Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings

Yongbo Li, Minqiang Xu, Xihui Liang, Wenhu Huang

Research output: Contribution to journalArticlepeer-review

221 Scopus citations

Abstract

This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.

Original languageEnglish
Article number7812757
Pages (from-to)6506-6517
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Volume64
Issue number8
DOIs
StatePublished - Aug 2017
Externally publishedYes

Keywords

  • Adaptive multiscale morphological analysis (AMMA)
  • bandwidth empirical mode decomposition (BEMD)
  • fault signature extraction
  • incipient fault diagnosis

Fingerprint

Dive into the research topics of 'Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings'. Together they form a unique fingerprint.

Cite this