A complexity measurement technique for time-series based on improved KW entropy

Zhenjie Zhu; Hong Chen; Shaojuan Hou; Zhenhuai Cai; Xujun Yang

doi:10.1117/12.3009584

16 October 2023 A complexity measurement technique for time-series based on improved KW entropy

Zhenjie Zhu, Hong Chen, Shaojuan Hou, Zhenhuai Cai, Xujun Yang

Author Affiliations +

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 1280325 (2023) https://doi.org/10.1117/12.3009584
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Considering the energy conservation and emission reduction is a key focus of attention. Owning to its special working condition, the quay crane usually undertake an important role. As a significant machinery in hoisting mechanism, the working states for gearbox is playing a significant role in machinery’s reliable operation. For extracting degradation indicators in huge vibration signals of quay crane, this paper proposes an improved KW symbol entropy for fault degradation feature. So as to keep united for the symbols, the technique introduces the root mean square for normal states signal and take the value as the standard and then combining a parameter named symbol coefficient in forming the unified symbols scale. Besides, a parameter named symbols number is proposed to expand the section of symbols set, the information expressing ability in improved then. Based on this, considering information entropy theory, some complexity index for symbol sequence is calculated, and then, two index are calculated as IKSE and IKDE. In order to verify the accuracy of the method proposed in this paper, an example is introduced to analyze the signals in actual working conditions. The results express that the two indexes can accurately represent the degradation trend of the signals, and for the parameter stability, has a certain application value.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenjie Zhu, Hong Chen, Shaojuan Hou, Zhenhuai Cai, and Xujun Yang "A complexity measurement technique for time-series based on improved KW entropy", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 1280325 (16 October 2023); https://doi.org/10.1117/12.3009584

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