Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 680⁻749, Korea.
Sensors (Basel). 2018 Dec 10;18(12):4359. doi: 10.3390/s18124359.
The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer (FLO). The traditional feedback linearization observer is stable; however, this technique suffers from a lack of robustness. The proposed variable structure technique was used to improve the robustness of the fault estimation while reducing the uncertainties in the feedback linearization observer. The effectiveness of the proposed FLO procedure for the identification of outer, inner, and ball faults was tested using the Case Western University vibration dataset. The proposed model outperformed the variable structure observer (VSO), traditional feedback linearization observer (TFLO), and proportional-integral observer (PIO) by achieving average performance improvements of 5.5%, 8.5%, and 18.5%, respectively.
滚动轴承是旋转机械中的重要部件。合适的轴承故障检测和诊断（FDD）对于保持机器在安全健康的状态下运行至关重要。针对这个问题，提出了一种扩展的基于观测器的 FDD 方法，该方法使用了变结构反馈线性化观测器（FLO）。传统的反馈线性化观测器是稳定的；然而，这种技术缺乏鲁棒性。所提出的变结构技术用于提高故障估计的鲁棒性，同时减少反馈线性化观测器中的不确定性。使用凯斯西储大学振动数据集测试了所提出的 FLO 程序在外圈、内圈和球故障识别中的有效性。所提出的模型通过分别实现 5.5%、8.5%和 18.5%的平均性能提升，优于变结构观测器（VSO）、传统反馈线性化观测器（TFLO）和比例积分观测器（PIO）。
