Material discrimination using bispectral signatures

Nyffenegger, Paul A.; Hinich, Melvin J.; Ritter, Dillon; Hansen, Stephen

doi:10.1121/1.1780169

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…From a detection perspective it is almost always preferable to focus on the largest peak (the exception being the rare case where the variance of the larger peak makes it more difficult to detect than the smaller peak). From this point forward the detector based on the peak located at (f 1 = f (1) n , f 2 = f (1) n ) will be referred to as the "peak 1" detector while the peak located at (f 1 = f (2) n , f 2 = f (1) n ) will be referred to as the "peak 2" detector. Figure (4) provides the detector performance for both peak 1 and peak 2 detectors as a function of the nonlinearity parameter k non .…”

Section: Resultsmentioning

confidence: 99%

“…In structural dynamics, Nyffenegger et al 2 used estimates of the bispectrum for discriminating among cylinders comprised of different materials. Worden and Tomlinson 3 used estimates of the auto-bispectrum to detect different types of nonlinearity in a N-DOF spring-mass system while Messina and Vittal 4 used estimates of the auto-bispectrum to detect nonlinear mode interaction in a power system.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinearity detection in multiple degree-of-freedom systems using the auto-bispectral density

Nichols

Marzocca

Milanese

2008

Health Monitoring of Structural and Biological Systems 2008

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Higher-order spectra (HOS) appear often in the analysis and identification of nonlinear systems. The autobispectrum is one example of a HOS and is frequently used in the analysis of stationary structural response data to detect the presence of structural nonlinearities. In this work we derive an expression for the auto-bispectrum of a multi-degree-of-freedom structure with quadratic nonlinearities. A nonlinearity detection strategy, based on estimates of the bispectrum, is then described. The performance of several such detectors is quantified using Receiver Operator Characteristic (ROC) curves illustrating the trade-off between Type-I error and power of detection (1-Type-II error).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinearity detection in multiple degree-of-freedom systems using the auto-bispectral density

Nichols

Marzocca

Milanese

2008

Health Monitoring of Structural and Biological Systems 2008

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“…Examples include ultrasonic (Peng et al, 2012;Su et al, 2014), thermographic (Kroeger, 2014), infrared imaging (Vavilov et al, 2015), visual (Bossi and Giurgiutiu, 2015), acoustic (Sarasini and Santulli, 2014), and electromagnetic (Yang et al, 2013) techniques. Among all these possibilities, the one that is closer to the approach proposed in this work is the exploitation of acoustic pulses (Nyffenegger et al, 2004). In a similar way, ultrasounds are used in the nondestructive evaluation and classification of different materials.…”

Section: Introductionmentioning

confidence: 96%

Discriminating different materials by means of vibrations

Baldi

Marullo²,

D'Aurizio³

et al. 2022

Front. Manuf. Technol.

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Material characterization and discrimination is of interest for multiple applications, ranging from mechanical engineering to medical and industrial sectors. Despite the need for automated systems, the majority of the existing approaches necessitate expensive and bulky hardware that cannot be used outside ad-hoc laboratories. In this work, we propose a novel technique for discriminating between different materials and detecting intra-material variations using active stimulation through vibration and machine learning techniques. A voice-coil actuator and a tri-axial accelerometer are used for generating and sampling mechanical vibration propagated through the materials. Results of the present analysis confirm the effectiveness of the proposed approach. Processing a mechanical vibration signal that propagates through a material by means of a neural network is a viable means for material classification. This holds not only for distinguishing materials having gross differences, but also for detecting whether a material underwent some slight changes in its structure. In addition, mechanical vibrations at 500 Hz demonstrated an ability to provide a compact and meaningful representation of the data, sufficient to categorize 8 different materials, and to distinguish reference materials from other defective materials, with an average accuracy greater than 90%.

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“…Material discrimination plays a significant role in material processing industries and plants [25]. The existing techniques, such as acoustic pulses [26], spectral X-ray imaging [27], optical spectroscopy techniques [28], are expensive, time-consuming, require sophisticated instruments and a trained person. The existing techniques are not suitable for regular laboratory experiments, medium and small-scale industries.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of 2D ZnO-based piezoelectric nanogenerator and its application in non-destructive material discrimination

Supraja¹,

Sankar²,

Kumar³

et al. 2021

Adv. Nat. Sci: Nanosci. Nanotechnol.

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The present report aims at the application of piezoelectric nanogenerators for non-destructive material discrimination. The detailed characteristics of zinc oxide (ZnO) nanosheet-based piezoelectric nanogenerator and its applications in mechanical energy harvesting and sensing are considered as major objectives of the present study. The nanogenerator is fabricated with ZnO nanosheet film prepared by hydrothermal method with necessary electrodes. The nanogenerator response as a function of different load resistances, different finger-tapping frequencies, different finger-tapping pressures, bending and twisting movements are recorded. The maximum output power of 18 nW is observed at the load resistance of 200 KΩ with 60 mV output voltage. The output voltages of ∼150 mV, 350 mV, and 200 mV are observed for finger-tapping, bending, and twisting movements, respectively. The output voltages of ∼76 mV, 100 mV, and 145 mV are observed for low, medium, and high pressures applied by the finger. Nanogenerator is also tested for its stability of the output at different points of time after the device fabrication and found stable over for one year. Further, the nanogenerator is used as an impact sensor for non-destructive material discrimination application. The output voltages of 176 mV, 225 mV, 272 mV, and 300 mV were observed for acrylic, ceramic, marble, glass balls of uniform diameter but with a different mass. The fabricated nanogenerator can discriminate the equal size materials of different densities. Further, ZnO nanosheet-based nanogenerator has potential applications in mechanical sensors due to the high flexibility and mechanical reliability of the ZnO nanosheets.

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Material discrimination using bispectral signatures

Cited by 6 publications

References 33 publications

Nonlinearity detection in multiple degree-of-freedom systems using the auto-bispectral density

Nonlinearity detection in multiple degree-of-freedom systems using the auto-bispectral density

Discriminating different materials by means of vibrations

Characteristics of 2D ZnO-based piezoelectric nanogenerator and its application in non-destructive material discrimination

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