Li Shih-Yu, Gu Kai-Ren
Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Mechanical and Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Sensors (Basel). 2019 May 10;19(9):2178. doi: 10.3390/s19092178.
In this paper, a set of smart fault-detection approach with chaotic mapping strategy is developed for an industrial ball-bearing system. There are four main statuses in this ball-bearing system: normal, inner race fault, outer race fault, and ball fault. However, it is hard to simply classify each of them through their vibration signals in time-series. By developing a nonlinear error dynamic system as well as a chaotic mapping strategy, the signals in the time series can be converted into the chaotic domain, which are revealed in 3D phase portraits. Further, through collocation of clustering methods, such as Euclidean distance (ED) and the kernel method of K-means (KM), the proposed 3D phase portraits of each different state can be efficiently identified through checking the autonomously adjusted ranges of feature values. The experiment results show that the proposed smart detection approach is effective and feasible, and the accuracy of detection in the testing stage is close to 100%.
本文针对工业球轴承系统开发了一套具有混沌映射策略的智能故障检测方法。该球轴承系统有四种主要状态：正常、内圈故障、外圈故障和滚珠故障。然而，很难通过时间序列中的振动信号简单地对它们进行分类。通过开发非线性误差动态系统以及混沌映射策略，时间序列中的信号可以转换到混沌域，并在三维相图中显示出来。此外，通过欧几里得距离（ED）和K均值核方法（KM）等聚类方法的搭配，通过检查特征值的自动调整范围，可以有效地识别出每种不同状态的三维相图。实验结果表明，所提出的智能检测方法是有效可行的，测试阶段的检测准确率接近100%。
