Diagnostic systems for turbocharged engines

• US 6,785,604 B2
• Filed: 05/15/2002
• Issued: 08/31/2004
• Est. Priority Date: 05/15/2002
• Status: Expired due to Fees

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Jacobson, Evan Earl
• Primary Examiner(s): Kwon, John
• Application Number:
  US10/144,738
• Publication Number:
  US 20030216856A1
• Time in Patent Office: 839 Days
• Field of Search: 701/99, 701/100, 701/101, 701/114, 701/115, 706/15
• US Class Current: 701/114
• CPC Class Codes: F02B 37/00 Engines characterised by provision of pumps driven at least for part of the time by exhaust
• CPC Class Codes: Y02T 10/12 Improving ICE efficiencies

Abstract

A method, system, and machine-readable storage medium for diagnosing operation in a turbocharged engine having an engine control module (ECM) operable to control engine operation in response to data received from a plurality of sensors is disclosed. In operation, the method, system, and machine-readable storage medium store data corresponding to a compressor map defining a region of compressor efficiency and compressor speeds during operation, and a turbine map defining a region of turbine efficiency and turbine speeds during operation. Next, the method, system and apparatus determine a predicted value for an operating parameter using data received from selected ones of the plurality of sensors and the data stored in memory, determine an actual value for the operating parameter using data received from selected ones of the plurality of sensors, and generate an abnormal operation signal if a difference between actual and predicted values is greater than a predetermined amount.

Claims

1. A method for diagnosing operation in a turbocharged engine having an engine control module (ECM) operable to control engine operation in response to data received from a plurality of sensors, the method comprising:

storing in a memory data corresponding to;

a compressor map defining a region of compressor efficiency and compressor speeds during operation; and

a turbine map defining a region of turbine efficiency and turbine speeds during operation;

determining a predicted value for an operating parameter measured by a first hardware sensor using data received from at least a second hardware sensor and the at least one of the compressor map and the turbine map;

determining an actual value for the operating parameter using data received from the first hardware sensor;

generating an abnormal operation signal if a difference between actual and predicted values exceeds a predetermined amount;

disabling the first hardware sensor if the difference between the actual and predicted values has exceeded the predetermined amount; and

controlling operation of an engine based on the predicted value.

2. The method of claim 1, wherein the disabling step further comprises disabling the first hardware sensor if the difference between the actual and predicted values has exceeded the predetermined amount a predetermined number of times or has occurred continuously over a predetermined period of time.

3. The method of claim 1, wherein the generating step further comprises adjusting an engine operation based on the actual value if the difference is less than the predetermined amount.

4. The method of claim 1, further comprising adjusting an engine operation based on the predicted value when the difference is greater than the predetermined amount.

5. The method of claim 1, further comprising determining whether a fault exists in the first hardware sensor.

6. A method for training an artificial neural network (ANN), the method comprising:

determining a predicted value for an operating parameter measured by a first hardware sensor using data received from the ANN;

determining an actual value for the operating parameter using data received from the first hardware sensor; and

using the actual value to train the ANN if a difference between the actual and predicted values is less than a predetermined amount.

7. The method of claim 6, further comprising:

disabling the first hardware sensor if the difference between the actual and predicted values has exceeded the predetermined amount a predetermined amount of times or has occurred continuously over a predetermined period of time; and

controlling operation of an engine based on the predicted value.

8. The method of claim 6, further comprising adjusting an engine operation using the predicted value when the difference between the actual and predicted values is greater than a predetermined amount.

9. The method of claim 6, further comprising determining whether a fault exists in the first hardware sensor.

10. A machine-readable storage medium having stored thereon machine executable instructions, the execution of said instructions adapted to implement a method for diagnosing abnormal operation in a turbocharged engine having an engine control module (ECM) operable to control engine operation in response to data received from a plurality of sensors, the method comprising:

storing in a memory data corresponding to;

a compressor map defining a region of compressor efficiency and compressor speeds during operation; and

a turbine map defining a region of turbine efficiency and turbine speeds during operation;

determining a predicted value for an operating parameter measured by a first hardware sensor using data received from at least a second hardware sensor and the at least one of the compressor map and the turbine map;

determining an actual value for the operating parameter using data received from the first hardware sensor;

generating an abnormal operation signal if a difference between actual and predicted values is greater than a predetermined amount;

disabling the first hardware sensor if the difference between the actual and predicted values has exceeded the predetermined amount a predetermined number of times or has occurred continuously over a predetermined period of time; and

controlling operation of an engine based on the predicted value.

