Multimodal Industrial Inspection and Analysis

Diagnosis of complex engineering systems requires the use of multiple sensor sources to acquire information. In this paper we present a survey of multimodal data acquisition systems for nondestructive testing (NDT) and engineering analysis. We begin with a summary of the relative strengths and weaknesses of individual NDT modalities. Thereafter we present existing multimodal inspection hardware systems that use complementary NDT sensors. The advantages of such multimodal data acquisition over conventional single modality sensors in inspection and analysis are highlighted. Possible approaches to fuse complementary multimodal sensor data are discussed. We conclude with possible directions for the future development of multimodal inspection systems.

[1]  X. E. Gros,et al.  Fusion of multiprobe NDT data for ROV inspection , 1995, 'Challenges of Our Changing Global Environment'. Conference Proceedings. OCEANS '95 MTS/IEEE.

[2]  Chia-Hsiang Menq,et al.  Multiple-sensor integration for rapid and high-precision coordinate metrology , 2000 .

[3]  Tadeusz Stepinski,et al.  Automatic detecting and classifying defects during Eddy current inspection of riveted lap-joints , 2000 .

[4]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[5]  Dominique Placko,et al.  Localization of defects in steam generator tubes using a multi-coil eddy current probe dedicated to high speed inspection , 2002 .

[6]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[7]  Berthold K. P. Horn Extended Gaussian images , 1984, Proceedings of the IEEE.

[8]  Jérôme Idier,et al.  X-ray and ultrasound data fusion , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[9]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[10]  D Horn,et al.  NDE reliability gains from combining eddy-current and ultrasonic testing , 2000 .

[11]  Belur V. Dasarathy Industrial applications of multi-sensor multi-source information fusion , 2000, Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482).

[12]  Haim J. Wolfson,et al.  Geometric hashing: an overview , 1997 .

[13]  Gregg Podnar,et al.  Image understanding algorithms for remote visual inspection of aircraft surfaces , 1997, Electronic Imaging.

[14]  X. E. Gros,et al.  NDT data fusion at pixel level , 1999 .

[15]  Marc Levoy,et al.  Fitting smooth surfaces to dense polygon meshes , 1996, SIGGRAPH.

[16]  Pramod K. Varshney Multisensor data fusion , 1997 .

[17]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[18]  Gérard G. Medioni,et al.  Structural Indexing: Efficient 3-D Object Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Matthias Eck,et al.  Automatic reconstruction of B-spline surfaces of arbitrary topological type , 1996, SIGGRAPH.

[20]  Mahmood Fateh,et al.  Automatic defect classification in long-range ultrasonic rail inspection using a support vector machine-based ?smart system? , 2004 .

[21]  Chia-Hsiang Menq,et al.  Multiple-Sensor Planning and Information Integration for Automatic Coordinate Metrology , 2001, J. Comput. Inf. Sci. Eng..

[22]  Joseph N. Wilson,et al.  Handbook of computer vision algorithms in image algebra , 1996 .

[23]  Kyungmi Lee,et al.  Classification of Ultrasonic Shaft Inspection Data Using Discrete Wavelet Transform , 2003 .

[24]  X. E. Gross NDT Data Fusion , 1997 .

[25]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Xavier E. Gros Applications of NDT Data Fusion , 2001 .