Presentation is loading. Please wait.

Presentation is loading. Please wait.

Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous multi-sensor data fusion using geometric transformations and Parzen windows for the NDE.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous multi-sensor data fusion using geometric transformations and Parzen windows for the NDE."— Presentation transcript:

1 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous multi-sensor data fusion using geometric transformations and Parzen windows for the NDE of gas transmission pipelines By: Joseph Oagaro Advisor: Dr. Shreekanth Mandayam Committee: Dr. John Chen, Dr. Robi Polikar Dr. John Schmalzel MS Thesis Defense August 27, 2004

2 Oagaro Thesis Defense ECE Dept./Rowan UniversityOutline Introduction Background Approach Implementation & Results Conclusions

3 Oagaro Thesis Defense ECE Dept./Rowan UniversityOutline Introduction –NDE of Gas Transmission Pipelines –Data Fusion –Research Objectives & Scope Background Approach Implementation & Results Conclusions

4 Oagaro Thesis Defense ECE Dept./Rowan University Gas Transmission Pipelines Sleeve Weld Corrosion SCC T-section Valve

5 Oagaro Thesis Defense ECE Dept./Rowan University Gas Transmission Pipeline Indications Benign –T-sections –Welds –Valves –Taps –Straps –Sleeves –Transitions Anomalies –Stress Corrosion Cracking –Pitting –Arching –Mechanical Damage

6 Oagaro Thesis Defense ECE Dept./Rowan University Practical Problems Found In Current Practice MFL inspection tool – the “Pig” Corrosion defect geometry is not accurately determined –+/- 15% of pipe-wall thickness error –20% of measurements not accurate When and where to dig?

7 Oagaro Thesis Defense ECE Dept./Rowan University Nondestructive Evaluation (NDE) Test Specimen E 1, in E 1, out Anomaly

8 Oagaro Thesis Defense ECE Dept./Rowan University NDE using Multiple Inspection Modalities E 2, out E 1, in Test Specimen Anomaly E 2, in E 3, in E 1, out E 3, out

9 Oagaro Thesis Defense ECE Dept./Rowan University Data Fusion NDE Inspection Signature 1 NDE Inspection Signature 2 Data Fusion Process Fused Data

10 Oagaro Thesis Defense ECE Dept./Rowan University Defect Characterization: Types of Information

11 Oagaro Thesis Defense ECE Dept./Rowan University Research Objectives The design and development of geometric transformation based data fusion algorithms for the prediction of specific information fusion measures – redundancy and complementarity The application of the data fusion algorithms to accurately and confidently predict the varying depth profile of surface-breaking pipe wall defects in a gas transmission pipeline The demonstration of the algorithms ability to fuse data from multiple homogeneous and heterogeneous sensors The design and development of experimental validation test platforms and protocols for measuring the efficacy of the data fusion techniques.

12 Oagaro Thesis Defense ECE Dept./Rowan UniversityOutline Introduction Background –Previous Work Approach Implementation & Results Conclusions

13 Oagaro Thesis Defense ECE Dept./Rowan University Previous Work in Data Fusion M.Mina, J. Yim, S. Udpa, L. Udpa, et. al., “Two- dimensional multi-frequency eddy current data fusion,” 1996 Data fusion to combine multi-frequency eddy current images of same specimen X. E. Gros, J. Bousigue and K. Takahashi, “NDT data fusion at pixel level,” 1999 Data fusion of eddy current and infrared thermographic images using weighted averaging, Bayesian analysis, and Dempster Shafer among other techniques D. Horn and W. R. Mayo, “NDE reliability gains from combining eddy- current and ultrasonic testing,” 2000 Bayesian analysis and Dempster Shafer to combine ultrasound and eddy current images of Zr-Nb pressure tube specimens with manufactured defects S. Gautier, B. Lavayssiere, E. Fleuet and J. Idier, “Using complementary types of data for 3D flaw imaging,” 1998 Used Dempster Shafer and Bayesian inference to fuse X-ray and ultrasound images to reveal complementarities J. Yim, “Image Fusion Using Multi-resolution decomposition and LMMSE filter,” 1995 Proved ANN are viable method for data fusion. Used MLP and RBF to combine eddy current and ultrasound images P.J. Kulick, J. Oagaro, Min Kim, et. Al., “A multi- sensor data fusion system for assessing the integrity of gas transmission pipelines,” 2004 Extraction of redundant and complementary information using RBF neural networks from ultrasound, MFL, and thermal imaging data

