School of Computer Science and Technology, Information Countermeasure Technique Institute, Harbin Institute of Technology, Harbin 150080, China.
Defence Industry Secrecy Examination and Certification Center, Bejing 100001, China.
Rev Sci Instrum. 2023 May 1;94(5). doi: 10.1063/5.0140516.
The difficulties in early fault diagnosis of bearings mainly include two aspects: first, the initial damage size of the bearing is small, and the abnormal vibration caused by slight damage to the bearing is very weak. Second, vibration signals collected in actual industrial environments always contain strong noise interference. Therefore, traditional diagnostic procedures are not satisfactory. To address these challenges, this work provides a hybrid model combining frequency-weighted energy operator (FWEO) with power spectrum fusion (PSF) to identify weak fault features of bearings and detect different fault types. Different from traditional time-domain signal filtering, PSF is first used to reduce the interference of noise components in the power spectrum, which will not weaken the fault signal components during denoising. Second, the filtered signal is transformed into the time domain and FWEO is employed to further enhance the cyclic fault signal caused by the weak defect of the bearing. Finally, the existence of a fault is identified by observing the squared envelope spectrum of the signal. The effectiveness of the proposed hybrid model is demonstrated through two simulated fault signals and three different experimental fault signals. The results show that the proposed model has high anti-noise performance and robustness and can extract the fault frequency well.
一是轴承初始损伤尺寸较小，轴承轻微损伤所产生的异常振动很微弱；二是实际工业环境中采集到的振动信号往往含有较强的噪声干扰。因此，传统的诊断程序并不令人满意。针对这些挑战，本工作提出了一种将频率加权能量算子（FWEO）与功率谱融合（PSF）相结合的混合模型，用于识别轴承的微弱故障特征并检测不同的故障类型。与传统的时域信号滤波不同，首先使用 PSF 来降低功率谱中噪声分量的干扰，在去噪过程中不会削弱故障信号分量。其次，对滤波后的信号进行时域变换，并采用 FWEO 进一步增强由轴承微弱缺陷引起的周期性故障信号。最后，通过观察信号的平方包络谱来判断故障的存在。通过两个模拟故障信号和三个不同的实验故障信号验证了所提出的混合模型的有效性。结果表明，该模型具有较高的抗噪声性能和鲁棒性，能够很好地提取故障频率。
