Structural health monitoring using active members and neural networks

• US 5,774,376 A
• Filed: 08/07/1995
• Issued: 06/30/1998
• Est. Priority Date: 08/07/1995
• Status: Expired due to Term

• Current Assignee: Northrop Grumman Systems Corporation (Northrop Grumman Corporation)
• Original Assignee: TRW Limited (Zeppelin - Stiftung der Stadt Friedrichshafen)
• Inventors: Manning, Raymund A.
• Primary Examiner(s): Trammell, James P.
• Assistant Examiner(s): PEESO, THOMAS R
• Application Number:
  US08/592,747
• Time in Patent Office: 1,058 Days
• Field of Search: 364/505, 364/508, 395/20, 395/21, 395/23, 395/24, 395/183.13, 73/35.09, 73/35.11, 73/570, 73/576, 73/577, 73/598
• US Class Current: 702/56
• CPC Class Codes: E02B 17/0034 Maintenance, repair or inspection of offshore constructions

Abstract

A system for monitoring the structural integrity of a mechanical structure. The system utilizes a trainable adaptive interpreter such as a neural network to analyze data from the structure to characterize the structure'"'"'s health. An actuator is attached to the mechanical structure for generating vibrations in response to an input signal. A sensor, also attached to the mechanical structure, senses the vibrations and generates an output signal in response thereto. The sensor output signal is then coupled to a pre-trained adaptive interpreter for generating an output which characterizes the structural integrity of the mechanical structure. The system can provide continual health monitoring of a structural system to detect structural damage and pinpoint probable location of the damage. The system can operate while the structural system is in service there by significantly reducing structural inspection costs.

Claims

1. A system for monitoring structural integrity, said system comprising:

a mechanical structure;

an actuator attached to said mechanical structure for generating vibrations in said structure in response to an input signal;

means for generating said input signal;

a sensor attached to said mechanical structure member for sensing said vibrations and generating an output signal in response thereto; and

trainable adaptive interpreter means coupled to said sensor for receiving said sensor output and generating an output which characterizes the structural integrity of said mechanical structure, said characterized structural integrity being indicative of damage to said mechanical structure.

2. The system of claim 1 wherein said sensor means comprises a plurality of sensors located at a plurality of regions in said mechanical structure and said trainable adaptive interpreter means output characterizes the structure at each of said regions.

3. The system of claim 1 further comprising a preprocessor means coupled to said sensor for analyzing the sensor output signals, wherein said preprocessor means includes means for determining the poles and zeros of a transfer function of said mechanical structure and said poles and zeros are used as input to said adaptive interpreter.

4. The system of claim 3 wherein said adaptive interpreter is a neural network.

5. The system of claim 4 further comprising means for training said neural network, wherein said poles and zeros from an undamaged mechanical structure are used as training input by said means for training said neural network.

6. The system of claim 5 wherein said mechanical structure has at least one structural member and said means for training said neural network trains said neural network to produce an output that is proportional to the cross-sectional area of said structural member.

7. The system of claim 6 wherein said means for training further uses poles and zeros from structural member having changed stiffness.

8. The system of claim 1 wherein said actuator and sensors are piezoelectric.

9. The system of claim 4 wherein said neural network is a back propagation neural network.

10. A method for monitoring structural integrity of a mechanical structure, said method comprising:

generating an input signal;

generating vibrations in said structure in response to an input signal;

sensing said vibrations;

generating an output signal in response to said vibrations;

thereafter, receiving said sensor output in a trainable adaptive interpreter; and

generating an output which characterizes the structural integrity of said mechanical structure, said characterized structural integrity being indicative of damage to said mechanical structure.

11. The method of claim 10 further comprising the steps of:

locating a plurality of sensors at a plurality of regions in said mechanical structure; and

generating an output by said trainable adaptive interpreter which characterizes the structure at each of said regions.

12. The method of claim 10 further comprising the step of determining the poles and zeros of a transfer function of said mechanical structure and using said poles and zeros as input to said adaptive interpreter.

13. The method of claim 12 further comprising the step of training said adaptive interpreter by u sing poles and zeros representative of an undamaged mechanical structure as training input.

14. The method of claim 10 wherein said step of training includes the step of training said adaptive interpreter to produce an output that is proportional to the cross-sectional area of a member of mechanical structure.

15. A system for detecting the existence of structural damage in a mechanical structure, said system comprising:

a mechanical structure having at least one structural member with a cross-sectional area;

an actuator attached to said structural member for generating vibrations in said structure in response to an input signal;

means for generating said input signal to said actuator;

a sensor attached to said mechanical structure member for sensing said vibrations and generating an output signal in response thereto;

a neural network coupled to said sensor for receiving said sensor output and generating an output which characterizes the structural integrity of said mechanical structure said characterized structural integrity being indicative of damage to said mechanical structure, and said output being related to the cross-sectional area of said structural member of said mechanical structure.

16. The system of claim 15 further comprising:

means for training said neural network to produce an output that is proportional to the cross-sectional area of said structural member.

17. The system of claim 16 wherein said means for training further uses poles and zeros from structural member whose stiffness has changed.

18. The system of claim 15 further comprising:

means for training said neural network, wherein said means for training said neural network said uses said poles and zeros from an undamaged mechanical structure as training input.

19. The system of claim 15 wherein said neural network is a back propagation neural network.

20. The system of claim 15 further comprising:

preprocessor means coupled to said sensor for analyzing the sensor output signals, wherein said preprocessor means includes means for determining the poles and zeros of a transfer function of said mechanical structure and said poles and zeros are used as input to said neural network.

21. A system for monitoring structural integrity, said system comprising:

a mechanical structure;

an actuator attached to said mechanical structure for generating vibrations in said structure in response to an input signal;

means for generating said input signal;

a sensor attached to said mechanical structure member for sensing said vibrations and generating an output signal in response thereto;

a neural network coupled to said sensor for receiving said sensor output and generating an output which characterizes the structural integrity of said mechanical structure;

a preprocessor means coupled to said sensor for analyzing the sensor output signals, wherein said preprocessor means includes means for determining the poles and zeros of a transfer function of said mechanical structure and said poles and zeros are used as input to said adaptive interpreter; and

means for training said neural network, wherein said poles and zeros from an undamaged mechanical structure are used as training input by said means for training said neural network,

said mechanical structure having at least one structural member and said means for training said neural network trains said neural network to produce an output that is proportional to the cross-sectional area of said structural member.

22. A system for monitoring structural integrity, said system comprising:

a mechanical structure;

an actuator attached to said mechanical structure for generating vibrations in said structure in response to an input signal;

means for generating said input signal;

a sensor attached to said mechanical structure member for sensing said vibrations and generating an output signal in response thereto;

a neural network coupled to said sensor for receiving said sensor output and generating an output which characterizes the structural integrity of said mechanical structure, said neural network being a back propagation neural network; and

a preprocessor means coupled to said sensor for analyzing the sensor output signals, wherein said preprocessor means includes means for determining the poles and zeros of a transfer function of said mechanical structure and said poles and zeros are used as input to said adaptive interpreter.

23. A method for monitoring structural integrity of a mechanical structure which has a cross-sectional area, said method comprising:

generating an input signal;

generating vibrations in said structure in response to an input signal;

sensing said vibrations;

generating an output signal in response to said vibrations;

training an adaptive interpreter to produce an output that is proportional to the cross-sectional area of a member of said mechanical structure;

receiving said sensor output in said trainable adaptive interpreter; and

generating an output which characterizes the structural integrity of said mechanical structure.