Abstract
In this paper four non-parametric and five parametric signal processing techniques are reviewed and their performances are compared through application to a sample exponentially damped synthetic signal with closely-spaced frequencies representing the ambient response of structures. The non-parametric methods are Fourier transform, periodogram estimate of power spectral density, wavelet transform, and empirical mode decomposition with Hilbert spectral analysis (Hilbert-Huang transform). The parametric methods are pseudospectrum estimate using the multiple signal categorization (MUSIC), empirical wavelet transform, approximate Prony method, matrix pencil method, and the estimation of signal parameters by rotational invariance technique (ESPRIT) method. The performances of different methods are studied statistically using the Monte Carlo simulation and the results are presented in terms of average errors of multiple sample analyses.
Highlights
Vibration-based structural health monitoring (SHM) has become increasingly popular in recent years as a general and global method to detect possible damage scenarios [1,2,3,4] unlike optical observations that can detect only superficial and localized damage [5, 6] or nondestructive testing methods such as radiography or -ray [7]
Similar to parametric and non-parametric system identification categorization based on the prior knowledge of the model, signal processing methods are divided into parametric and nonparametric methods based on the knowledge of the signal source [8, 9]
While detection of the stationary content of the signals have been vastly studied over many decades non-stationary and exponentially damped content detection is a relatively newer concept
Summary
Vibration-based structural health monitoring (SHM) has become increasingly popular in recent years as a general and global method to detect possible damage scenarios [1,2,3,4] unlike optical observations that can detect only superficial and localized damage [5, 6] or nondestructive testing methods such as radiography or -ray [7]. The main step in any SHM approach is the processing of collected response signals (Sun et al., 2015) In this stage, signals are analyzed to extract specific features such as modal parameters or directly create a model that fits the data as in system identification (SI). The second approach and the use of signal processing methods to extract features, natural frequencies and damping coefficients, is the focus of this paper. These features can further be used directly to detect damages through classification algorithms or used to create an updated model of the structure as in the finite element model updating approach. The performances of different methods are studied statistically using the Monte Carlo simulation and the results are presented in terms of average errors of multiple sample analyses
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
