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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2021, Vol. 42 ›› Issue (11): 524845-524845.doi: 10.7527/S1000-6893.2021.24845

• Review • Previous Articles     Next Articles

Recent advances of fault diagnosis methods based on transmissibility function for mechanical structures

LI Quankun1,2, JING Xingjian2   

  1. 1. School of Power and Energy, Northwestern Polytechnical University, Xi'an 710129, China;
    2. Department of Mechanical Engineering, Hong Kong Polytechnic University, Hong Kong 999077, China
  • Received:2020-10-08 Revised:2020-12-05 Published:2021-06-08
  • Supported by:
    The Fundamental Research Funds for the Central Universities (3102020OQD705); The General Research Fund (GRF) Project of Hong Kong Research Grants Council (RGC) (15206717)

Abstract: The importance of fault diagnosis to health monitoring of mechanical structures draws extensive attention from researchers to diagnosis methods. Transmissibility function based methods, compared with other diagnosis methods, are much simpler and provide more sensitive and effective damage features and indexes. According to different transmissibility functions adopted, either linear or nonlinear, transmissibility function based methods can be classified into four categories:general linear methods, general nonlinear methods, methods based on Generalized Frequency Response Function (GFRF) and methods based on the Second Order Output Spectrum (SOOS). The main purpose of this article is to provide a comprehensive review on recent advances of fault diagnosis methods based on linear and nonlinear transmissibility functions, explain their basic principles and procedures through nonlinear multiple degrees of freedom models, demonstrate their merits and demerits with experimental results on a bolted satellite-like structure, and discuss their improvement in sensitivity of related damage indicators and potential applications to some complex structures.

Key words: linear faults, nonlinear faults, transmissibility functions, fault detection, fault location

CLC Number: