Author
A. Trochidis
Bio: A. Trochidis is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Beam (structure) & Wavelet. The author has an hindex of 13, co-authored 18 publications receiving 1048 citations.
Papers
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TL;DR: In this article, the fundamental vibration mode of a cracked cantilever beam is analyzed using continuous wavelet transform and both the location and size of the crack are estimated by the sudden change in the spatial variation of the transformed response.
286 citations
TL;DR: In this paper, a method for crack identification in plates based on wavelet analysis is presented and the viability of the proposed approach is demonstrated using simulation examples.
148 citations
TL;DR: In this article, the fundamental vibration mode of a double-cracked cantilever beam is analyzed using continuous wavelet transform and both the location and depth of the cracks are estimated.
137 citations
TL;DR: In this paper, the fundamental vibration mode of a cracked cantilever beam is analyzed and both the location and size of the crack are estimated by the sudden changes in the spatial variation of the analysed response, while the size of a crack is related to the fractal dimension measure.
121 citations
TL;DR: In this paper, a two-dimensional wavelet transform is used for identification of cracks in plate structures, where the vibration mode of a cracked plate is transformed to the wavelet domain and both the location and extent of the crack are accurately displayed in the transformed response.
103 citations
Cited by
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TL;DR: A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented in this paper, and the damage identification algorithms in terms of signal processing are discussed.
Abstract: A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing...
1,613 citations
TL;DR: Time-frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications and the expectation is that further research and applications of these algorithms will flourish in the near future.
646 citations
TL;DR: Structural health monitoring and damage detection techniques are tools of great importance in the off-shore, civil, mechanical and aeronautical engineering communities, both for safety reasons and because of the economic benefits that can result.
Abstract: Structural health monitoring and damage detection techniques are tools of great importance in the off-shore, civil, mechanical and aeronautical engineering communities, both for safety reasons and because of the economic benefits that can result. The need to be able to detect damage in complex structures has led to the development of a vast range of techniques, of which many are based upon structural vibration analysis. In the present article, some of the latest advances in Structural Health Monitoring and Damage Detection are reviewed, with an emphasis on composite structures on the grounds that this class of materials currently has a wide range of engineering applications. FOREWORD-It should be noted that this review is not intended to be a general, all-encompassing review covering the whole range of structural health monitoring (SHM); it was planned as the starting point for a study focusing on damage detection, localization and assessment for certain kinds of structure. Thus, the line of thought behind the search and the structure of this review is a result of objectives beyond the scope of the paper itself. Nevertheless, it was considered that, once the above was understood, an updated synopsis such as this could also be useful for other researchers in the same field. ©2006 SAGE Publications.
468 citations
TL;DR: In this article, a method for estimating the damage location in beam and plate structures is presented, where the estimated mode shapes of the beam are analyzed by the one-dimensional continuous wavelet transform.
374 citations
TL;DR: This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area.
Abstract: This paper reviews the state-of-the-art of wavelet analysis for tool condition monitoring (TCM). Wavelet analysis has been the most important non-stationary signal processing tool today, and popular in machining sensor signal analysis. Based on the nature of monitored signals, wavelet approaches are introduced and the superiorities of wavelet analysis to Fourier methods are discussed for TCM. According to the multiresolution, sparsity and localization properties of wavelet transform, literatures are reviewed in five categories in TCM: time–frequency analysis of machining signal, signal denoising, feature extraction, singularity analysis for tool state estimation, and density estimation for tool wear classification. This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area.
348 citations