Application of the bispectrum for detection of small nonlinearities excited sinusoidally

Courtney, C. R. P.; Neild, Simon A; Wilcox, Paul D.; Drinkwater, Bruce W.

doi:10.1016/j.jsv.2010.04.031

Cited by 54 publications

(28 citation statements)

References 34 publications

(56 reference statements)

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“…Ultrasonic scattered signals are not simply deterministic or stochastic, but rather a combination of both [8]. Predictability can be viewed as the signal strength of the deterministic component of the time series with respect to the whole time series.…”

Section: Analysing the Predictability Of Ultrasonic Scattered Signalsmentioning

confidence: 99%

Measuring predictability in ultrasonic signals: An application to scattering material characterization

2014

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In this paper, we present a novel and completely di↵erent approach to the problem of scattering material characterization: measuring the degree of predictability of the time series. Measuring predictability can provide information of the signal strength of the deterministic component of the time series in relation to the whole time series acquired. This relationship can provide information about coherent reflections in material grains with respect to the rest of incoherent noises that typically appear in non-destructive testing using ultrasonics. This is a non-parametric technique commonly used in chaos theory that does not require making any kind of assumptions about attenuation profiles. In highly scattering media (low SNR), it has been shown theoretically that the degree of predictability allows material characterization. The experimental results obtained in this work with 32 cement probes of 4 di↵erent porosities demonstrate the ability of this technique to do classification. It has also been shown that, in this particular application, the measurement of predictability can be used as an indicator of the percentages of porosity of the test samples with great accuracy.Keywords: Ultrasonic Signal Modality, Ultrasonic NDT, Multiple scattering, Noise, Determinism, Chaos Theory, Higher order statistics IntroductionWhen scattering materials are subject to ultrasonic non-destructive testing (NDT), the ultrasonic pulse undergoes some variations that are related to the internal grain microstructure of the specimen. Each grain behaves like a scattering center, producing an echo that when superimposed on other echoes coming from other grains can even hide the echoes produced by a possible defect. Similar situations are found in other related fields such as ultrasound B-mode scans (where the grain noise is called speckle) and in radar with clutter [1].In the literature, a wide range of solutions has been proposed to enhance the detection of small cracks or defects and to reduce (or even eliminate) the e↵ect discussed in each of the above situations. Some of these solutions are signal averaging, auto-and cross-correlation, matched filtering, frequency spectrum analysis [2], spectral correlation [3], and wavelet transformations [4]. The aforementioned analyses discard the information encoded in the grain noise; however, this information can be used to recognize potential di↵erences among materials, tissues, or surfaces. This approach has been employed to characterize materials by extracting temporal signal statistics [1], the resonance frequency [5], and even the penetration depth [6]. Our work continues with this line of thought and proposes a new approach that attempts to extract information about the nature of the signal, thereby characterizing the signal modality. Signal modality characterization is a key concept of a multidisciplinary research topic that includes di↵erent concepts such as signal linearity, stationarity, and stochasticity [7]. The applications of signal modality characterization are becoming mo...

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Section: Analysing the Predictability Of Ultrasonic Scattered Signalsmentioning

confidence: 99%

Measuring predictability in ultrasonic signals: An application to scattering material characterization

2014

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“…At low amplitudes, discrepancies are evident, given by non negligible effects of the term n a ðtÞ in Eq. (10).…”

Section: B Effects Of Ambient Noisementioning

confidence: 99%

“…10 The contribution due to nonlinear features in the propagation medium is often very small. As a consequence, the nonlinear-to-linear ratio (NLR), defined as the ratio of the nonlinear over the linear signal, is often smaller than the SNR, and nonlinear effects are submerged into the noise level, even though recorded signals might be well above noise.…”

Section: Introductionmentioning

confidence: 99%

Influence of noise on the threshold for detection of elastic nonlinearity

Bentahar

Guerjouma

Idijmarene

et al. 2013

Journal of Applied Physics

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Wheel/rail noise generation due to nonlinear effects and parametric excitationNonlinear effects on the propagation of elastic waves in hysteretic media are of great importance, with applications in imaging and monitoring evolution of damage/phase transitions/biological features. However, excitation amplitudes are an issue in order to improve performances of the techniques. Noise effects in the signals can be stronger than the generated nonlinear effects, thus rendering difficult the nonlinear analysis. Here, we analyze the link between the amplitude threshold for detection of nonlinear effects and different kinds of noises, which might be present in experiments. We also discuss the implications in the intrpretation of experiments. V C 2013 American Institute of Physics. [http://dx.

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“…According to definition functions (26) and (27) we know that K i expresses the generality of i-class objects, in other words is the public section, which constructs the common features of the object. D i expresses the distribution of i-class object bispectrum, that is to say, this domain includes all the bispectrum binary feature images.…”

Section: Construct the Target Templatementioning

confidence: 99%

Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation

Liu

Han

2013

Shock and Vibration

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Abstract. A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higherorder spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples' binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. In the experimental part, the rolling bearing fault diagnosis experiment results proved that the classification was completely correct.

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Application of the bispectrum for detection of small nonlinearities excited sinusoidally

Cited by 54 publications

References 34 publications

Measuring predictability in ultrasonic signals: An application to scattering material characterization

Measuring predictability in ultrasonic signals: An application to scattering material characterization

Influence of noise on the threshold for detection of elastic nonlinearity

Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation

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