11. The machine-readable storage medium of claim 10, wherein the generating step further comprises adjusting an engine operation based on the actual value if the difference is less than the predetermined amount.

12. The machine-readable storage medium of claim 10, further comprising:

adjusting an engine operation based on the predicted value when the difference is greater than the predetermined amount; and

determining whether a fault exists in the first hardware sensor.

13. A machine-readable storage medium having stored thereon machine executable instructions, the execution of said instructions adapted to implement a method for training an artificial neural network (ANN), the method comprising:

determining a predicted value for an operating parameter measured by a first hardware sensor using data received from the ANN;

determining an actual value for the operating parameter using data received from the first hardware sensor; and

using the actual value to train the ANN if a difference between the predicted and actual values is less than a predetermined amount.

14. The machine-readable storage medium of claim 13, further comprising:

disabling the first hardware sensor if the difference between the predicted and actual values has exceeded the predetermined amount a predetermined amount of times or has occurred continuously over a predetermined period of time; and

controlling operation of an engine based on the predicted value.

15. The machine-readable storage medium of claim 13, further comprising:

adjusting an engine operation based on the predicted value when the difference is greater than the predetermined amount; and

determining whether a fault exists in the first hardware sensor.

16. An apparatus for diagnosing abnormal operation in a turbocharged engine having an engine control module (ECM) operable to control engine operation in response to data received from a plurality of sensors, the apparatus comprising:

a microprocessor with a memory that includes data corresponding to;

a compressor map defining a region of compressor efficiency and compressor speeds during operation; and

a turbine map defining a region of turbine efficiency and turbine speeds during operation;

a module configured to determine a predicted value for an operating parameter measured by a first hardware sensor using data received from at least a second hardware sensor and the at least one of the compressor map and the turbine map;

a module configured to determine an actual value for the operating parameter using data received from the first hardware sensor;

a module configured to generate an abnormal operation signal if a difference between actual and predicted values is greater than a predetermined amount;

a module configured to disable the first hardware sensor if the difference between the actual and predicted values has exceeded the predetermined amount a predetermined number of times or has occurred continuously over a predetermined period of time; and

a module configured to control operation of an engine based on the predicted value.

17. The apparatus of claim 16, wherein the generating module further comprises a module configured to adjust an engine operation based on the actual value if the difference is less than the predetermined amount.

18. The apparatus of claim 16, further comprising:

a module configured to adjust an engine operation based on the predicted value when the difference is greater than the predetermined amount; and

a module configured to determine whether a fault exists in the first hardware sensor.

19. The apparatus of claim 16, wherein the plurality of modules comprise functionally related computer program code and data.

20. An apparatus for training an artificial neural network (ANN), the apparatus comprising:

a module configured to determine a predicted value for an operating parameter measured by a first hardware sensor using data received from the ANN;

a module configured to determine an actual value for the operating parameter using data received from the first hardware sensor; and

a module configured to use the actual value to train the ANN if a difference between the predicted and actual values is less than a predetermined amount.

21. The apparatus of claim 20, further comprising:

a module configured to disable the first hardware sensor if the difference between the predicted and actual values has exceeded the predetermined amount a predetermined amount of times or has occurred continuously over a predetermined period of time; and

a module configured to control operation of an engine based on the predicted value.

22. The apparatus of claim 20, further comprising:

a module configured to adjust an engine operation using the predicted value when the difference between the predicted and actual values is greater than a predetermined amount; and

a module configured to determine whether a fault exists in the first hardware sensor.

23. The apparatus of claim 20, wherein the plurality of modules comprise functionally related computer program code and data.