Detection and localization of damage using empirical mode decomposition and multilevel support vector machine

Abstract

Damage in the structure may raise a significant amount of maintenance cost and serious safety problems. Hence detection of the damage at its early stage is of prime importance. The main contribution pursued in this investigation is to propose a generic optimal methodology to improve the accuracy of positioning of the flaw in a structure. This novel approach involves a two-step process. The first step essentially aims at extracting the damagesensitive features from the received signal, and these extracted features are often termed the damage index or damage indices, serving as an indicator to know whether the damage is present or not. In particular, a multilevel SVM (support vector machine) plays a vital role in the distinction of faulty and healthy structures. Formerly, when a structure is unveiled as a damaged structure, in the subsequent step, the position of the damage is identified using Hilbert-Huang transform. The proposed algorithm has been evaluated in both simulation and experimental tests on a 6061 aluminum plate with dimensions 300 mm 9 300 mm 9 5 mm which accordingly yield considerable improvement in the accuracy of estimating the position of the flaw.

Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study

“…Simon Carbajo et al 35 presented a new automated structural change detection algorithm by extraction of damage-sensitive features of IFs and signal energies from HHT that were applied in an experimental single degree of freedom (DOF) system and a wind turbine blade under band-limited base excitation. Dushyanth et al 36 evaluated the HHT method to pinpoint the location of damage, in both simulation and experimental tests, and used a set of damage indices and a multi-level support vector machine to distinguish the healthy or damaged state of an aluminum plate. The authors concluded that the proposed detection method can be applied to real-time applications due to its high accuracy and the considerable decrease in the computational time.…”

Rail Flaw Detection via Kolmogorov Entropy of Chaotic Oscillator Based on Ultrasonic Guided Waves

“…Due to the dispersive, multimodal, and attenuating characteristics of UGWs in long range detection, actual sampled guided wave signals often appear as weak signals against a background of strong noise. Scholars have extensively researched signal processing methods for UGWs, including time-frequency analysis [2], such as short-time Fourier transform [3], 2D Fourier transform [4], wavelet transform [5,6], Hilbert-Huang transform [7], empirical modal decomposition [8], Wigner-Ville distribution [9], and artificial neural networks [10], as well as dispersion compensation methods [11], time inversion focusing methods [12], etc. Most of the above methods use noise suppression techniques to reduce the noise of the target signal and the noise signal superimposed on the overlapped signal, which can reduce the sensitivity of damage detection.…”

Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine

“…Damage-sensitive features were extracted from the wavelet based autoregressive coefficients and then the nonlinear multiclass SVM was employed to estimate the damage severity. Dushyanth et al [26] developed a new multilevel SVM and stated that the proposed SVM method required much less training data as compared to other methods. Gui et al [27] presented three optimization algorithm based SVMs for damage detection.…”