Abstract
Vibration is one of the most common condition monitoring data types for electromechanical products. Aiming at life feature extraction from vibration data and considering the advantages of Shannon information entropy in measuring product uncertainty as well as Hilbert-Huang Transform (HHT) in vibration data processing, a life feature extraction method based on Hilbert marginal spectrum entropy was proposed. Hilbert marginal spectrum entropy was taken as the life feature that could characterize product degradation. The computing way of Hilbert marginal spectrum entropy for Accelerated Degradation Testing (ADT) vibration data was presented. Finally, this method was applied to motor ADT vibration data. By quantifying metrics such as monotonicity, prognosability, trendability, and fitness of Hilbert marginal spectrum entropy feature, the analysis result shows that this life feature extraction method is effective and engineering practicable.
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© 2013 Springer-Verlag Berlin Heidelberg
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Wang, F., Li, X., Jiang, T. (2013). Life Feature Extraction Based on Hilbert Marginal Spectrum Entropy for ADT Vibration. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. Lecture Notes in Electrical Engineering, vol 211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34522-7_5
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DOI: https://doi.org/10.1007/978-3-642-34522-7_5
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