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S. Loutridis

Bio: S. Loutridis is an academic researcher from Technological Educational Institute of Larissa. The author has contributed to research in topics: Wavelet transform & Vibration. The author has an hindex of 16, co-authored 21 publications receiving 1351 citations. Previous affiliations of S. Loutridis include Aristotle University of Thessaloniki.

Papers
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Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: In this article, a method for monitoring the evolution of gear faults based on the newly developed empirical mode decomposition scheme is presented, which can be used for system failure prediction and is shown that the instantaneous frequency of the vibration signal is a sensitive indicator of the existence of damage in the gear pair.

220 citations

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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

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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

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TL;DR: In this article, a new method for crack detection in beams based on instantaneous frequency and empirical mode decomposition is proposed, which can be used to improve the effectiveness of vibration-based crack detection techniques.
Abstract: In this paper, a new method for crack detection in beams based on instantaneous frequency and empirical mode decomposition is proposed. The dynamic behaviour of a cantilever beam with a breathing crack under harmonic excitation is investigated both theoretically and experimentally. A simple single-degree-of-freedom system with varying stiffness is employed to simulate the dynamic behaviour of the beam. The time-varying stiffness is modelled using a simple periodic function. Both simulated and experimental response data are analysed by applying empirical mode decomposition and Hilbert transform and the instantaneous frequency of each oscillatory mode is obtained. It is shown that the instantaneous frequency oscillates between frequencies corresponding to the open and closed states revealing the breathing of the crack. The variation of the instantaneous frequency increases with increasing crack depth following a polynomial law and consequently can be used for estimation of crack size. Using the intrinsic modes of the system, the harmonic distortion of the distorted sinusoidal response is calculated. It follows that the harmonic distortion increases with crack depth following definite trends and can be also used as an effective indicator for crack size. The proposed time–frequency approach is superior compared to Fourier analysis and can be used to improve the effectiveness of vibration-based crack detection techniques.

121 citations


Cited by
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Journal ArticleDOI
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

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TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.

1,410 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the PHM field is provided, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information, to enable rapid customization and integration of PHM systems for diverse applications.

1,164 citations

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TL;DR: A feature learning model for condition monitoring based on convolutional neural networks is proposed to autonomously learn useful features for bearing fault detection from the data itself and significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier.

871 citations

Journal ArticleDOI
TL;DR: A systematic review of over 20 major time-frequency analysis methods reported in more than 100 representative articles published since 1990 can be found in this article, where their fundamental principles, advantages and disadvantages, and applications to fault diagnosis of machinery have been examined.

719 citations