Abstract
The method of metal magnetic memory (MMM) was developed for early fault diagnosing of ferromagnetic materials. MMM signal is a weak-field detect signal, where the Earth’s magnetic field acts as the stimulus instead of an artificial magnetic field, and can be easily affected by the various factors such as environment interference and electronic noise. This paper is aimed to denoise metal magnetic memory signal and extract the feature of stress concentration zone. An efficient algorithm is proposed for detection of stress concentration zone based on wavelet and teager energy operator (TEO). This algorithm employs wavelet transform, to decompose the MMM signal into sub-band signal. In each of the critical sub-band signals, the mask construction is obtained by smoothing the TEO of corresponding wavelet coefficients that is applied to enhance the discriminability of signal components against those of noise. The multiscale related feature is extracted for the low signal-to-noise ratio signals that accurately determines the stress concentration. Finally, the proposed method is proved to be effective through the experimental data.
This work is supported by National Natural Science Foundation (51405303), Postdoctoral fund of Jiangsu Province (1301175C) Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Zhu, S., Zhang, J., Bi, Z. (2016). Metal Magnetic Memory Signal Denoising for Stress Concentration Zone. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-10-2338-5_28
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DOI: https://doi.org/10.1007/978-981-10-2338-5_28
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