Laser doppler vibrometer for remote assessment of structural components

• US 20030074145A1
• Filed: 05/20/2002
• Published: 04/17/2003
• Est. Priority Date: 05/11/1999
• Status: Active Grant

• Current Assignee: Georgia Tech Research Corporation (University System of Georgia)
• Original Assignee: Georgia Tech Research Corporation (University System of Georgia)
• Inventors: Harley, Ronald Gordon, Springer, Paul LeBaron III, Mahaffey, James E.
• Granted Patent: US 6,915,217 B2
• Time in Patent Office: Days
• US Class Current: 702/56
• CPC Class Codes: G01H 3/00 Measuring characteristics of vibrations by using a detector in a fluid G01H7/00, G01H9/00, G01H11/00 take precedence
• CPC Class Codes: G01H 3/04 Frequency
• CPC Class Codes: G01H 9/00 Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
• CPC Class Codes: G01M 7/00 Vibration-testing of structures; Shock-testing of structures G01M9/00 takes precedence
• CPC Class Codes: G01M 7/025 Measuring arrangements

Abstract

A method and system for remotely inspecting the integrity of a structure. This can be performed by a method creating a vibratory response in the structure from a remote location and then measuring the vibratory response of the structure remotely. Alternatively, this can be performed by a system for remotely measuring the integrity of a structure using a vehicle and an artificial neural network, where the vehicle is equipped with a vibratory response device. The vibratory response can be produced by infrasonic and audio frequencies that can be produced by at least a vehicle, motor, or sound recording. The vibratory response can be measured with a laser vibrometer or an audio recording device.

Claims

1. A method of inspecting the integrity of a structure comprising:

creating a vibratory response in said structure remotely; and

measuring the vibratory response remotely.

2. The method of claim 1, wherein said vibratory response is produced by a suite of infrasonic and audio frequencies.

3. The method of claim 2, wherein said infrasonic and audio frequencies are produced by a vehicle.

4. The method of claim 2, wherein said infrasonic and audio frequencies are produced by a motor.

5. The method of claim 2, wherein said infrasonic and audio frequencies are produced by a sound recording.

6. The method of claim 1, wherein said vibratory response is measured with a laser vibrometer.

7. The method of claim 1, wherein said vibratory response is measured with an audio recording device.

8. The method of claim 1, wherein said vibratory response is measured as vibration data.

9. The method of claim 8, wherein said vibration data is preprocessed in a way comprising:

collecting said laser vibrometer vibration data as Fast Fourier Transform data in 4 hertz increments from 0 hertz to 1600 hertz for N data sets, where said N data sets corresponds to the number of said structures measured, and broken and used for training;

dividing the frequency range into 4 hertz increments from 0 hertz to 792 hertz producing 199 data points for each data set;

taking the natural logarithm of said 199 data points of each data set;

normalizing said 199 data points by dividing said 199 data points by the largest data point value of that particular data set for each data set;

transforming said 199 data points of each data set into a 199 point row vector;

concatenating said row vectors into one single N by 199 matrix; and

saving said matrix in a format suitable to present to the artificial neural network.

10. The method of claim 8, wherein said vibration data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

11. The method of claim 8, wherein said vibration data is evaluated with an artificial neural network.

12. The method of claim 11, wherein said artificial neural network is a feed-forward artificial neural network.

13. The method of claim 11, wherein said artificial neural network is a self-organizing map artificial neural network.

14. The method of claim 1, wherein said structure comprises a power pole cross-arm.

15. The method of claim 1, wherein the said structure can be coated with a reflecting material.

16. A method for evaluating the integrity of a structure comprising:

measuring vibratory response in said structure remotely; and

evaluating said excitation with an artificial neural network.

17. The method of claim 16, wherein said vibratory response is measured with a laser vibrometer.

18. The method of claim 16, wherein said vibratory response is measured with an audio recording device.

19. The method of claim 16, wherein said vibratory response is measured as vibration data

20. The method of claim 19, wherein said vibration data is preprocessed in a way comprising:

collecting said laser vibrometer vibration data as Fast Fourier Transform data in 4 hertz increments from 0 hertz to 1600 hertz for N data sets, where said N data sets corresponds to the number of said structures measured, and broken and used for training;

dividing the frequency range into 4 hertz increments from 0 hertz to 792 hertz producing 199 data points for each data set;

taking the natural logarithm of said 199 data points of each data set;

normalizing said 199 data points by dividing said 199 data points by the largest data point value of that particular data set for each data set;

transforming said 199 data points of each data set into a 199 point row vector;

concatenating said row vectors into one single N by 199 matrix; and

saving said matrix in a format suitable to present to the artificial neural network.

21. The method of claim 19, wherein said vibration data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

22. The method of claim 16, wherein said artificial neural network is a feed-forward artificial neural network.

23. The method of claim 16, wherein said artificial neural network is a self-organizing map

24. The method of claim 16, wherein said structure comprises a power pole cross-arm.

25. The method of claim 16, wherein the said structure can be coated with a reflecting material.

26. A method of remotely inspecting the integrity of a structure comprising:

creating infrasonic and audio frequencies;

producing a vibratory response in said structure using said frequencies;

measuring said vibratory excitation; and

determining said structural integrity using an artificial neural network.

27. The method of claim 26, wherein said infrasonic and audio frequencies are a semi-random, broad-band suite of audio frequencies.

