Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions

Dezun Zhao; Jianyong Li; Weidong Cheng; Weigang Wen

doi:10.1016/j.isatra.2022.06.047

Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions

ISA Trans. 2023 Feb:133:518-528. doi: 10.1016/j.isatra.2022.06.047. Epub 2022 Jul 4.

Authors

Dezun Zhao¹, Jianyong Li², Weidong Cheng³, Weigang Wen⁴

Affiliations

¹ Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
² 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.
³ School of Mechanical Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China. Electronic address: wdcheng@bjtu.edu.cn.
⁴ School of Mechanical Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.

PMID: 35843740
DOI: 10.1016/j.isatra.2022.06.047

Abstract

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.

Keywords: Iterative generalized demodulation; Multi-fault diagnosis; Rolling bearing; Rotational frequency detection; Variable rotational speed.