Zhao Dezun, Li Jianyong, Cheng Weidong, Wen Weigang
Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
School of Mechanical Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, Ministry of Education, Beijing 100044, China.
ISA Trans. 2023 Feb;133:518-528. doi: 10.1016/j.isatra.2022.06.047. Epub 2022 Jul 4.
The rotational frequency (RF) is an important information for multi-fault features detection of rolling bearing under varying speed conditions. In the traditional methods, such as the computed order analysis (COA) and the time-frequency analysis (TFA), the RF should be measured using an encoder or extracted by a complex algorithm, which bring challenge to bearing fault diagnosis. In order to address this issue, a novel iterative generalized demodulation (IGD) based method guided by the instantaneous fault characteristic frequency (IFCF) extraction and enhanced instantaneous rotational frequency (IRF) matching is proposed in this paper. Specifically, the resonance frequency band excited by bearing fault is first obtained by the band-pass filter, and its envelope time-frequency representation (TFR) is calculated using the Hilbert transform and the short-time Fourier transform (STFT). Second, the IFCF is extracted using the harmonic summation-based peak search algorithm from the envelope TFR. Third, the time-varying RF ridge is transformed into a line paralleling to the time axis using the IGD with the phase function (PF). The PF is calculated by the IFCF function and fault characteristic coefficient (FCC). Lastly, the iterative generalized demodulation spectrum (IGDS) is obtained using the fast Fourier transform (FFT) for identifying fault type corresponding to the extracted IFCF. Based on obtained fault type and FCC ratios, new PFs and frequency points (FPs) are calculated for detecting other faults. Both simulated and experimental results validate that multi-fault features of rolling bearing under time-varying rotational speeds can be effectively identified without RF measurement and extraction.
旋转频率（RF）是变速条件下滚动轴承多故障特征检测的重要信息。在传统方法中，如计算阶次分析（COA）和时频分析（TFA），RF需使用编码器测量或通过复杂算法提取，这给轴承故障诊断带来了挑战。为解决这一问题，本文提出了一种基于瞬时故障特征频率（IFCF）提取和增强瞬时旋转频率（IRF）匹配的新型迭代广义解调（IGD）方法。具体而言，首先通过带通滤波器获得轴承故障激发的共振频带，并使用希尔伯特变换和短时傅里叶变换（STFT）计算其包络时频表示（TFR）。其次，使用基于谐波求和的峰值搜索算法从包络TFR中提取IFCF。第三，利用带相位函数（PF）的IGD将时变RF脊转换为与时间轴平行的直线。PF由IFCF函数和故障特征系数（FCC）计算得出。最后，使用快速傅里叶变换（FFT）获得迭代广义解调谱（IGDS），以识别与提取的IFCF对应的故障类型。基于获得的故障类型和FCC比率，计算新的PF和频率点（FP）以检测其他故障。仿真和实验结果均验证了在无需测量和提取RF的情况下，能够有效识别时变转速下滚动轴承的多故障特征。
