Identification of Fuzzy Measures for Machinery Fault Diagnosis

Masahiro Tsunoyama, Yuki Imai, Hayato Hori, Hirokazu Jinno, Masayuki Ogawa, Tatsuo Sato

Abstract

This paper proposes an identification method of fuzzy measure for fault diagnosis of rotating machineries using vibration spectra method. The membership degrees for spectra in fuzzy set composed of vibration spectra are obtained from the optimized membership functions. The fuzzy measure is identified by the proposed method using the partial correlation coefficients between two spectra and the weight of each spectrum given by skilled engineers. The possibility of faults are determined by the fuzzy integral that is made by using the membership degrees and fuzzy measures for spectra. This paper also evaluates the method using field data.

References

  1. Wang, Z. and Klir, G. J., 1992. Fuzzy Measure Theory, Plenum Press.
  2. Sipley, B., 2000. Cause and correlation in Biology. Cambridge University Press.
  3. Grabisch, M. , Murofushi, T. and Sugeno, M., 2000. Fuzzy Measures and Integrals : Theory and Applications, Springer.
  4. Chen, P., Feng,F. and Toyoda, T., 2002. Sequential Method for Plant Machinery by Statistical Tests and Possibility Theory, REAJ, vol. 24, No. 4, pp. 331-322.
  5. Marinai , L., and Singh, R., 2006. A Fuzzy Logic Approach to Gas Path Diagnostics in Aero-engines, Computational Intelligence in Fault Diagnosis, Springer.
  6. Taya, M. and Murofushi, T., 2006. Fuzzy measure identification for bootstrapped Choquet integral model in multicriteria decision making. International Conference on Soft Computing and Intelligent Systems, and International Symposium on advanced Intelligent Systems 2006, pp.1402-1407, Japan.
  7. Liu, X., Ma, L. and Mathew, J., 2007. Rotating machinery fault diagnosis on fuzzy data fusion techniques, 2nd World Congress on Engineering Asset Management and the 4th International Conference on Condition Monitoring, pp. 1309-1318, Harrogate England.
  8. Tsunoyama, M., Masumori, K., Jinno, H., Ogawa M. and Sato, T., 2010. An Application of Fuzzy Measure and Integral for Diagnosis of Rotating Machinery Faults, ICFC 2010, International Conference on Fuzzy Computation, pp. 120-124, Spain.
  9. Tsunoyama, Imai, Y., K., Jinno, H., Ogawa M. and Sato, T., 2012. An Application of Fuzzy Measure and Integral to Diagnosis Utilizing Knowledge of Skilled Engineers, PRDC 2012, Pacific Rim Dependable Computing, Japan.
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Paper Citation


in Harvard Style

Tsunoyama M., Imai Y., Hori H., Jinno H., Ogawa M. and Sato T. (2013). Identification of Fuzzy Measures for Machinery Fault Diagnosis . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 273-278. DOI: 10.5220/0004629202730278


in Bibtex Style

@conference{fcta13,
author={Masahiro Tsunoyama and Yuki Imai and Hayato Hori and Hirokazu Jinno and Masayuki Ogawa and Tatsuo Sato},
title={Identification of Fuzzy Measures for Machinery Fault Diagnosis},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)},
year={2013},
pages={273-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004629202730278},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)
TI - Identification of Fuzzy Measures for Machinery Fault Diagnosis
SN - 978-989-8565-77-8
AU - Tsunoyama M.
AU - Imai Y.
AU - Hori H.
AU - Jinno H.
AU - Ogawa M.
AU - Sato T.
PY - 2013
SP - 273
EP - 278
DO - 10.5220/0004629202730278