Dong Wei, Feng Bin, Liu Yulun, Zhang Shuqing
School of Machinery and Automation, Weifang University, Weifang, 261061, China.
School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China.
Sci Rep. 2025 Apr 30;15(1):15169. doi: 10.1038/s41598-025-99913-w.
The detection of rolling bearing faults is essential to ensure the operational safety of rotating machinery. An effective method for diagnosing rolling bearing faults is the maximum second-order cyclostationarity blind deconvolution (CYCBD) method, which has the ability to extract weak fault periodic pulse features. However, blind deconvolution methods such as CYCBD often fail when composite bearing faults occur in the presence of strong background noise. To overcome this issue, an adaptive blind deconvolution (ABDD) method based on improved CYCBD is proposed. In this method, a finite impulse response (FIR) filter bank is constructed to cover the entire frequency band of the signal, enabling fault frequency segmentation. The improved CYCBD is then used to obtain a filter pattern that locks on the fault frequency. These filter patterns are arranged in descending order based on the correlation kurtosis (CK). The required filtering mode is selected according to the sorting mode, and a spectrum analysis is performed to extract fault features of single and compound faults in the bearing. Simulation and experimental results demonstrate that ABDD effectively extracts bearing composite fault features and outperforms classic feature decomposition and blind deconvolution methods in extracting single and composite fault features.
滚动轴承故障检测对于确保旋转机械的运行安全至关重要。一种有效的滚动轴承故障诊断方法是最大二阶循环平稳盲反卷积（CYCBD）方法，该方法能够提取微弱故障周期性脉冲特征。然而，当在强背景噪声存在的情况下出现复合轴承故障时，诸如CYCBD之类的盲反卷积方法常常失效。为克服这一问题，提出了一种基于改进CYCBD的自适应盲反卷积（ABDD）方法。在该方法中，构建一个有限脉冲响应（FIR）滤波器组以覆盖信号的整个频带，实现故障频率分割。然后使用改进的CYCBD获得锁定故障频率的滤波器模式。这些滤波器模式根据相关峰度（CK）按降序排列。根据排序模式选择所需的滤波模式，并进行频谱分析以提取轴承中单一和复合故障的特征。仿真和实验结果表明，ABDD有效地提取了轴承复合故障特征，并且在提取单一和复合故障特征方面优于经典特征分解和盲反卷积方法。
