A defect inspection method of the IH-JAR by statistical pattern recognition

통계적 패턴인식에 의한 유도가열 솥의 비파괴 불량 검사 방법

  • Published : 2000.01.01

Abstract

A die-casting junction method is usually used to manufacture the tub of an IH(induction heating) jar. If there is a very small air bubble in the junction area, the thermal conductivity is deteriorated and local overheat occurs. Such problem brings serious inferiority of the IH jar. In this paper, we propose a new method to detect such defect with simply measured thermal data. Thermal distribution of preheated tubs is obtained by scanning with infrared thermal sensors and analyzed with the statistic pattern recognition method. By defining the characteristic feature as the temperature difference between sensors and using ellipsoid function as decision boundary, a supervised learning method of genetic algorithm is proposed to obtain the required parpameters. After applying the proposed method to experiment, we have proved that the rate of recognition is high even for a small number of data set.

Keywords

References

  1. IH압력밥솥에 관한 보고서, LG전자 조리기기OBU설계실, 1997
  2. A. W. Van Herwaarden, D. C. Van Duyn and B. W. Van Oudheusden, 'Integrated thermopile sensor,' Sensor and Actuators, A21-A23, pp. 621-630, 1989 https://doi.org/10.1016/0924-4247(89)80046-9
  3. K. S. Lee et al.), 'Applications of the thermopile infrared sensor the home appliances,' Proc. of 1998 Appliance Manufacturer Conference & Expo., pp. 21-32, Nashville, Tenn., October 12-14, 1998
  4. P. Smyth and J. Mellstrom, 'Failure monitoring in dynamic systems: model construction without fault training data,' Telecommunications and Data Acquisition Progress Report, vol. 112, pp. 37-49, Jet Propulsion Laboratory, Pasadena, CA, Feb 15th, 1993
  5. T. Back and Hans-Paul Schwefel, 'An over view of evolutionary algorithms for parameter optimization,' Evolutionary Computation 1 (1), pp. 1-23, 1993
  6. Duda and Hart, Pattern Recognition and Machine Intelligence, Prentice Hall, 1972
  7. R. Schalkoff, Pattern Recognition, John Wiley&Sons, Inc. 1992
  8. J. C. Bezdek and S. K. Pal, Fuzzy Models For Pattern Recognition, IEEE Press, 1992
  9. S. Abe and R. Thawonmas, 'A fuzzy classifier with ellipsoidal regions,' IEEE Trans. Fuzzy System, vol. 5, pp. 358-368, Mar. 1997 https://doi.org/10.1109/91.618273
  10. L. P. Karasev, E. D. Mezintsev, and K. V. Khilkov, 'Using defect simulators in developing methods of acoustic emission inspection combined with strength tests of vessels,' Technical diagnostics and nondestructive testing, vol. 3, pp. 33. 1991
  11. P. Perner, 'A knowledge-based image-inspection system for automatic defect recognition, classification, and process diagnosis,' Machine vision and applications, vol. 7, pp. 135. 1994 https://doi.org/10.1007/BF01211659
  12. V. N. Potapov, N. G. Belyi, V. S. Grom, 'Increasing the contrast of defect images in radiation inspection of welded joints', Technical diagnostics and nondestructive testing, vol. 1, pp. 74, 1989