The fault features of rotating machinery at the early degradation stage are always weak as a result of interference from strong noise and irrelevant harmonics. Although traditional acoustic diagnosis techniques have attracted much attention with the merits of rich information and non-contact, extracting meaningful features related to faults in extremely low signal-to-noise ratios (SNRs) has always been a challenging problem. Considering the extraordinary physical properties of phononic crystals (PnCs) and acoustic metamaterials, this study proposes a structural enhancement method for rotating machinery fault diagnosis via line-defect PnC sensing. In contrast to conventional acoustic filtering and enhancement methods, this method can directly filter out noise and enhance fault features in the pre-processing stage of acoustic perception without the need for complex post-processing algorithms. Consequently, the original information of the fault features is preserved intact, thus further increasing the detection limit of current acoustic sensing. Specially, the designed line-defect PnC is parametrically tunable. Combined with the prior knowledge of rotating machinery fault features, such as gears and bearings, it is possible to design structures suitable for enhancing their fault features, which has great potential for practical engineering applications. The enhancement mechanism of line-defect PnC is theoretically described, and numerical simulations are also conducted to verify its ability to detect weak faults in rotating machinery. The experimental results show that by comparison with the variational modal decomposition (VMD)-based method, the proposed method exhibits superior fault feature enhancement performance under low SNR conditions. By systematically combining fault detection methods and acoustic metamaterial sensing, it can be foreseen that the proposed method shows great potential in mechanical equipment fault diagnosis.

中文翻译：

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通过线缺陷声子晶体传感增强旋转机械弱故障特征


由于强噪声和无关谐波的干扰，旋转机械退化早期的故障特征往往较弱。尽管传统的声学诊断技术凭借信息丰富、非接触等优点而备受关注，但在极低信噪比（SNR）下提取与故障相关的有意义的特征一直是一个具有挑战性的问题。考虑到声子晶体（PnC）和声学超材料的非凡物理特性，本研究提出了一种通过线缺陷 PnC 传感进行旋转机械故障诊断的结构增强方法。与传统的声学滤波和增强方法相比，该方法可以在声感知的预处理阶段直接滤除噪声并增强故障特征，而不需要复杂的后处理算法。因此，故障特征的原始信息被完整保留，从而进一步提高了当前声学传感的检测极限。特别是，设计的线缺陷 PnC 是参数可调的。结合齿轮、轴承等旋转机械故障特征的先验知识，可以设计出适合增强其故障特征的结构，在实际工程应用中具有巨大潜力。从理论上描述了线缺陷PnC的增强机制，并进行了数值模拟，验证了其检测旋转机械弱故障的能力。实验结果表明，与基于变分模态分解（VMD）的方法相比，该方法在低信噪比条件下表现出优越的故障特征增强性能。 通过系统地结合故障检测方法和声学超材料传感，可以预见该方法在机械设备故障诊断中显示出巨大的潜力。