基于全局-局部欧拉弹性判别投影的旋转机械故障诊断方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (11) : 65-74.

论文

苏树智1，2，张茂岩1，方贤进1，2，朱彦敏3

作者信息 +

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

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

Author information +

文章历史 +

摘要

故障诊断方法通常对异常值敏感，并且难以同时提取全局和局部判别信息，从而导致低维判别特征子集类间分离性不佳，针对该问题提出了一种基于全局-局部欧拉弹性判别投影（Global-Local Euler Elastic Discriminant Projection，GLEEDP）的旋转机械故障诊断方法。该方法通过余弦度量将高维故障特征映射到欧拉表示空间，扩大异类故障样本间的差异，然后在该空间中构建了基于全局、局部及类间散布三个目标函数的最优化模型，实现了在保持整体结构的基础上，进一步提高低维判别特征子集的局部类内聚集性和全局类间分离性。在轴承和齿轮箱两个机械故障数据集上的实验结果表明，所提方法可以有效发掘故障判别信息，具有优越的故障诊断性能。

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.

导出引用

苏树智1，2，张茂岩1，方贤进1，2，朱彦敏3. 基于全局-局部欧拉弹性判别投影的旋转机械故障诊断方法[J]. 振动与冲击, 2023, 42(11): 65-74

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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