is a fuel cell powertrain.

7. The method according to claim 1, wherein said real-world operating conditions and said real-world confounding variables are generated when a vehicle containing said system is being driven during normal operation.

8. The method according to claim 1, wherein said system is a mobile technical system of a vehicle.

29. The method according to claim 1, wherein the real-world confounding variables comprise at least an outside temperature.

30. The method according to claim 1, wherein the providing the adaptive model input with said predefined operating conditions and said predefined confounding variables in the second phase further comprises:

setting said predefined confounding variables to a fixed value; and

varying said predefined operating conditions.

31. The method according to claim 1, wherein during the second phase:

the characteristic output curve is further based on said predefined operating conditions and said predefined confounding variables, and

the predicted output curve is further based on said predefined operating conditions and said predefined confounding variables.

9. An arrangement for real time diagnostics of a system, comprising:

a reference model receiving configured to receive predefined operating conditions and predefined confounding variables of said system and outputting a reference characteristic;

means for inputtingan input mechanism configured to input to said system real-world operating conditions and real-world confounding variables of said system wherein the output of said system provides actual system output variables;

an adaptive model receiving configured to receive, in a first phase, said real-world operating conditions and said real-world confounding variables and, in a second phase said pre-defined operating conditions and said predefined confounding variables to provide a first output during said first phase and a second output during said second phase;

a first comparator means for comparingconfigured to compare said actual system output variables with said first output of said adaptive model;

a feedback means receivingmechanism configured to receive an output of said first comparator means and feeding feed said output directly to said adaptive model during said first phase;

a second comparator means for comparingconfigured to compare an output of said reference model with the second output of said adaptive model during said second phase, wherein the output of said reference model comprises a characteristic output curve for the system based on said reference model, and wherein the second output of said adaptive model comprises a predicted output curve for the system based on said adaptive model;

a diagnostics module receiving configured to receive an output of said second comparator during said second phase;

and

a switching means for switching mechanism configured to switch between said first and second phase.

10. The arrangement according to claim 9, wherein said first phase is a training phase and said second phase is a diagnostics phase.

11. The arrangement according to claim 9, wherein said reference characteristics are a series of measured response functions generated by a stationary test of said system.

12. The arrangement according to claim 11, wherein said measured response functions provide a polarization curve generated by a stationary test of a fuel cell powertrain.

13. The arrangement according to claim 11, wherein said measured response functions provide speed/torque curve generated by a stationary test of an internal combustion engine.

14. The arrangement according to claim 9, wherein said system is a fuel cell powertrain.

15. The arrangement according to claim 9, wherein said real-world operation conditions and said real-world confounding variables are generated from a measuring means during the normal driving operation of a vehicle containing said system.

16. A method comprising:

receiving, by a processing device, an actual-system output from a vehicle component;

adapting, by the processing device, an adaptive model in a training phase in response to a received error signal, wherein the received error signal is received at the adaptive model, wherein the received error signal is a difference between a predicted output of the adaptive model and the actual-system output, and wherein the difference is used to train the adaptive model;

generating, by the processing device, the predicted output in a diagnostic phase based on a received set of diagnostic conditions;

switching, by the processing device, between the training phase and the diagnostic phase;

when in the diagnostic phase;

comparing, by the processing device, a reference output from a reference model to the predicted output, wherein the reference output comprises a characteristic output curve for the vehicle component based on the reference model, and wherein the predicted output comprises a predicted output curve for the vehicle component based on the adaptive model; and

providing, by the processing device, an indication based on the comparison.

17. The method of claim 16 wherein the adaptive model utilizes adaptive curve fitting.

18. The method of claim 16 wherein the adaptive model utilizes a three-layer feedforward neural network.

19. The method of claim 16 wherein the adaptive model is trained only when the received error is above a threshold.

20. The method of claim 16 further comprising transmitting the adaptive model to a database.

21. The method of claim 16 wherein the switching between the training phase and the diagnostic phase is in response to a request.

22. The method of claim 16 wherein the switching between the training phase and the diagnostic phase occurs according to a schedule.

27. The method of claim 16, wherein switching between the training phase and the diagnostic phase comprises switching to the diagnostic phase only when an average error corresponding to the received error signal is below a threshold.

28. The method of claim 16, further comprising triggering the training phase only when an average error corresponding to the received error signal is increasing and exceeds a threshold.

23. A real-time diagnostic device, comprising:

a vehicle component, configured to provide an actual-system output;

an adaptive model, configured to;

generate a predicted output in a training phase by adjusting the adaptive model in response to a received error signal, wherein the received error signal is received at the adaptive model, wherein the received error signal is a difference between the predicted output of the adaptive model and the actual-system output, and wherein the difference is used to train the adaptive model;

generate the predicted output in a diagnostic phase based on a received set of diagnostic conditions;

switch between the training phase and the diagnostic phase;

a reference model, configured to;

generate a reference output;

a comparator, configured to;

compare the predicted output in the diagnostic phase to the reference output, wherein the reference output comprises a characteristic output curve for the vehicle component based on the reference model, and wherein the predicted output in the diagnostic phase comprises a predicted output curve for the vehicle component based on the adaptive model, and

provide an indication based on the comparison.

24. The device of claim 23 wherein the adaptive model is configured to use curve fitting.

25. The device of claim 23 wherein the adaptive model is configured to use neural networks.

26. The device of claim 23 further comprising a diagnostics module for transmitting the adaptive model to a server.

32. The device of claim 23, wherein the adaptive model is further configured to switch to the diagnostic phase only when the received error signal is below a threshold, and wherein the received error signal being below a threshold indicates that the adaptive model accurately models the actual-system output from the vehicle component.