Fault diagnosis method of rotating machinery based on global-local Euler elastic discriminant projection

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (11) : 65-74.

SU Shuzhi1,2, ZHANG Maoyan1, FANG Xianjin1,2, ZHU Yanmin3

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Abstract

Fault diagnosis methods are usually sensitive to outliers, and it is difficult to extract global and local discriminant information at the same time, resulting in poor separation between low-dimensional discriminant feature subsets. To solve this problem, a fault diagnosis method of rotating machinery was proposed based on Global-Local Euler Elastic Discriminant Projection (GLEEDP). This method maps the high-dimensional fault features to the Euler representation space through the cosine metrics, and expands the differences between heterogeneous fault samples. Then, an optimization model based on three objective functions of global, local and within-class scatter is constructed in this space, which further improves the local intra class aggregation and global inter class separation of low dimensional discriminant feature subsets on the basis of maintaining the overall structure. The experimental results on two mechanical fault datasets of bearing and gearbox show that the proposed method can effectively explore the fault discrimination information and has superior fault diagnosis performance.

Key words

cosine metric / euler representation / dimension reduction / fault diagnosis

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SU Shuzhi1,2, ZHANG Maoyan1, FANG Xianjin1,2, ZHU Yanmin3. Fault diagnosis method of rotating machinery based on global-local Euler elastic discriminant projection[J]. Journal of Vibration and Shock, 2023, 42(11): 65-74

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