Laser doppler vibrometer for remote assessment of structural components

• US 6,915,217 B2
• Filed: 05/20/2002
• Issued: 07/05/2005
• Est. Priority Date: 05/11/1999
• Status: Expired due to Fees

• Current Assignee: Georgia Tech Research Corporation (University System of Georgia)
• Original Assignee: Georgia Tech Research Corporation (University System of Georgia)
• Inventors: Springer, Paul LeBaron III, Mahaffey, James E., Harley, Ronald Gordon
• Primary Examiner(s): Barlow, John
• Assistant Examiner(s): Washburn, Douglas N
• Application Number:
  US10/151,438
• Publication Number:
  US 20030074145A1
• Time in Patent Office: 1,142 Days
• Field of Search: 735/84, 735/97, 736/72, 737/69, 737/80, 702/39, 702/40, 702/50
• US Class Current: 702/40
• 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 at a location remote from said structure, wherein said vibratory repsonse is produced by a suite of infrasonic and audio frequencies, and wherein said infrasonic and audio frequencies are produced by a motor;

and measuring the vibratory repsonse at a location remote from said structure.

2. The method of claim 1, wherein said infrasonic and audio frequencies are produced by a vehicle including the motor.

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

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

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

6. The method of claim 5, wherein said vibration data is preprocessed in a way comprising:

collecting said 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 frequenc 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 format suitable to present to the artificial neural network.

7. The method of claim 5, wherein said vibration data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

8. The method of claim 5, wherein said vibration data is evaluated with an artificial neural network.

9. The method of claim 8, wherein said artificial neural network is a feed-forward artificial neural network.

10. The method of claim 8, wherein said artificial neural network is a self-organizing map artificial neural network.

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

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

13. The method of claim 1, wherein the vehicle is selected from an aircraft and an automobile.

19. The method of claim 7, wherein said vibration data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

14. A method for evaluation the integrity of a structure comprising:

measuring a vibratory response in a structure, wherein the measurement is performed remotely from a vehicle;

and evaluation said response with an artifical neural network.

15. The method of claim 14, wherein said vibratory response is measured with a laser vibrometer.

16. The method of claim 14, wherein said vibratory response is measured with an audio recording device.

17. The method of claim 14, wherein said vibratory response is measured as vibration data.

18. The method of claim 17, wherein said vibration data is preprocessed in a way comprising:

collecting said 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 format suitable to present to the artificial neural network.

20. The method of claim 14, wherein said artificial neural network is a feed-forward artificial neural network.

21. The method of claim 14, wherein said artificial neural network is a self-organizing map.

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

23. The method of claim 14, wherein the said structure can be coated with a reflecting material.

24. The method of claim 16, wherein the vehicle is selected from an aircraft and an automobile.

25. A method of remotely inspecting the intergrity of a structure comprising:

creating infrasonic and audio frequencies at a location remote from said structure;

producing a vibratory response in said structure using said frequencies;

measuring said vibratory response from a vehicle at a location remote from said structure;

and determining said structural integrity using an artificial neural network.

26. The method of claim 25, wherein said infrasonic and audio frequencies are a semi-random, broad-band suit of audio frequencies.

27. The method of claim 25, wherein a creator of infrasonic and audio frequencies comprises a vehicle.

28. The method of claim 25, wherein a creator of infrasonic and audio frequencies comprises a motor.

29. The method of claim 25, wherein a creator of infrasonic and audio frequencies comprises the plaaying of a sound recording of infrasonic and audio frequencies.

30. The method of claim 25, wherein said vibratory response is measured with a laser vibrometer.

31. The method of claim 25, wherein said vibratory response is measured with an audio recording device.

32. The method of claim 25, wherein said vibratory response is measured as vibration data.

33. The method of claim 32, wherein said vibration data is preprocessed in a way comprising:

collecting said 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 ta 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 format suitable to present to the artificial neural network.

34. The method of claim 32, wherein said vibration data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

35. The method of claim 25, wherein said artificial neural network is a feed-forward artificial neural network.

36. The method of claim 25, wherein said artificial neural network is a self-organizing map artificial neural network.

37. The method of claim 25, wherein said structure comprises a power pole cross-arm.

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

39. The method of claim 25, wherein the vehicle is selected from an aircraft and an automobile.

40. The method of claim 27, wherein the vehicle is selected from an aircraft and an automobile.

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

creating a vibratory response in a structure remotely from said structure, wherein said structure comprises a power pole cross-arm;

a vehicle, wherein said vehicle comprises an aircraft comprising a vibratory response measuring device, and wherein said vibratory response measuring device is a laser vibrometer.

42. The system of claim 41, wherein said structure is vibratorily excited by an audio frequency.

43. The system of claim 42, wherein said audio frequency is produced by said vehicle.

44. The system of claim 42, wherein said audio frequency is produced by a motor.

45. The system of claim 42, wherein said audio frequency is produced from a sound recording.

46. The system of claim 42, wherein said audio frequency comprises a semi-random, broad-band suite of frequencies.

47. The system of claim 41, wherein said vibratory response is measured as vibration data.

48. The system of claim 47, wherein said vibration data is preprocessed in a way comprising:

collecting said vibration data 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 to 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.

