Abstract
Entropy changes with the variation of the system status. It has been widely used as a standard for the determination of system status, quantity of system complexity and system classification. Based on the singular spectrum entropy of traditional calculation method, a sliding singular spectrum entropy method is proposed to use for singularity detection and extraction of impaction signal. Each original signal point is intercepted a neighborhood points of the signal with a given length and the singular spectrum entropy for the intercepted signal is calculated. A surrogate signal with the same length as the original signal is acquired by point-to-point calculation. Numerical simulation and gear fault diagnosis experiment are studied to verify the proposed method, the results show that the method is valid for the reflection on the changing of system status, singularity detection and the extraction of the weak fault feature signal mixed in the strong background signal.
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Lu, Y., Li, Y., Xiao, H., Wang, Z. (2008). A Sliding Singular Spectrum Entropy Method and Its Application to Gear Fault Diagnosis. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_83
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DOI: https://doi.org/10.1007/978-3-540-87442-3_83
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