Acoustic Emission for Civil Structures

Ziehl, Paul; Pollock, Adrian A.

doi:10.5772/32652

Cited by 11 publications

(12 citation statements)

References 36 publications

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“…AE has been used for the monitoring of bridges since the 1970s. The technique has been applied to assess and monitor a wide range of structural components in bridges such as: steel elements, concrete elements, prestressed elements, cables, anchorages, among others [19][20][21][22][23]. For instance, Hadzor, Barnes, et al [23] applied AE techniques for the assessing of the structural integrity of prestressed girders located in an elevated section of the I-565 highway in Alabama.…”

Section: Introductionmentioning

confidence: 99%

Deterministic and probabilistic fatigue prognosis of cracked specimens using acoustic emissions

Zárate¹,

Caicedo²,

Yu³

et al. 2012

Journal of Constructional Steel Research

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Section: Introductionmentioning

confidence: 99%

Deterministic and probabilistic fatigue prognosis of cracked specimens using acoustic emissions

Zárate¹,

Caicedo²,

Yu³

et al. 2012

Journal of Constructional Steel Research

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“…in inhomogeneous materials, such as fiber-reinforced composites and to qualify the fracture mechanical behavior of materials or the structural-mechanical state of machine parts, or health monitoring, as it is usually called. [1][2][3][4] The AE signal generated by an energy release which is induced by a sudden irreversible local change in mechanically loaded material is a burst type (Figure 1) vibration of 30-600 kHz. The local change is usually crazing or micro-cracking.…”

Section: Introductionmentioning

confidence: 99%

“…The vibration can be detected with suitable sensors and converted into an electric signal. [1][2][3][4] The AE signal may be a continuous and/or long damping vibration but its source is usually an external noise. On the other hand, acoustic emission of lower frequency (0,1-1,0 kHz) can also be generated by e.g.…”

Section: Introductionmentioning

confidence: 99%

“…5,6 Similar vibrations can be observed in vehicle parts such as airplane wings or a fuselage. [1][2][3][4] Figure 1. Numerical descriptors of a burst type AE signal in the time domain AE signal processing AE signals are detected as a function of time when they exceed a given threshold level (Figure 1), hence the time domain characteristics of single AE signals (h) are fundamental.…”

Section: Introductionmentioning

confidence: 99%

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Novel evaluation method of acoustic emission data based on statistical fiber bundle cells

Vas

Kocsis²,

Czigány

et al. 2019

Journal of Composite Materials

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In general, the aim of acoustic emission (AE) assisted tensile tests of composite materials is to identify and characterize the damage and failure modes of the specimens. This paper presents a fiber-bundle-cells (FBC) based statistical model, which provides a possible solution to the problem of characterizing the mechanical and failure behavior of the material. The model, based on the results of mechanical tests and AE measurements, decomposes the measured AE event number and tensile force-load time processes into components corresponding to the different damage modes. The AE events belonging to different failure modes are described by inhomogeneous Poisson point processes, while failures are modeled with the breakage of fibers as elementary parts of the sample. Hence damage modes can be characterized with the number fraction, and the tensile strength and signal energy distributions of the components. Moreover, the variation of the number fraction of the intact or damaged fibers as a function of the load time can be calculated and depicted as well. As reliability function or a kind of damage map, it reveals the mechanical load-bearing ability of the material tested. The applicability of the model is demonstrated by compact tension testing and the comparison of short and long glass fiber reinforced VERTON PP sheets and injection molded wood fiber reinforced PP composites.With the help of fast Fourier transformation (FFT), the frequency components of the single AE signals can be characterized by the amplitude or power spectrum in the frequency domain. Their description parameters are the bandwidth (B), the mean frequency (fm), the frequency of the maximum amplitude component (fP), and the amplitude (AP) or energy (UP) of the maximum peak. Similar parameters can be obtained from the mean spectra of a series of AE signals, and histograms and/or cross-plots can be calculated from the data of single AE signals. 2,4 Special techniques can provide information in both the time and the frequency domain, resulting in time-dependent spectrum statistics. 2,4 The classic method is the short time FFT (SFFT) technique, which uses a moving time window. In the case of wavelet transformations (WT), AE signals are analyzed with the help of various wavelet components. The signal transformations mentioned above provide additional or in itself usable information about the sources and failure modes in the frequency domain [21][22][23][24][25][26] or both in the time and the frequency domains [27][28][29][30][31][32][33][34][35][36] . In the latter case, wavelet transformations based on Haar 28 or Gabor 27, 33 wavelet components as well as Hilbert-Huang transform 30 can be used to decompose the AE signals and calculate the energy of the components. [27][28][29][30][31][32][33][34][35][36] In the case of thin plates, modal analysis can be applied to study and separate extensional and flexural Lamb waves. 37,38 The finite element method (FEM) can give a good base for wave discrimination. 38 The localization of a single AE signal in the spe...

