Robust locally linear embedding and its application in analogue circuit fault diagnosis

被引:4
|
作者
He, Wei [1 ,2 ]
Yuan, Zhijie [3 ]
Yin, Baiqiang [4 ]
Wu, Wei [1 ,2 ]
Min, Zhixian [1 ,2 ]
机构
[1] China Elect Technol Grp Corp, Research Inst 38th, Hefei 230031, Peoples R China
[2] Key Lab Aperture Array & Space Applicat, Hefei 230088, Peoples R China
[3] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China
[4] Hefei Univ Technol, Sch Elect & Automat Engn, Hefei 230009, Peoples R China
基金
中国国家自然科学基金;
关键词
fault diagnosis; locality linear embedding; softmax regression; analogue circuit; NONLINEAR DIMENSIONALITY REDUCTION; PRINCIPAL COMPONENT ANALYSIS; MINIMUM ERROR ENTROPY; CORRENTROPY; PROGNOSTICS; MANIFOLD; FILTERS;
D O I
10.1088/1361-6501/acdcb1
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
During long-term operation of analogue circuits, fault diagnosis is important for preventing the occurrence of hazards. However, noise often accompanies sampled signals and makes the task of fault diagnosis more difficult. Therefore, developing a robust feature extraction technique is an indispensable part of fault diagnosis. The locally linear embedding (LLE) algorithm has recently emerged as a promising technique for dimensional reduction and feature extraction because it preserves linear neighborhoods, and it is quite effective when there is a locally linear dependent structure embedded in fault data. However, LLE is sensitive to noise. Therefore, the maximum correntropy criterion is adopted to resist non-Gaussian noise by seeking the optimal weight coefficient, and a half-quadratic optimization procedure is introduced to address the objective function. Moreover, softmax regression is applied to locate faults. Finally, two typical analogue circuit systems are used to demonstrate the robustness of the modified algorithm to non-Gaussian noise. The experimental results show that the robust LLE algorithm can outperform LLE in the extraction of fault features when there is non-Gaussian noise in the fault signals, and the proposed fault diagnosis method has a better effect in locating faults compared with other feature extraction methods.
引用
收藏
页数:14
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