Lee Dong-Han, Ahn Jong-Hyo, Koh Bong-Hwan
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1 gil, Jung-gu, Seoul 100-715, Korea.
Sensors (Basel). 2017 Oct 28;17(11):2477. doi: 10.3390/s17112477.
This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space.
本研究提出了一种用于轴承系统的故障检测与诊断方法，该方法采用基于总体经验模态分解（EEMD）的特征提取，结合粒子群优化（PSO）、主成分分析（PCA）和等距映射（Isomap）。首先，假设一个数学模型来生成来自受损轴承部件（如内圈、外圈和滚动体）的振动信号。介绍了将振动信号分解为固有模态函数（IMF）并提取统计特征的过程，以开发一个损伤敏感参数向量。最后，使用PCA和Isomap算法对该参数向量进行分类和可视化，以将损伤特征与健康轴承部件区分开来。此外，基于PSO的优化算法通过为参数向量选择合适的权重来提高分类性能，以最大化参数向量在三维空间中的分离和分组可视化效果。