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“…On the contrary, acoustic emissions in the frequency range around or above 100 kHz are generally characterised by a lower number of noise sources, thus enabling to detect the progression of damage as it happens [7]. AE, as a result, has been taken as a formalized structural evaluation method in civil applications since the early 1980's and it is very popular in the integrity monitoring of reinforced polymeric vessels [48,49].…”

Section: Acoustic Emissionmentioning

confidence: 99%

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan¹

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Keywords AbstractBearings are one of the most commonly-used and crucial components in the majority of rotating machines in industrial applications. Failure of this component is common and can lead to machine breakdown. If it is not detected in time, bearing faults can even lead to catastrophic failures. An effective diagnostic system provides early warning and fault identification such that any machine breakdown can be avoided. Condition monitoring can benefit plant operators resulting in shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning.However, machines and associated systems today are increasingly complex, while diagnostic techniques have been mainly applied to relatively simple rotating machines. Therefore, there is a need for research on condition monitoring techniques that can be applied to complex systems in variable operating conditions. This thesis presents a study on bearing condition monitoring under variable operating condition using Support Vector Machines. Data collected from multiple sensors including accelerometers, acoustic emission sensors and tachometers are used for the studies presented in this thesis. Support Vector Machines are an efficient method for classification and prediction, and shows outstanding performance in modelling and generalization.The study begins with collecting bearing condition monitoring data. Vibration analysis is a well-established technique, while acoustic emission is gaining an increasing amount of attention especially on its capability to detect bearing incipient fault such as subsurface cracks. Existing studies so far have not demonstrated AE's diagnostic capabilities using naturally-damaged bearing, and only a very few studies adopt envelope and cyclostationary analysis on bearing fault diagnosis with this sensor technology. Due to the second-order cyclostationary nature of bearing signals and the success of envelope analysis in analysing bearing signals, the diagnostic and prognostic studies presented in this thesis make use of both envelope analysis and cyclostationary analysis to analyse vibration and acoustic emission bearing signals that will be collected from bearing diagnostics and run-to-failure prognostic experiments through a bearing diagnostic test-rig and a bearing prognostic test-rig.Using the collected data, the effectiveness and diagnostic capability of cyclostationary analysis will first be assessed using feature analysis with support vector machine. A diagnostic study is then conducted, investigating the diagnostic effectiveness of acoustic emission and its superiority to vibration, especially in the detection of bearing subsurface cracks. Through comparison of AE with vibration signals, this study confirms AE's superiority in detecting subsurface cracks in bearings and the absence of vibration symptoms of subsurface cracks.This diagnostic study has also shown the crucial ability of the cyclic logarithmic envelope iii spectrum in separating different cyc...

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Acoustic Emission for Civil Structures

Cited by 11 publications

References 36 publications

Deterministic and probabilistic fatigue prognosis of cracked specimens using acoustic emissions

Deterministic and probabilistic fatigue prognosis of cracked specimens using acoustic emissions

Novel evaluation method of acoustic emission data based on statistical fiber bundle cells

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

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