14 Oagaro Thesis Defense ECE Dept./Rowan University Previous Work A data fusion algorithm with the ability to identify redundant and complementary information present in homogeneous binary NDE signatures Algorithm utilized geometric transformations with radial basis function (RBF) neural networks Algorithm tested on canonical images and multiple combinations of pairs of NDE data sets including UT, MFL, and thermal imaging.

15 Oagaro Thesis Defense ECE Dept./Rowan University Previous Results Specimen Ultrasonic MFL Thermal RedundantComplementary Binary Images

16 Oagaro Thesis Defense ECE Dept./Rowan University Proposed Work Specimen Ultrasonic MFL Thermal RedundantComplementary Multi-level Images

17 Oagaro Thesis Defense ECE Dept./Rowan University Proposed Work Specimen Ultrasonic AE data Redundant Complementary Heterogeneous Data Fusion

18 Oagaro Thesis Defense ECE Dept./Rowan University Improvements to Previous Work Adapt algorithm to function with multi-level NDE signatures –Predict redundant and complementary information of defects shape and location –Identify and predict depth of defect Modify algorithm to perform data fusion on heterogeneous NDE signatures –Provide combinations of data varying in dimension and information Test algorithm on experimental NDE data from UT, MFL, Thermal imaging, and AE

19 Oagaro Thesis Defense ECE Dept./Rowan UniversityOutline Introduction Background Approach –Definition of Redundant and Complementary Information –Data Transformation using Parzen windows –Geometric Transformations Implementation & Results Conclusions

20 Oagaro Thesis Defense ECE Dept./Rowan University Dataset 1 (NDE Image) Dataset 2 (NDE (x,y) points) NDE Signatures Parzen Windows Density Estimation Information 2 Information 1 Equal Dimensionality Defect depth information related to pixel intensity and location Redundant & Complementary Information Definition Geometric Transformation Using RBF Redundant Information Complementary Information

21 Oagaro Thesis Defense ECE Dept./Rowan University Defect Characterization: Redundant and Complementary Information Definitions Defect Characterization: Redundant and Complementary Information Definitions Defect Profile Method 1 NDE Signature Method 2 NDE Signature Redundant Information Complementary Information

22 Oagaro Thesis Defense ECE Dept./Rowan University Dataset 1 (NDE Image) Dataset 2 (NDE Vector) NDE Signatures Parzen Windows Density Estimation Information 2 Information 1 Equal Dimensionality Defect depth information related to pixel intensity and location Redundant & Complementary Information Definition Geometric Transformation Using RBF Redundant Information Complementary Information

23 Oagaro Thesis Defense ECE Dept./Rowan University All kinds of NDE data 2-D Image Data –Information of defect: geometry, depth, and location 1-D Scatter Point Data –Defect location information only UT MFL Thermal AE

24 Oagaro Thesis Defense ECE Dept./Rowan University Parzen Windows Density estimation: p(x) –Counts number of samples that fall into region, with region reducing in size as number of points increases The density p(x) is estimated by the superposition of Gaussians, where each Gaussian is centered at a data point instance.