28. The method of claim 26, wherein said creator of infrasonic and audio frequencies comprises a vehicle.

29. The method of claim 26, wherein said creator of infrasonic and audio frequencies comprises a motor.

30. The method of claim 26, wherein said creator of infrasonic and audio frequencies comprises the playing of a sound recording of infrasonic and audio frequencies.

31. The method of claim 26, wherein said vibratory response is measured with a laser vibrometer.

32. The method of claim 26, wherein said vibratory response is measured with an audio recording device.

33. The method of claim 26, wherein said vibratory response is measured as vibration data.

34. The method of claim 33, wherein said vibration data is preprocessed in a way comprising:

collecting said laser vibrometer vibration data as Fast Fourier Transform data in 4 hertz increments from 0 hertz to 1600 hertz for N data sets, where said N data sets corresponds to the number of said structures measured;

dividing the frequency range into 4 hertz increments from 0 hertz to 792 hertz producing 199 data points for each data set;

taking the natural logarithm of said 199 data points of each data set;

normalizing said 199 data points by dividing said 199 data points by the largest data point value of that particular data set for each data set;

transforming said 199 data points of each data set into a 199 point row vector;

concatenating said row vectors into one single N by 199 matrix; and

saving said matrix in a format suitable to present to the artificial neural network.

35. The method of claim 33, wherein said vibration data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

36. The method of claim 26, wherein said artificial neural network is a feed-forward artificial neural network.

37. The method of claim 26, wherein said artificial neural network is a self-organizing map artificial neural network.

38. The method of claim 26, wherein said structure comprises a power pole cross-arm.

39. The method of claim 26, wherein the said structure can be coated with a reflecting material.

40. A system for remotely measuring the integrity of a structure comprising:

a vehicle, wherein said vehicle comprises a vibratory response measuring device; and

a neural network.

41. The system of claim 40, wherein said vehicle comprises an aircraft.

42. The system of claim 40, wherein said vehicle comprises an automobile.

43. The system of claim 40, wherein said structure is vibratorily excited by an audio frequency.

44. The system of claim 40, wherein said audio frequency is produced by said vehicle.

45. The system of claim 40, wherein said audio frequency is produced by a motor.

46. The system of claim 40, wherein said audio frequency is produced from a sound recording.

47. The system of claim 40, wherein said infrasonic and audio frequency comprises a semi-random, broad-band suite of audio frequencies.

48. The system of claim 40, wherein said vibratory measuring device is a laser vibrometer.

49. The system of claim 40, wherein said vibratory measuring device is a audio recording device.

50. The system of claim 40, wherein said vibratory response is measured as vibration data.

51. The system of claim 50, wherein said vibration data is preprocessed in a way comprising:

collecting said laser vibrometer vibration data as Fast Fourier Transform data in 4 hertz increments from 0 hertz to 1600 hertz for N data sets, where said N data sets corresponds to the number of said structures measured, and broken and used for training;

dividing the frequency range into 4 hertz increments from 0 hertz to 792 hertz producing 199 data points for each data set;

taking the natural logarithm of said 199 data points of each data set;

normalizing said 199 data points by dividing said 199 data points by the largest data point value of that particular data set for each data set;

transforming said 199 data points of each data set into a 199 point row vector;

concatenating said row vectors into one single N by 199 matrix; and

saving said matrix in a format suitable to present to the artificial network.

52. The system of claim 51, wherein said data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

53. The system of claim 40, wherein said artificial neural network is a feed-forward artificial neural network.

54. The system of claim 40, wherein said artificial neural network is a self-organizing map artificial neural network.

55. A system for remotely measuring the integrity of a structure comprising:

a vehicle, wherein said vehicle produces an audio frequency that causes a vibratory response in said structure and wherein said vehicle comprises a vibratory response measuring device; and

a neural network, wherein said neural network evaluates said vibratory excitation.

56. The system of claim 55, wherein said vehicle comprises an aircraft.

57. The system of claim 55, wherein said vehicle comprises an automobile.

58. The system of claim 55, wherein said audio frequency comprises a semi-random, broad-band suite of audio frequencies.

59. The system of claim 55, wherein said vibratory measuring device is a laser vibrometer.

60. The system of claim 55, wherein said vibratory measuring device is an audio recording device.

61. The system of claim 55, wherein said vibratory response is measured as vibration data.

62. The method of claim 61, wherein said vibration data is preprocessed in a way comprising:

collecting said laser vibrometer vibration data as Fast Fourier Transform data in 4 hertz increments from 0 hertz to 1600 hertz for N data sets, where said N data sets corresponds to the number of said structures measured, and broken and used for training;

dividing the frequency range into 4 hertz increments from 0 hertz to 792 hertz producing 199 data points for each data set;

taking the natural logarithm of said 199 data points of each data set;

normalizing said 199 data points by dividing said 199 data points by the largest data point value of that particular data set for each data set;

transforming said 199 data points of each data set into a 199 point row vector;

concatenating said row vectors into one single N by 199 matrix; and

saving said matrix in a format suitable to present to the artificial neural network.

63. The method of claim 62, wherein said data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

64. The system of claim 62, wherein said artificial neural network is a feed-forward artificial neural network.

65. The system of claim 62, wherein said artificial neural network is a self-organizing map artificial neural network.