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Open AccessArticle

Adaptive Multiscale Symbolic-Dynamics Entropy for Condition Monitoring of Rotating Machinery

by Chunhong Dou 1 and Jinshan Lin 2,*
1
School of Information and Control Engineering, Weifang University, No. 5147 Dong Feng Dong Street, Weifang 261061, China
2
School of Mechatronics and Vehicle Engineering, Weifang University, No. 5147 Dong Feng Dong Street, Weifang 261061, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1138; https://doi.org/10.3390/e21121138
Received: 15 October 2019 / Revised: 10 November 2019 / Accepted: 18 November 2019 / Published: 21 November 2019
Vibration data from rotating machinery working in different conditions display different properties in spatial and temporal scales. As a result, insights into spatial- and temporal-scale structures of vibration data of rotating machinery are fundamental for describing running conditions of rotating machinery. However, common temporal statistics and typical nonlinear measures have difficulties in describing spatial and temporal scales of data. Recently, statistical linguistic analysis (SLA) has been pioneered in analyzing complex vibration data from rotating machinery. Nonetheless, SLA can examine data in spatial scales but not in temporal scales. To improve SLA, this paper develops symbolic-dynamics entropy for quantifying word-frequency series obtained by SLA. By introducing multiscale analysis to SLA, this paper proposes adaptive multiscale symbolic-dynamics entropy (AMSDE). By AMSDE, spatial and temporal properties of data can be characterized by a set of symbolic-dynamics entropy, each of which corresponds to a specific temporal scale. Afterward, AMSDE is employed to deal with vibration data from defective gears and rolling bearings. Moreover, the performance of AMSDE is benchmarked against five common temporal statistics (mean, standard deviation, root mean square, skewness and kurtosis) and three typical nonlinear measures (approximate entropy, sample entropy and permutation entropy). The results suggest that AMSDE performs better than these benchmark methods in characterizing running conditions of rotating machinery.
Keywords: multiscale; symbolic dynamics; entropy; condition monitoring; rotating machinery multiscale; symbolic dynamics; entropy; condition monitoring; rotating machinery
MDPI and ACS Style

Dou, C.; Lin, J. Adaptive Multiscale Symbolic-Dynamics Entropy for Condition Monitoring of Rotating Machinery. Entropy 2019, 21, 1138.

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