25 Oagaro Thesis Defense ECE Dept./Rowan University Parzen Windows k: number of samples that fall in region (window function) n: total number of samples V: volume of region φ: window function (Gaussian) h: width of window function (variance of Gaussian) x: point at which region is centered

26 Oagaro Thesis Defense ECE Dept./Rowan University Parzen Windows 2-D Density Image Scattered AE Data Points Density Estimation

27 Oagaro Thesis Defense ECE Dept./Rowan University Dimensionality Transformation using Parzen Windows Scatter Plot of AE Data separated into clusters AE Density Images

28 Oagaro Thesis Defense ECE Dept./Rowan University Dimensionality Transformation using Parzen Windows Defect Region Estimate Cluster Areas from Parzen windows

29 Oagaro Thesis Defense ECE Dept./Rowan University Dataset 1 (NDE Image) Dataset 2 (NDE Vector) NDE Signatures Parzen Windows Density Estimation Information 2 Information 1 Equal Dimensionality Defect depth information related to pixel intensity and location Redundant & Complementary Information Definition Geometric Transformation Using RBF Redundant Information Complementary Information

30 Oagaro Thesis Defense ECE Dept./Rowan UniversityApproach Geometric Transformation Feature x 1 Feature x 2 Redundant/ Complementary Information OBJECT

31 Oagaro Thesis Defense ECE Dept./Rowan University Geometric Transformations Spatial Transformation

32 Oagaro Thesis Defense ECE Dept./Rowan University Geometric Transformations Gray-level Interpolation

33 Oagaro Thesis Defense ECE Dept./Rowan UniversityApproach Geometric Transformation Feature x 1 Feature x 2 Redundant/ Complementary Information g 2 (x 2 ) Θ g 1 -1 (x 1, x 2 ) = h homomorphic operator OBJECT

34 Oagaro Thesis Defense ECE Dept./Rowan UniversityApproach Redundant Data Extraction  Train RBF (homomorphic operator  +) g 1 (x 1, x 2 ) = g 2 (x 2 ) – h 1 x1x1 x 2 - h 1 RBF Neural Network x2x2

35 Oagaro Thesis Defense ECE Dept./Rowan UniversityApproach Redundant Data Extraction  Test RBF h 1 = x 2 – g 1 (x 1, x 2 ) x1x1 x 2 - h 1 RBF Neural Network x2x2 h1h1 - + Σ x2x2

36 Oagaro Thesis Defense ECE Dept./Rowan UniversityOutline Introduction Background Approach Implementation & Results –Test specimen suite –Multi-sensor integration –Homogeneous Data Fusion –Heterogeneous Data Fusion Conclusions

37 Oagaro Thesis Defense ECE Dept./Rowan University Test Specimen Suite 1 Specimen #Plate thickness (in)IndicationDefect Depth (in) 000.5NoneN/A 010.5Pitting0.3005 020.5Pitting0.198 030.5Pitting0.0945 100.375NoneN/A 110.375Pitting0.298 120.375Pitting0.199 130.375Pitting0.1105 200.3125NoneN/A 210.3125Pitting0.303 220.3125Pitting0.1955 230.3125 Pitting 0.0995

38 Oagaro Thesis Defense ECE Dept./Rowan University Test Specimen Suite 2 Specimen #TypePlate Thickness (in)IndicationCrack Depth (in) Uni08Uniaxial0.5SCC0.08 Uni16Uniaxial0.5SCC0.16 Uni32Uniaxial0.5SCC0.32 Bi08Biaxial0.5SCC0.08 Bi16Biaxial0.5SCC0.16 Bi32Biaxial0.5SCC0.32 SA-516 grade 70 Pipeline Steel Uniaxial and Biaxial Loading: simulates axial and hoop stress of pipeline

39 Oagaro Thesis Defense ECE Dept./Rowan University Experimental Setup: UT Immersion Tank Test Specimen Ultrasound Transducer Stepper Motors Linear Actuators

40 Oagaro Thesis Defense ECE Dept./Rowan University UT Inspection Parameters 10 MHz Piezoelectric Transducer Pulse Echo Testing C-scan images of Time of Flight (TOF) and amplitude data

41 Oagaro Thesis Defense ECE Dept./Rowan University UT TOF Images: Dataset 1, Suite 1 ½” 3/8” 5/16” No Defect 0.3” Depth 0.2” Depth 0.1” Depth

42 Oagaro Thesis Defense ECE Dept./Rowan University UT TOF Images: Dataset 2, Suite 1 ½” 3/8” 5/16” No Defect 0.3” Depth 0.2” Depth 0.1” Depth

