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Quaternion Based Omnidirectional Machine Condition Monitoring System
Wai Kit Wong, Chu Kiong Loo, Way Soong Lim
Pages - 145 - 165 | Revised - 01-05-2011 | Published - 31-05-2011
Published in International Journal of Image Processing (IJIP)
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KEYWORDS
Image Processing Applications, Monitoring and Surveillance, Image Processing, Machine Condition Monitoring System, Neuro Fuzzy System, Quaternion
ABSTRACT
Thermal monitoring is useful for revealing some serious electrical problems in a factory that often go undetected until a serious breakdown occurs. In factories, there are various types of functioning machines to be monitored. When there is any malfunctioning of a machine, extra heat will be generated which can be picked up by thermal camera for image processing and identification purpose. In this paper, a new and effective omnidirectional machine condition monitoring system applying log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed for monitoring machine condition in an omnidirectional view. With this monitoring system, it is convenient to detect and monitor the conditions of (overheat or not) of more than one machines in an omnidirectional view captured by using a single thermal camera. Log-polar mapping technique is used to unwarp omnidirectional thermal image into panoramic form. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. Simulation results also show that the proposed system is an efficient omnidirectional machine monitoring system with accuracy more than 97%.
1 | Bhattacharya, A., & Dan, P. K. (2014). Recent trend in condition monitoring for equipment fault diagnosis. International Journal of System Assurance Engineering and Management, 5(3), 230-244. |
A Vanderlugt, “Signal detection by complex spatial filtering”, IEEE Trans. Inf. Theory, Vol. 10, (1964) p.p.139-145. | |
A. Mahalanobis, B.V.K. Vijaya Kumar and D. Casasent, “Minimum average correlation energy filters”, Applied Optics, Vol. 26, (1987) p.p. 3633-3640. | |
A. Mahalanobis, B.V.K. Vijaya Kumar, S.R.F. Sims and J.F. Epperson, “Unconstrained correlation filters”, Applied Optics, Vol. 33, (1994) p.p. 3751-3759. | |
B.V.K. Kumar, D.W. Carlson, and A. Mahalanobis, “Optimal trade-off synthetic discriminant function filters for arbitrary devices”, Optics Letters, Vol. 19, No. 19, (1994) p.p. 1556-1558. | |
B.V.K. Vijaya Kumar, M. Savvides, K. Venkataramani and C. Xie, “Spatial frequency domain image processing for biometric recognition”, Proc. Of Int. Conf. on Image Processing, Vol.1, (2002) p.p. I53-I56. | |
C. F. R. Weiman and G. Chaikin, “Logarithmic Spiral Grids For Image Processing And Display”, Computer Graphics and Image Processing, Vol. 11, 1979, p.p. 197-226. | |
C. Xie, M. Savvides and B.V.K. Vijaya Kumar, “Quaternion correlation filters for face recognition in wavelet domain”, Int. Conf. on Accoustic, Speech and Signal Processing (ICASSP 2005):II85- II88. | |
C.G. Ho, R.C.D. Young and C.R. Chatwin, “Sensor geometry and sampling methods for space variant image processing”, Pattern Analysis and Application, Springer Verlag, (2002) p.p. 369-384. | |
C.T. Lin and C.S.G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, Upper Saddle River, N.J., 1996. | |
D. Nauck, F. Klawonn and R. Kurse, Foundations of Neuro-Fuzzy Systems, Wiley, Chichester, U.K., 1997. | |
F. Berton, A brief introduction to log-polar mapping, Technical report, LIRA-Lab, University of Genova, (Feb 2006). | |
F. Jurie, “A new log-polar mapping for space variant imaging: Application to face detection and tracking”, Pattern Recognition,Elsevier Science, 32:55, 1999, p.p. 865-875. | |
G.Z. Li and S.C. Fang, “Solving interval-valued fuzzy relation equations”, IEEE Trans. on Fuzzy Systems, Vol. 6, No. 2, (May 1998) p.p. 321-324. | |
H. Araujo, J. M. Dias, “An Introduction To The Log-polar Mapping”, Proceedings of 2nd Workshop on Cybernetic Vision, 1996, p.p. 139-144. | |
H. V. Kennedy, “Modeling noise in thermal imaging systems”, Proc. of SPIE, Vol. 1969, (1993), p.p. 66-70. | |
H.K. Kwan and Y. Cai, “A Fuzzy Neural Network and its Application to Pattern Recognition”, IEEE Trans. on Fuzzy Systems, 2(3), (1997) p.p. 185-193. | |
http://www.flirthemography.com | |
J. Leotamonphong and S. Fang, “An efficient solution procedure for fuzzy relation equations with max product composition”, IEEE Trans. on Fuzzy Systems, Vol. 7, No. 4, (Aug 1999) p.p. 441-445. | |
J. Owens, A. Hunter and E. Fletcher, “A Fast Model–Free Morphology–Based Object Tracking Algorithm”, British Machine Vision Conference, p.p. 767-776, 2002. | |
