Application of Blind Source Separation Techniques for Generation of PHM Useful Information

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Mar 26, 2021
Bruno P. Leão João Gomes Roberto K. H. Galvão Takashi Yoneyama

Abstract

One of the most important issues when dealing with PHM developments is the availability of adequate sensors to provide measures that indicate the health state of a component or system. Installation of additional sensors for such purpose usually implies increments in costs and weight and reduction of reliability and availability. Sometimes equivalent information can be inferred from other available sources, allowing the design of PHM solutions with no need for additional sensors.The power consumed by a set of components may provide information concerning their health states. These components may be all fed by the same power supply. This paper proposes a novel application of blind source separation techniques to infer the power consumed by the components using only the measurement of the power supply output. The usefulness of such techniques is demonstrated in a real.

How to Cite

P. Leão, B. ., Gomes, J. ., K. H. Galvão, R. ., & Yoneyama, T. . (2021). Application of Blind Source Separation Techniques for Generation of PHM Useful Information. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1717
Abstract 5 | PDF Downloads 3

##plugins.themes.bootstrap3.article.details##

Keywords

sensor fusion, sensors, time domain analysis, virtual sensors

References
(Barnard and Aldrich, 2003) J. P. Barnard and C. Aldrich. Diagnostic Monitoring of Internal Combustion Engines by Use of Independent Component Analysis and Neural Networks, in Proceedings of 2003 International Joint Conference on Neural Networks, Portland, OR, 2003.
(Chen et al., 2003) Z. S. Chen, Y. M. Yang, G. J. Shen, X. S. Wen, Early Diagnosis of Helicopter Gearboxes Based on Independent Component Analysis, in Proceedings of 5th International Symposium on Test and Measurement, Beijing,China, 2003.
(Duda et al., 2001) R. O. Duda; P. E. Hart and D. G. Stork, Pattern Classification. 2nd ed. New York: Wiley, 2001.
(Gelle et al., 2003) G. Gelle; M. Colas and C. Serviere,
Blind Source Separation: A New Pre-Processing Tool for Rotating Machines Monitoring?, IEEE Transactions on Instrumentation and Measurement, vol. 52, pp. 790-795, 2003.
(Hyvärinen, 1999a) A. Hyvärinen, Survey on Independent Component Analysis, Neural Computing Surveys, vol. 2, pp. 94-128, 1999.
(Hyvärinen, 1999b) A. Hyvärinen, Fast and Robust Fixed-Point Algorithms for Independent Component Analysis, IEEE Transactions on Neural Networks, vol. 10, pp. 626-634, 1999.
(Hyvärinen and Oja, 2000) A. Hyvärinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, vol. 13, pp. 411- 430, 2000.
(Jolliffe, 1986) I. T. Jolliffe, Principal Component Analysis, New York: Springer-Verlag, 1986.
(Leão et al., 2009) B. P. Leão, J. P. P. Gomes, R. K. H. Galvão and T. Yoneyama, Aircraft Flap and Slat Systems Health Monitoring Using Statistical Process Control Techniques, in Proceedings of IEEE Aerospace Conference, Big Sky, MO, 2009.
(Li and Qu, 2002) L. Li and L. Qu, Machine Diagnosis with Independent Component Analysis and Envelope Analysis, in Proceedings of International Conference on Industrial Technology, Bangkok, Thailand, 2002.
(Ma and Hao, 2004) X. J. Ma, Z. H. Hao, Multisensor Data Fusion Based on Independent Component Analysis for Fault Diagnosis of Rotor, in Proceedings of International Symposium on Neural Networks, Dalian, China, 2004.
(Schimert, 2008) J. Schimert, Data-Driven Fault Detection Based on Process Monitoring Using Dimension Reduction Techniques, in Proceedings of IEEE Aerospace Conference, Big Sky, MO, 2008.
(Tian et al., 2003) X. H. Tian, J. Lin, K. R. Fyfe, M. J. Zuo, Gearbox Fault Diagnosis Using Independent Component Analysis in the Frequency Domain and Wavelet Filtering, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, China, 2003.
(Vachtsevanos, 2006) G. Vachtsevanos, F. L. Lewis, M. Roemer, A. Hess, and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Hoboken, NJ: John Wiley & Sons, 2006.
Section
Technical Papers

Most read articles by the same author(s)

1 2 > >>