标题： 小龙虾优化小波滤波器及其在故障诊断中的应用 显示英文标题

标题： A crayfish-optimized wavelet filter and its application to fault diagnosis

摘要： 工业机器故障诊断确保了系统的可靠性和功能性，但由于信噪比（SNR）低、背景噪声和随机干扰，识别振动信号中的信息频率带可能具有挑战性。小波滤波器通常用于此目的，但其参数对于定位信息频率带以提取重复瞬变至关重要。本研究利用了一种 crayfish 优化算法（COA）自适应地优化小波滤波器以提取故障特征。COA 在解决与不准确的 CK 周期相关的问题时使用相关峰度（CK）作为适应度函数。所提出的这种方法应用于不同的工业案例，并与现有方法进行了比较，展示了其在提取信息频率方面的优越性。 显示英文摘要

摘要： Industrial machine fault diagnosis ensures the reliability and functionality of the system, but identifying informative frequency bands in vibration signals can be challenging due to low signal-to-noise ratio (SNR), background noise, and random interferences. The wavelet filter is commonly used for this purpose, but its parameters are crucial for locating the informative frequency band to extract repetitive transients. This study utilizes a crayfish optimization algorithm (COA) to optimize the wavelet filter adaptively for extracting fault characteristics. COA uses correlated kurtosis (CK) as a fitness function while addressing issues related to inaccurate CK period through an updation process. The proposed methodology is applied to different industrial cases and compared with existing methods, demonstrating its superiority in extracting informative frequencies.