43 Oagaro Thesis Defense ECE Dept./Rowan University UT Amplitude Images: Suite 2 0.08” Depth Uniaxial Biaxial 0.32” Depth 0.16” Depth

44 Oagaro Thesis Defense ECE Dept./Rowan University Experimental Setup: MFL Gaussmeter Hall Effect Probe Current Leads Linear Actuator Test Specimen

45 Oagaro Thesis Defense ECE Dept./Rowan University MFL Inspection Parameters Induced magnetic field due to applied current of 200 A Gaussmeter and Hall probe used to measure flux leakage at defect Tangential or y component of flux density is measured and imaged

46 Oagaro Thesis Defense ECE Dept./Rowan University MFL Tangential Images: Dataset 1, Suite 1 ½” 3/8” 5/16” No Defect 0.3” Depth 0.2” Depth 0.1” Depth

47 Oagaro Thesis Defense ECE Dept./Rowan University MFL Tangential Images: Dataset 2, Suite 1 ½” 3/8” 5/16” No Defect 0.3” Depth 0.2” Depth 0.1” Depth

48 Oagaro Thesis Defense ECE Dept./Rowan University Experimental Setup: Thermal Imaging Test Specimen Infrared Camera Heat Source

49 Oagaro Thesis Defense ECE Dept./Rowan University Thermal Imaging Inspection Parameters Heat source: Two 110 Watt Halogen Lamps directed at defect for 10 seconds Highly sensitive infrared camera captures heat distribution throughout specimen – 1 image per second for 20 seconds Images represent temperature distribution throughout specimen

50 Oagaro Thesis Defense ECE Dept./Rowan University Thermal Images: Dataset 1, Suite 1 ½” 3/8” 5/16” No Defect 0.3” Depth 0.2” Depth 0.1” Depth

51 Oagaro Thesis Defense ECE Dept./Rowan University Thermal Images: Dataset 2, Suite 1 ½” 3/8” 5/16” No Defect 0.3” Depth 0.2” Depth 0.1” Depth

52 Oagaro Thesis Defense ECE Dept./Rowan University Experimental Setup: AE Loading Platform Test Specimen Test Platform Frame Hydraulic Rams Load Cells

53 Oagaro Thesis Defense ECE Dept./Rowan University Test Platform Design Criteria Design Challenges –Rigid Frame –Biaxial Loading of test specimen 30,000 psi (45,000 lbs) along Axis 1 15,000 psi (22,500 lbs) along Axis 2 Represents 1/3 maximum capacity of pipeline –Minimize noise created from testing platform –Low cost

54 Oagaro Thesis Defense ECE Dept./Rowan University AE Test Platform Design: Version 3 Frame Load Transducer Specimen Hydraulic Cylinders Specimen Clamping Bracket

55 Oagaro Thesis Defense ECE Dept./Rowan University Why Version 3? Hydraulic design –Allows for increasing max load to 30 ksi (45,000 lbs) –Controlled loading environment –Negligible noise effects with hydraulic loading New clamping brackets –Single bracket piece – minimizes noise –Designed to withstand forces exceeding maximum loading specs –1” Pinned connections to specimens Allows for movement of specimen to linearize loading Prevents deformation of specimen at connection

56 Oagaro Thesis Defense ECE Dept./Rowan University Sensor Configuration 30 ksi 15 ksi Biaxial Uniaxial Wing Sensors (6, 7, 8, 9): detect extraneous noise from test platform gripping method 30 ksi Defect

57 Oagaro Thesis Defense ECE Dept./Rowan University Located AE Data from Defect Area Uniaxial 0.08” Depth Uniaxial 0.16” Depth

58 Oagaro Thesis Defense ECE Dept./Rowan University Located AE Data from Defect Area Uniaxial 0.32” Depth Biaxial 0.08” Depth

59 Oagaro Thesis Defense ECE Dept./Rowan University Located AE Data from Defect Area Biaxial 0.16” Depth Biaxial 0.32” Depth

