Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram

Chuan Li, Diego Cabrera, José Valente De Oliveira, René Vinicio Sanchez, Mariela Cerrada, Grover Zurita

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Local faults of rotating machinery usually result in repetitive transients whose impulsiveness or cyclostationarity can be employed as faulty signatures. However, to simultaneously accommodate the impulsiveness and the cyclostationarity is a challenging task for rotating machinery diagnostics. Inspired by recently-reported infogram that is sensitive to either the impulsiveness or the cyclostationarity using spectral negentropy defined in time domain or frequency domain, a multiscale clustering grey infogram (MCGI) is proposed by combining both negentropies in a grey fashion using multiscale clustering. Fourier spectrum of the vibration signal is decomposed into multiple scales with different initial resolutions. In each scale, fine segments are grouped using hierarchical clustering. Meanwhile, both time-domain and frequency-domain spectral negentropies are taken into account to guide the clustering through grey evaluation of both negentropies. Numerical simulations and experimental tests are carried out for validating the proposed MCGI. For comparison, peer methods are applied to challenge different noises and interferences. The results show that, thanks to the multiscale clustering of the spectrum and the grey evaluation of both negentropies, the present MCGI is robust in extracting the repetitive transients for the rotating machinery diagnosis.

Original languageEnglish
Pages (from-to)157-173
Number of pages17
JournalMechanical Systems and Signal Processing
Volume76-77
DOIs
StatePublished - 1 Aug 2016

Bibliographical note

Funding Information:
This work is supported in part by the Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation of the Republic of Ecuador, the National Natural Science Foundation of China (51375517), and the Project of Chongqing Science and Technology Commission (cstc2015jcyjA70007, cstc2015jcyjA90003). The valuable comments and suggestions from the editors and the two reviewers are very much appreciated.

Publisher Copyright:
© 2016 Elsevier Ltd. All rights reserved.

Keywords

  • Fault diagnosis
  • Multiscale clustered grey infogram
  • Repetitive transient
  • Rotating machinery
  • Spectral negentropy

Fingerprint

Dive into the research topics of 'Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram'. Together they form a unique fingerprint.

Cite this