The double-feature extraction method based on slope entropy and symbolic dynamic entropy for the fault diagnosis of rolling bearing

被引：1
|
作者：
Zhang, Zhe [1 ]
Liu, Yingwei [1 ]
Han, Yuxuan [2 ]
Huangfu, Pengfei [1 ]
Ma, Zhiyuan [1 ]
Shi, Weichen [1 ]
Feng, Ke [1 ]
机构：
[1] Xian Univ Technol, Sch Mech & Precis Instrument Engn, Xian, Shaanxi, Peoples R China
[2] Xian Univ Posts & Telecommun, Sch Automat, Xian, Shaanxi, Peoples R China
来源：
SIGNAL IMAGE AND VIDEO PROCESSING | 2024年 / 18卷 / SUPPL 1期
关键词：
Slope entropy; Symbolic dynamic entropy; Sparrow search algorithm; S_Dbw; Fault diagnosis;
D O I：
10.1007/s11760-024-03144-x
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper explores the application of slope entropy in fault diagnosis. In order to improve the recognition rate of faults, double-feature extraction is proposed, which combines slope entropy (SLEn) and symbolic dynamic entropy (SDE). There are five methods of entropy (slope entropy, permutation entropy, symbolic dynamic entropy, and sample entropy) are combined two by two in the double-feature experimental extraction. The K-Nearest Neighbor (KNN) is selected as the recognizer to calculate their recognition rates. The slope entropy and symbolic dynamic entropy having the highest recognition rates at 100%. It is followed by the combination of slope entropy and permutation entropy with an identification rate of 99%. In order to compensate for the lack of adaptive parameter selection in slope entropy and divide the parameter alpha and beta ranges of slope entropy more effectively, a new criterion is proposed to optimize the slope entropy by using the sparrow search algorithm (SSA-SLEn). Its state pattern reflects the characteristics of the fault signals in a more reasonable way.
引用
收藏
页码：211 / 226
页数：16
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