A new automatic diagnosis method based on the multivariable analysis for structural faults of rotary machinery

  • G.Y. Guan Mie University, 1577 Kurimamachiya-cho, Tsu-shi, Japan,
  • L.Y. Song
  • H.Q. Wang
  • K. Li
  • P. Chen
Keywords: Rotating machinery, structural fault, symptom parameters, principal component analysis

Abstract

To effectively detect and identify the structural faults of the rotating machinery, a new kind of symptom parameters of the vibration signal measured in multiple directions is proposed (hereinafter referred to as the structural feature symptom parameter SFSP), the method to extract anomaly signals based on the multi-band filter, the method to enhance the sensitivity of the symptom parameters using the least square mapping, and the automatic fault diagnosis system established for the structural faults of the rotating machinery in combination with multivariate analysis (principal component analysis) method. In addition, the validity of the diagnostic method was also verified with reference to the experimental data measured on the rotating machine in different states of structural faults.
Published
2018-02-12
Section
Articles