Refined time-shift multiscale dispersion Lempel-Ziv complexity to diagnose rolling bearing faults

被引:0
作者
Yongjian Li
Li Tan
Peng Li
Qing Xiong
机构
[1] Wuyi University,School of Railway Tracks and Transportation
[2] Chengdu Vocational & Technical College of Industry,School of Intelligent Manufacturing and Automobile
关键词
Fault diagnosis; Time-shift; Dispersion Lempel-Ziv complexity; Feature extraction; Rolling bearing;
D O I
暂无
中图分类号
学科分类号
摘要
The key to damage detection is whether fault features can be extracted effectively from raw signals. Hence, we propose an approach based on the refined time-shift multiscale dispersion Lempel-Ziv complexity (RTSMDLZC) to effectively extract fault features. First, the time-shift multiscale sequence constructed from the raw time series can obtain more fault information more effectively. Then, the refined method addresses the lacking of sizeable numerical fluctuation on a large scale and enhances the algorithm’s stability. Simulation signals and two experimental cases verify the effectiveness and applicability of the RTSMDLZC. The results indicate that compared with other classic methods, the RTSMDLZC can extract bearing fault features more accurately and has better identification accuracy.
引用
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页码:4557 / 4566
页数:9
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