Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan.
Institute for the Development and Quality, Macao 999078, China.
Sensors (Basel). 2023 Apr 7;23(8):3801. doi: 10.3390/s23083801.
This article presents a performance investigation of a fault detection approach for bearings using different chaotic features with fractional order, where the five different chaotic features and three combinations are clearly described, and the detection achievement is organized. In the architecture of the method, a fractional order chaotic system is first applied to produce a chaotic map of the original vibration signal in the chaotic domain, where small changes in the signal with different bearing statuses might be present; then, a 3D feature map can be obtained. Second, five different features, combination methods, and corresponding extraction functions are introduced. In the third action, the correlation functions of extension theory used to construct the classical domain and joint fields are applied to further define the ranges belonging to different bearing statuses. Finally, testing data are fed into the detection system to verify the performance. The experimental results show that the proposed different chaotic features perform well in the detection of bearings with 7 and 21 mil diameters, and an average accuracy rate of 94.4% was achieved in all cases.
本文提出了一种使用分数阶混沌特征的轴承故障检测方法的性能研究，其中清楚地描述了五个不同的混沌特征和三种组合，并对检测结果进行了组织。在该方法的架构中，首先应用分数阶混沌系统对原始振动信号进行混沌域中的混沌映射，其中可能存在不同轴承状态下信号的微小变化；然后，可以得到一个 3D 特征图。其次，介绍了五种不同的特征、组合方法和相应的提取函数。在第三个动作中，应用了用于构建经典域和联合域的可拓理论的相关函数来进一步定义属于不同轴承状态的范围。最后，将测试数据输入到检测系统中进行验证。实验结果表明，所提出的不同混沌特征在检测直径为 7 毫米和 21 毫米的轴承方面表现良好，在所有情况下的平均准确率达到 94.4%。