49. The system of claim 48, wherein said data set comprises 200 data points, where the 200^th data point is actual breaking strength of said structure.

50. The system of claim 41, further comprising a artificial neural network.

51. The system of claim 41, wherein said artificial neural network is chosen from a self-organizing ap artificial neural network and a feed-forward artificial neural network.

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

creating a vibratory response in a structure remotely from said structure, wherein said structure comprises a power pole cross-arm;

a vehicle, wherein said vehicle produces an audio frequency that causes a vibratory response in said structure, wherein said vehicle comprises and aircraft and wherein said vibratory response measuring device is a laser vibrometer.

53. The system of claim 52, wherein said vibratory response is measured as vibration data.

54. The system of claim 53, 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 t 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 logarithim 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 format suitable to present to the artificial neural network.

55. The system of claim 54, wherein said data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

56. The system of claim 54, further comprising an artificial neural network, wherein said neural network evaluates said vibratory excitation.

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

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

measuring vibratory response in said structure remotely, wherein said vibratory response is measured as vibration data; and

evaluating said excitation with an artificial neural network, wherein said vibration data is reprocessed in a way including;

collecting said 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.

59. The method of claim 58, wherein said vibratory response is measured with a laser vibrometer.

60. The method of claim 58, wherein said vibratory response is measured with an audio recording device.

61. The method of claim 58, wherein said vibration data set compnses 200 data points, where the 200^th data point is the actual breaking strength of said structure.

62. The method of claim 58, wherein said artificial neural network is a feed-forward artificial neural network.

63. The method of claim 58, wherein said artificial neural network is a self-organizing map.

64. The method of claim 58, wherein said structure comprises a power pole cross-arm.

65. The method of claim 58, wherein the said structure can be coated with a reflecting material.

66. 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, wherein said vibratory response is measured as vibration data;

measuring said vibratory excitation; and

determining said structure integrity using an artificial neural network,

wherein said vibration data is preprocessed in a way including;

collecting said vibration data as Fast Fourier Transform data in 4 hertz increments from 0 h 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 valu of that particular data set for each data set;

transforming said 199 data points of each data set into said 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.

67. The method of claim 66, wherein said infrasonic and audio frequencies are a semi-random, road-band suite of audio frequencies.

68. The method of claim 66, wherein said creator of infrasonic and audio frequencies comprises a vehicle.

69. The method of claim 66, wherein said creator of infrasonic and audio frequencies comprises a motor.

70. The method of claim 66, wherein said creator of infrasonic and audio frequencies comprises playing of a sound recording of infrasonic and audio frequencies.

71. The method of claim 66, wherein said vibratory response is measured with a laser vibrometer.

72. The method of claim 66, wherein said vibratory response is measured with an audio recording device.

73. The method of claim 66, wherein said vibration data set comprises 200 data points, where the 200^th data point is the actual breaking strength of said structure.

74. The method of claim 66, wherein said artificial neural network is a feed-forward artificial neural network.

75. The method of claim 66, wherein said artificial neural network is a self-organizing map artificial neural network.

76. The method of claim 66, wherein said structure comprises a power pole cross-arm.

77. The method of claim 66, wherein the said structure can be coated with a reflecting material.

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

a vehicle;

a vibratory response measuring device, wherein the vibratory response measuring device is part of the vehicle;

and a neural network, wherein the neural network is part of the vibratory response measure device, and wherein the structure is vibratorily excited at a location remote from said structure using at least one of an infrasonic frequency and an audio frequency, wherein the structure produces a vibratory response that is measured remotely using a vibratory response measuring device.

79. The system of claim 78, wherein the vehicle comprises an aircraft.

80. The system of claim 78, wherein the vehicle comprises an automobile.

81. The system of claim 78, wherein the structure is vibratorily excited by an audio frequency.

82. The system of claim 78, wherein the audio frequency is produced by said vehicle.

83. The system of claim 78, wherein the audio frequency is produced by a motor.

84. The system of claim 78, wherein the audio frequency is produced from a sound recording.

85. The system of claim 78, wherein the at least one of the infrasonic and the audio frequency comprises a semi-random, broad-band suite of audio frequencies.

86. The system of claim 78, wherein the vibratory measuring device is a laser vibrometer.

87. The system of claim 78, wherein the vibratory measuring device is an audio recording device.

88. The system of claim 78, wherein a vibratory response produced by the vibratorily excited structure is measured as vibration data.

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

creating a vibratory respone in said structure at a location remote from said structure, wherein said vibratory reponse is produced by a suite of infrasonic and audio frequencies, and wherein said infrasonic and audio frequencies are produced by a sound recording;

and measuring the vibratory response at a location remote from said structure with a laser vibrometer.

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

creating a vibratory response in said structure remotely from said structure, wherein said structure comprises a power pole cross-arm;

amd wherein said infrasonic and audio frequencies are produced by a sound recording;

and measuring the vibratory response remotely from said structure with a laser vibrometer.

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

creating a vibratory response in said structure remotely, wherein said vibratory response is produced by a suite of infrasonic and audio frequencies, wherein said infrasonic and audio frequencies are produced by a motor, and wherein said structure comprises a power pole cross-arm; and

measuring the vibratory response remotely, wherein said vibratory response is measured with a laser vibrometer.