Higher order statistical frequency domain decomposition for operational modal analysis

Gelu Nita, Mohamed Mahgoub, S. Sharyatpanahi, N. Cretu, T. M. El-Fouly

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Experimental methods based on modal analysis under ambient vibrational excitation are often employed to detect structural damages of mechanical systems. Many of such frequency domain methods, such as Basic Frequency Domain (BFD), Frequency Domain Decomposition (FFD), or Enhanced Frequency Domain Decomposition (EFFD), use as first step a Fast Fourier Transform (FFT) estimate of the power spectral density (PSD) associated with the response of the system. In this study it is shown that higher order statistical estimators such as Spectral Kurtosis (SK) and Sample to Model Ratio (SMR) may be successfully employed not only to more reliably discriminate the response of the system against the ambient noise fluctuations, but also to better identify and separate contributions from closely spaced individual modes. It is shown that a SMR-based Maximum Likelihood curve fitting algorithm may improve the accuracy of the spectral shape and location of the individual modes and, when combined with the SK analysis, it provides efficient means to categorize such individual spectral components according to their temporal dynamics as harmonic or stochastic system responses to unknown ambient excitations.

Original languageEnglish (US)
Title of host publicationSHMII 2015 - 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure
PublisherInternational Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
StatePublished - Jan 1 2015
Event7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2015 - Torino, Italy
Duration: Jul 1 2015Jul 3 2015

Other

Other7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2015
CountryItaly
CityTorino
Period7/1/157/3/15

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Civil and Structural Engineering
  • Artificial Intelligence

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