60 Oagaro Thesis Defense ECE Dept./Rowan University Acoustic Emission Images 0.08” Depth Uniaxial Biaxial 0.32” Depth 0.16” Depth

61 Oagaro Thesis Defense ECE Dept./Rowan University Specimen #Plate thickness (in)IndicationDefect Depth (in) 00a0.5NoneN/A 01a0.5Pitting0.3005 02a0.5Pitting0.198 03a0.5Pitting0.0945 10a0.375NoneN/A 11a0.375Pitting0.298 12a0.375Pitting0.199 13a0.375Pitting0.1105 20a0.3125NoneN/A 21a0.3125Pitting0.303 22a0.3125Pitting0.1955 23a0.3125Pitting0.0995 00b0.5NoneN/A 01b0.5Pitting0.3005 02b0.5Pitting0.198 03b0.5Pitting0.0945 10b0.375NoneN/A 11b0.375Pitting0.298 12b0.375Pitting0.199 13b0.375Pitting0.1105 20b0.3125NoneN/A 21b0.3125Pitting0.303 22b0.3125Pitting0.1955 23b0.3125Pitting0.0995 Test data Training data Trial 1: Middle thickness (3/8”) and middle defect depth (0.2”) specimen Data Fusion

62 Oagaro Thesis Defense ECE Dept./Rowan University Test data Training data Trial 2: All middle defect depth (0.2”) specimens from Dataset 2 Data Fusion Specimen #Plate thickness (in)IndicationDefect Depth (in) 00a0.5NoneN/A 01a0.5Pitting0.3005 02a0.5Pitting0.198 03a0.5Pitting0.0945 10a0.375NoneN/A 11a0.375Pitting0.298 12a0.375Pitting0.199 13a0.375Pitting0.1105 20a0.3125NoneN/A 21a0.3125Pitting0.303 22a0.3125Pitting0.1955 23a0.3125Pitting0.0995 00b0.5NoneN/A 01b0.5Pitting0.3005 02b0.5Pitting0.198 03b0.5Pitting0.0945 10b0.375NoneN/A 11b0.375Pitting0.298 12b0.375Pitting0.199 13b0.375Pitting0.1105 20b0.3125NoneN/A 21b0.3125Pitting0.303 22b0.3125Pitting0.1955 23b0.3125Pitting0.0995

63 Oagaro Thesis Defense ECE Dept./Rowan University Test data Training data Trial 3: All Middle thickness (3/8”) specimens from Dataset 2 Data Fusion Specimen #Plate thickness (in)IndicationDefect Depth (in) 00a0.5NoneN/A 01a0.5Pitting0.3005 02a0.5Pitting0.198 03a0.5Pitting0.0945 10a0.375NoneN/A 11a0.375Pitting0.298 12a0.375Pitting0.199 13a0.375Pitting0.1105 20a0.3125NoneN/A 21a0.3125Pitting0.303 22a0.3125Pitting0.1955 23a0.3125Pitting0.0995 00b0.5NoneN/A 01b0.5Pitting0.3005 02b0.5Pitting0.198 03b0.5Pitting0.0945 10b0.375NoneN/A 11b0.375Pitting0.298 12b0.375Pitting0.199 13b0.375Pitting0.1105 20b0.3125NoneN/A 21b0.3125Pitting0.303 22b0.3125Pitting0.1955 23b0.3125Pitting0.0995

64 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & UT Trial 1 Input Data Actual Output Desired Output

65 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & UT Trial 2 Input Data Actual Output Desired Output

66 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & UT Trial 2 (cont.) Input Data Actual Output Desired Output

67 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & UT Trial 3 Input Data Actual Output Desired Output

68 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & UT Trial 3 (cont.) Input Data Actual Output Desired Output

69 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: Thermal & UT Trial 1 Input Data Actual Output Desired Output

70 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: Thermal & UT Trial 2 Input Data Actual Output Desired Output

71 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: Thermal & UT Trial 2 (cont.) Input Data Actual Output Desired Output

