A full generalization of the Gini index for bearing condition monitoring

被引:29
作者
Chen, Bingyan [1 ,2 ]
Song, Dongli [1 ]
Gu, Fengshou [1 ,2 ]
Zhang, Weihua [1 ]
Cheng, Yao [1 ]
Ball, Andrew D. [2 ]
Bevan, Adam [3 ]
Gu, James Xi [4 ]
机构
[1] Southwest Jiaotong Univ, State Key Lab Tract Power, Chengdu 610031, Peoples R China
[2] Univ Huddersfield, Ctr Efficiency & Performance Engn, Huddersfield HD1 3DH, England
[3] Univ Huddersfield, Inst Railway Res, Huddersfield HD1 3DH, England
[4] Univ Bolton, Sch Engn, Bolton BL3 5AB, England
基金
中国国家自然科学基金;
关键词
Gini index; Generalized Gini indices; Fully generalized Gini indices; Sparsity measures; Condition monitoring; Rolling element bearings; FAULT-DIAGNOSIS; SMOOTHNESS INDEX; BAND SELECTION; L(P)/L(Q) NORM; KURTOSIS; DECONVOLUTION; SIGNATURE; KURTOGRAM;
D O I
10.1016/j.ymssp.2022.109998
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The classic Gini index (GI) is generalized recently by using nonlinear weight sequences as sparsity measures for sparse quantification and machine condition monitoring. The generalized GIs with different weight parameters are more robust to random transients. However, they show insufficient performance in discriminating repetitive transients under noise contamination. To overcome this shortage, this paper proposes a two-parameter generalization method to tune not only the weight parameter but also the norm order, allowing for a full generalization of the classic GI to quantify transient features and leading to new statistical indicators which are named fully generalized GIs (FGGIs). Mathematical derivations show that FGGIs satisfy at least four of the six typical attributes of sparsity measures and that those with weight parameter equal to one satisfy at least five sparse attributes, proving that they are a new family of sparsity measures. Numerical simulations demonstrate that FGGIs can monotonically evaluate the sparseness of the signals and that the FGGIs with appropriate parameters exhibit improved performance in resisting random transient interferences and discriminating noise-contaminated repetitive transients compared to traditional sparsity measures. The performance of FGGIs in the condition monitoring of rolling element bearings is validated using two different run-to-failure experiment datasets, including a gradual failure and a sudden failure. The results show that increasing the norm order can improve the capability of FGGIs to characterize transient fault features, allowing more accurate trending of bearing health conditions, and therefore achieving better condition monitoring performance than the traditional sparsity measures.
引用
收藏
页数:23
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