Piltan Farzin, Kim Jong-Myon
Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 680-479, Korea.
School of IT Convergence, University of Ulsan, Ulsan 680-479, Korea.
Sensors (Basel). 2018 Apr 7;18(4):1128. doi: 10.3390/s18041128.
An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.
一个有效的轴承故障检测与诊断（FDD）模型对于确保机器的正常和安全运行至关重要。本文提出了一种基于轴承振动数据建模的可靠的FDD模型参考观测器技术，该技术通过分析轴承的动态特性来建立轴承振动数据模型，以及一种高阶超扭曲滑模观测（HOSTSMO）技术，用于使用这些数据模型做出诊断决策。与线性方法相比，HOSTSMO技术通过对正常和故障条件下的5个自由度进行建模，能够自适应地提高滚动元件轴承（REB）非线性故障估计的性能。使用凯斯西储大学提供的振动数据集对所提出技术的有效性进行了评估，该数据集由记录的内圈、外圈、滚珠无故障（即正常）的REB振动加速度信号组成。实验结果表明，所提出的技术优于ARX - 拉盖尔比例积分观测（ALPIO）技术，对于裂纹严重程度分别为0.007英寸、0.014英寸和0.021英寸的三个等级，性能分别提高了18.82%、16.825%和17.44%。