72 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: Thermal & UT Trial 3 Input Data Actual Output Desired Output

73 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: Thermal & UT Trial 3 (cont.) Input Data Actual Output Desired Output

74 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & Thermal Trial 1 Input Data Actual Output Desired Output

75 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & Thermal Trial 2 Input Data Actual Output Desired Output

76 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & Thermal Trial 2 (cont.) Input Data Actual Output Desired Output

77 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & Thermal Trial 3 Input Data Actual Output Desired Output

78 Oagaro Thesis Defense ECE Dept./Rowan University Homogeneous Data Fusion Results: MFL & Thermal Trial 3 (cont.) Input Data Actual Output Desired Output

79 Oagaro Thesis Defense ECE Dept./Rowan University MSE of Homogeneous Combinations: Trial 1

80 Oagaro Thesis Defense ECE Dept./Rowan University MSE of Homogeneous Combinations: Trial 2

81 Oagaro Thesis Defense ECE Dept./Rowan University MSE of Homogeneous Combinations: Trial 3

82 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Specimen #Type Plate Thickness (in)Indication Crack Depth (in) Uni08Uniaxial0.5SCC0.08 Uni16Uniaxial0.5SCC0.16 Uni32Uniaxial0.5SCC0.32 Bi08Biaxial0.5SCC0.08 Bi16Biaxial0.5SCC0.16 Bi32Biaxial0.5SCC0.32 Trial 1: Uniaxial specimen with 0.16” deep defect Test data Training data

83 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Trial 2: Uniaxial and Biaxial specimens with 0.16” deep defect Test data Training data Specimen #Type Plate Thickness (in)Indication Crack Depth (in) Uni08Uniaxial0.5SCC0.08 Uni16Uniaxial0.5SCC0.16 Uni32Uniaxial0.5SCC0.32 Bi08Biaxial0.5SCC0.08 Bi16Biaxial0.5SCC0.16 Bi32Biaxial0.5SCC0.32

84 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Results: AE & UT Trial 1 Input Data Actual Output Desired Output

85 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Results: AE & UT Trial 1 (cont.) Input Data Actual Output Desired Output

86 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Results: AE & UT Trial 1 (cont.) Input Data Actual Output Desired Output

87 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Results: AE & UT Trial 2 Input Data Actual Output Desired Output

88 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Results: AE & UT Trial 2 (cont.) Input Data Actual Output Desired Output

89 Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous Data Fusion Results: AE & UT Trial 2 (cont.) Input Data Actual Output Desired Output

90 Oagaro Thesis Defense ECE Dept./Rowan University MSE of Heterogeneous Combination: AE & UT Trial 1

91 Oagaro Thesis Defense ECE Dept./Rowan University MSE of Heterogeneous Combination: AE & UT Trial 2

92 Oagaro Thesis Defense ECE Dept./Rowan University Discussion of Results Combination UT and MFL images produces the lowest MSE for both redundant and complementary information. –UT > MFL > thermal imaging > AE Least MSE was obtained in Trial 1 –maximum amount of training data present –exact opposite is true of Trial 3

93 Oagaro Thesis Defense ECE Dept./Rowan University Discussion of Results Redundant information extraction algorithm produced a lower MSE then the complementary information extraction algorithm. –Redundant training images contain more overall information then the corresponding complementary images Overall the predicted algorithm results matched the desired output values. –Average MSE = 0.0201

94 Oagaro Thesis Defense ECE Dept./Rowan UniversityOutline Introduction Background Approach Implementation & Results Conclusions –Accomplishments –Conclusions –Future Work

95 Oagaro Thesis Defense ECE Dept./Rowan UniversityAccomplishments Development of data fusion algorithms for using geometric transformations and Parzen windows density estimation techniques. Application of data fusion algorithms to predict varying defect depth profiles using combinations of UT, MFL, thermal imaging, and AE NDE data. –Average MSE value of 0.0028 and 0.0201 for the training and testing data respectively.