J.J. Buckley and Y. Hayashi, “Fuzzy neural networks: A survey”, Fuzzy Sets and Systems, 66, (1994) p.p. 1-13. | |
M. Bolduc and M.D. Levine, “A review of biologically-motivated space variant data reduction models for robotic vision”, Computer Vision and Image Understanding, Vol. 69, No. 2, (February 1998) p.p. 170-184. | |
M. Saviddes, K. Venkataramani and B.V.K. Vijaya Kumar, “Incremental updating of advanced correlation filters for biometric authentication systems”, Proc. of Int. Conf. on Multimedia and Expo, Vol. 3 (ICME 2003) p.p. 229-232. | |
M. Savvides, B.V.K. Vijaya Kumar and P. Khosla, “Face verification using correlations filters”, Procs of 3rd IEEE Automatic Identification Advanced Technologies, Tarrytown, N.Y., (2002) p.p. 56-61. | |
M.M. Bourke and D.G. Fisher, “A predictive fuzzy relational controller”, Proc. of the Fifth Int. Conf. on Fuzzy Systems, (1996) p.p. 1464-1470. | |
M.M. Bourke and D.G. Fisher, “Solution algorithms for fuzzy relational equations with maxproduct composition”, Fuzzy Sets Systems, Vol. 94, (1998) p.p. 61-69. | |
P. Refreiger, “Filter design for optical pattern recognition: multi-criteria optimization approach”, Optics Letters, Vol. 15, (1990) p.p. 854-856. | |
P. Xiao and Y. Yu, “Efficient learning algorithm for fuzzy max-product associative memory networks”, SPIE, Vol. 3077, (1997), p.p. 388-395. | |
R. Ostermark, “A Fuzzy Neural Network Algorithm for Multigroup Classification”, Elsevier Science, Fuzzy Sets and Systems, 105, (1999) p.p. 113-122. | |
R. Wodnicki, G.W. Roberts, and M.D. Levine, “A foveated image sensor in standard CMOS technology”, Custom Integrated Circuits Conf. Santa Clara, May 1995, p.p. 357-360. | |
R.K. Brouwer, “A fuzzy threshold max-product unit, with learning algorithm, for classification of pattern vectors”, Proc. of the VI Brazillian Symp. On Neural Networks, (Jan 2000) p.p. 208-212. | |
R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification, 2nd Ed. Wiley, N.Y, 2001. | |
S. C. Pei, J. J. Ding and J. Chang, “Color pattern recognition by quaternion correlation”, Proc. of Int. Conf. on Image Processing, Vol.1, (2001) : 894-897. | |
S. Gatcher, “Mirror Design for an Omnidirectional Camera with a Uniform Cylindrical Projection When Using the SVAVISCA Sensor”, Research Reports of CMP, OMNIVIEWS Project, Czech technical University in Prague, No. 3, 2001. Redirected from: http://cmp.felk.cvut.cz/projects/omniviews/ | |
S. Kumar, Neural Networks: A Classroom Approach, McGraw Hill, Int. Ed., 2004. | |
S.C. Pei, J.J. Ding and J.H. Chang, “Efficient implementation of quaternion Fourier transform convolution and correlation by 2-D Complex FFT”, IEEE Trans. on Signal Processing, Vol. 49, No. 11, (Nov 2001) p.p. 2783-2797. | |
S.J. Sangwine and T.A. Ell, “Hypercomplex auto- and cross-correlation of colour images”, Proc. of Int. Conf. on Image Processing, (ICIP 1999) p.p. 319-323. | |
S.K. Pal and S. Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, Wiley, Chichester, U.K., 1999. | |
T.A. Ell and S.J. Sangwine, “Colour –sensitive edge detection using hypercomplex filters”, (EUSIPCO 2000) p.p. 151-154. | |
T.A. Ell, “Hypercomplex spectral transforms”, PhD dissertation, Univ. Minnesota, Minneapolis, 1992. | |
T.A. Ell, “Quaternion-Fourier transforms for analysis of two-dimensional linear time-invariant partial differential systems”, Proc. of 32nd Conf. Decision Contr., (Dec 1993) p.p. 1830- 1841. | |
W. K. Wong, C. K. Loo, W. S. Lim, P. N. Tan, “Thermal Condition Monitoring System Using Log-Polar Mapping, Quaternion Correlation and Max-Product Fuzzy Neural Network Classification”, Elsevier, Neurocomputing (in press). | |
W. K. Wong, P. N. Tan, C. K. Loo and W. S. Lim , “Machine Condition Monitoring Using Omnidirectional Thermal Imaging System”, IEEE International Conference on Signal & Image Processing Applications 18-19, November 2009, Kuala Lumpur, Malaysia, Paper No. 151, p.p. 1-6. | |
W. K. Wong, P. N. Tan, C. K. Loo and W.S. Lim, “An Effective Surveillance System Using Thermal Camera”, 2009 International Conference on Signal Acquisition and Processing (ICSAP 2009), 3-5, Apr 2009, Kuala Lumpur, Malaysia: 13-17. | |
W. R. Hamilton, Elements of Quaternions, London, U.K.: Longmans, Green (1866). | |
Mr. Wai Kit Wong
Multimedia University - Malaysia
wkwong@mmu.edu.my
Associate Professor Chu Kiong Loo
Multimedia University - Malaysia
Associate Professor Way Soong Lim
Multimedia University - Malaysia
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