Structural damage identification by using wavelet entropy

Ren, Wang; Sun, Zhilin

doi:10.1016/j.engstruct.2008.03.013

Cited by 126 publications

(102 citation statements)

References 18 publications

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“…For example, the trend of information entropy of the vibration signals is studied by Elforjani [5] for fault diagnosis of ball bearings. Ren and Sun [6] also proposed a structural damage identification method based on the entropy of the wavelet transformed vibration signals. This paper aims at studying the behavior of the information entropy of the AE signals during the fatigue damage evolution of titanium specimens tested.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission entropy as a measure of damage in materials

Kahirdeh¹,

Sauerbrunn²,

Modarres³

2016

AIP Conference Proceedings

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Abstract. In this paper, we propose the information entropy of acoustic emission (AE) signatures as an index for assessment of degradation in materials. This is a new method for health monitoring of materials and structures that relies on the estimation of the information entropy from the AE signals detected, for example from cyclic fatigue loading of the materials. Different ways of calculation of the information entropy from AE signals are explored and discussed in this paper. As an example of the materials degradation, this study investigates the behavior of maximum entropy ( ) at the time of the materials' failure using a series of fatigue cracking tests on titanium alloy, a material commonly used in certain aircraft structures. This paper describes the fatigue tests in which AE waves and features were measured and discusses the various approaches to estimate the information entropy corresponding to the detected AE. The results show that fatigue failure occurs at the same maximum entropy value regardless of the trajectory of the fatigue crack growth and loading conditions that caused the fatigue.

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Section: Introductionmentioning

confidence: 99%

Acoustic emission entropy as a measure of damage in materials

Kahirdeh¹,

Sauerbrunn²,

Modarres³

2016

AIP Conference Proceedings

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“…Other specifications of the generalized information entropy such as wavelet information entropy and multiscale permutation information entropy have also been formulated and applied for applications of mechanical and structural damage diagnosis [34][35][36].…”

Section: Generalized Information Entropymentioning

confidence: 99%

“…Information entropy only depends on the probability mass function of the system symbol sequence and it increases with the increasing uniformity of the probability mass function. The above concept of information entropy has been generalized in a number of different ways by different researchers [10,[33][34][35][36] since the pioneering work of Shannon. In all, generalized information entropy H can be formulated as:…”

Section: Generalized Information Entropymentioning

confidence: 99%

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

Zhang

et al. 2014

Entropy

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Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor errors must be taken into account. This paper presents an information exergy index for bearing performance degradation assessment that combines singular value decomposition (SVD) and the information exergy method. Information exergy integrates condition monitoring information of multiple instants and multiple sensors, and thus performance degradation assessment uncertainties are reduced and robust degradation assessment results can be obtained using the proposed index. The effectiveness and robustness of the proposed information exergy index are validated through experimental case studies.

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“…Entropy, for instance, has been used to quantify nonlinearities and complexity in electroencephalogram (EEG) signals [40], event-related potentials in neuroelectrical signals [41,42], structural damage identification [43] and characterization of complexity in random processes [44][45][46]. In order to obtain an entropy estimate from measured data, a probability mass function (pmf) is required.…”

Section: Wavelet-tsallis Q-entropymentioning

confidence: 99%

Wavelet-Tsallis Entropy Detection and Location of Mean Level-Shifts in Long-Memory fGn Signals

Ramírez-Pacheco

Rizo-Dominguez

Cortez

2015

Entropy

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Long-memory processes, in particular fractional Gaussian noise processes, have been applied as models for many phenomena occurring in nature. Non-stationarities, such as trends, mean level-shifts, etc., impact the accuracy of long-memory parameter estimators, giving rise to biases and misinterpretations of the phenomena. In this article, a novel methodology for the detection and location of mean level-shifts in stationary long-memory fractional Gaussian noise (fGn) signals is proposed. It is based on a joint application of the wavelet-Tsallis q-entropy as a preprocessing technique and a peak detection methodology. Extensive simulation experiments in synthesized fGn signals with mean level-shifts confirm that the proposed methodology not only detects, but also locates level-shifts with high accuracy. A comparative study against standard techniques of level-shift detection and location shows that the technique based on wavelet-Tsallis q-entropy outperforms the one based on trees and the Bai and Perron procedure, as well.

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Structural damage identification by using wavelet entropy

Cited by 126 publications

References 18 publications

Acoustic emission entropy as a measure of damage in materials

Acoustic emission entropy as a measure of damage in materials

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

Wavelet-Tsallis Entropy Detection and Location of Mean Level-Shifts in Long-Memory fGn Signals

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