96 Oagaro Thesis Defense ECE Dept./Rowan UniversityAccomplishments Demonstration of the algorithms ability to fuse data from multiple homogeneous and heterogeneous sensors by combining NDE data from UT and AE. The design and development of experimental validation test platform –Biaxial loading test platform developed to obtain AE signatures –Comprehensive test specimen suite fabricated with multiple scans to create a database of approximately 500 NDE signatures

97 Oagaro Thesis Defense ECE Dept./Rowan UniversityConclusions Combination of homogeneous and heterogeneous datasets using –Geometric transformations –Radial basis function approximation –Parzen window density estimation Method provides numerical measures of algorithm effectiveness in terms of information redundancy and complementarity –Definitions of quantities can change with application –User definition shows versatility of approach –Possible future applications of multi-sensor data fusion outside the realm of gas transmission pipeline NDE.

98 Oagaro Thesis Defense ECE Dept./Rowan University Directions for Future Work Collection of additional acoustic emission data for heterogeneous data fusion –Need specimens that can be tested on AE loading platform as well as the UT, MFL, and thermal imaging test setups Adaptation to include combination of information from singular events such as –time-history, anecdotal evidence, and a priori knowledge.

99 Oagaro Thesis Defense ECE Dept./Rowan University Directions for Future Work Obtain actual pipeline data to adapt the data fusion algorithm to real world NDE signatures Apply the data fusion algorithms to applications outside the NDE realm to test its versatility –Development of a smart sensor systems. Incorporate the data fusion algorithm into the virtual reality platform –data integration and data management.

100 Oagaro Thesis Defense ECE Dept./Rowan UniversityAcknowledgements This research work is supported by the U.S. Department of Energy under grant no. DE-FC26- 02NT41648 Exxon Mobil, "Development of an Acoustic Emission Test Platform with a biaxial Stress Loading System," PERF 95-11 Physical Acoustics Corp. (PAC) – Dr. Ronnie Miller Advisor and Committee –Dr. Shreekanth Mandayam, Dr. John Chen, Dr. Robi Polikar, and Dr. John Schmalzel Fellow students: –Mike Kim, Phil Mease, Mike Possumato

101 Oagaro Thesis Defense ECE Dept./Rowan UniversityQuestions?

Download ppt "Oagaro Thesis Defense ECE Dept./Rowan University Heterogeneous multi-sensor data fusion using geometric transformations and Parzen windows for the NDE."

Similar presentations

Applications of one-class classification

Applications of one-class classification
Automatic classification of weld cracks using artificial intelligence and statistical methods Ryszard SIKORA, Piotr BANIUKIEWICZ, Marcin CARYK Szczecin.

Automatic classification of weld cracks using artificial intelligence and statistical methods Ryszard SIKORA, Piotr BANIUKIEWICZ, Marcin CARYK Szczecin.
Introduction to Nondestructive Testing

Introduction to Nondestructive Testing
Acoustic Emission Test Platform Customers: Exxon Mobil Department of Energy Team: Dan Edwards Terry Lott John Ludes Joseph Oagaro Consultant: Philip James.

Acoustic Emission Test Platform Customers: Exxon Mobil Department of Energy Team: Dan Edwards Terry Lott John Ludes Joseph Oagaro Consultant: Philip James.
Steven Chase 1, Yaw Adu-Gyamfi 2 and Paul Fuchs 3 1&2 Department of Civil Engineering, University of Virginia 3 Fuchs Consulting Inc.

Steven Chase 1, Yaw Adu-Gyamfi 2 and Paul Fuchs 3 1&2 Department of Civil Engineering, University of Virginia 3 Fuchs Consulting Inc.
Neural Network Approach to Modeling the Laser Material-Removal Process By Basem. F. Yousef London, Canada, N6A 5B9 December 2001.

