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

Abstract

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.

Improved KW entropy: a complexity measurement technique for time-series and its application in feature extraction of quay crane gearbox

“…It discretizes the continuous and complex motion states and maps them to several intervals marked with simple symbols, and uses symbols to describe dynamic character It is beneficial to discover the implicit mode of the system and improve the calculation speed. As an effective nonlinear signal processing method, it has been successfully applied in bio-medicine (Yao et al 2019;Gu et al 2021), mechanical engineering (Zhang et al 2019Dou 2019;Yang and Jia 2020;Han et al 2021;Wang and Li 2022) and other fields. The vibration signal of the quay crane gearbox contains a lot of impact and strong noise, and the symbolic dynamics method can be introduced in a targeted manner, thereby effectively reducing the noise and improving analysis effect and calculation efficiency.…”