Neural Network Approach to Modeling the Laser Material-Removal Process By Basem. F. Yousef London, Canada, N6A 5B9 December 2001.
“ULTRASONIC SYSTEM OF DISTANT DETERMINATION COORDINATES”

“ULTRASONIC SYSTEM OF DISTANT DETERMINATION COORDINATES”
Spring 2015 ECE Senior Design Topic Graduate Student Advisors

Spring 2015 ECE Senior Design Topic Graduate Student Advisors
S. Mandayam/ECE Dept./Rowan University Development of an Acoustic Emission Test Platform with a Biaxial Stress Loading System Joseph Oagaro, Shreekanth.

S. Mandayam/ECE Dept./Rowan University Development of an Acoustic Emission Test Platform with a Biaxial Stress Loading System Joseph Oagaro, Shreekanth.
Real-time Embedded Face Recognition for Smart Home Fei Zuo, Student Member, IEEE, Peter H. N. de With, Senior Member, IEEE.

Real-time Embedded Face Recognition for Smart Home Fei Zuo, Student Member, IEEE, Peter H. N. de With, Senior Member, IEEE.
MFL Signal Matrix Defect Profile Matrix True Defect Profile Defect Characterization System Fig. 1: Desired object transformation system.

MFL Signal Matrix Defect Profile Matrix True Defect Profile Defect Characterization System Fig. 1: Desired object transformation system.
1 Learning to Detect Objects in Images via a Sparse, Part-Based Representation S. Agarwal, A. Awan and D. Roth IEEE Transactions on Pattern Analysis and.

1 Learning to Detect Objects in Images via a Sparse, Part-Based Representation S. Agarwal, A. Awan and D. Roth IEEE Transactions on Pattern Analysis and.
S. Mandayam/ECE Dept./Rowan University Development of an Acoustic Emission Test Platform with a Biaxial Stress Loading System Joseph Oagaro, Shreekanth.

S. Mandayam/ECE Dept./Rowan University Development of an Acoustic Emission Test Platform with a Biaxial Stress Loading System Joseph Oagaro, Shreekanth.
S. Mandayam/ NDE/ Fall 99 Principles of Nondestructive Evaluation Shreekanth Mandayam Graduate / Senior Elective 0909-504-01/0909-413-01 Fall 1999

S. Mandayam/ NDE/ Fall 99 Principles of Nondestructive Evaluation Shreekanth Mandayam Graduate / Senior Elective / Fall 1999
S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks 0909.560.01/0909.454.01 Fall 2004 Shreekanth Mandayam ECE Department Rowan University.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks / Fall 2004 Shreekanth Mandayam ECE Department Rowan University.
High Frequency Ultrasonic Characterization of Carrot Tissue Christopher Vick Advisor: Dr. Navalgund Rao Center for Imaging Science Rochester Institute.

High Frequency Ultrasonic Characterization of Carrot Tissue Christopher Vick Advisor: Dr. Navalgund Rao Center for Imaging Science Rochester Institute.
Yujun Guo Kent State University August 13. 2003 PRESENTATION A Binarization Approach for CT-MR Registration Using Normalized Mutual Information.

Yujun Guo Kent State University August PRESENTATION A Binarization Approach for CT-MR Registration Using Normalized Mutual Information.
Multi-sensor data fusion using geometric transformations for the nondestructive evaluation of gas transmission pipelines by PJ Kulick Graduate Advisor:

Multi-sensor data fusion using geometric transformations for the nondestructive evaluation of gas transmission pipelines by PJ Kulick Graduate Advisor:
S. Mandayam/ECE Dept./Rowan University Development of an Acoustic Emission Test Platform with a Biaxial Stress Loading System Joseph Oagaro, Shreekanth.

S. Mandayam/ECE Dept./Rowan University Development of an Acoustic Emission Test Platform with a Biaxial Stress Loading System Joseph Oagaro, Shreekanth.
S. Mandayam/ NDE/ Fall 99 Principles of Nondestructive Evaluation Shreekanth Mandayam Graduate / Senior Elective 0909-504-01/0909-413-01 Fall 1999

S. Mandayam/ NDE/ Fall 99 Principles of Nondestructive Evaluation Shreekanth Mandayam Graduate / Senior Elective / Fall 1999

Similar presentations

Ads by Google