Analysis of nonlinear vibration-interaction using higher order spectra to diagnose aerospace system faults

Hassan, Mohammed; Coats, David; Gouda, Kareem; Shin, Yong-June; Bayoumi, Abdel

doi:10.1109/aero.2012.6187370

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Moreover, the power gains are overshadowed by the implications of large load supply voltage fluctuations induced at large spectral densities. When tuned to the low end of the transducer frequency range, which produced more power, the harvester produced less than half of a milliwatt of power at a spectral density 5 −3 g 2 /Hz, which is 10 to 100 time higher than ambient spectral densities seen in many industrial and vehicular applications [7,18,36].…”

Section: Random Excitationmentioning

confidence: 99%

Experimental Analysis of a Piezoelectric Energy Harvesting System for Harmonic, Random, and Sine on Random Vibration

Cryns

Hatchell

Santiago-Rojas

et al. 2013

Advances in Acoustics and Vibration

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Harvesting power with a piezoelectric vibration powered generator using a full-wave rectifier conditioning circuit is experimentally compared for varying sinusoidal, random, and sine on random (SOR) input vibration scenarios; the implications of source vibration characteristics on harvester design are discussed. The rise in popularity of harvesting energy from ambient vibrations has made compact, energy dense piezoelectric generators commercially available. Much of the available literature focuses on maximizing harvested power through nonlinear processing circuits that require accurate knowledge of generator internal mechanical and electrical characteristics and idealization of the input vibration source, which cannot be assumed in general application. Variations in source vibration and load resistance are explored for a commercially available piezoelectric generator. The results agree with numerical and theoretical predictions in the previous literature for optimal power harvesting in sinusoidal and flat broadband vibration scenarios. Going beyond idealized steady-state sinusoidal and flat random vibration input, experimental SOR testing allows for more accurate representation of real world ambient vibration. It is shown that characteristic interactions from more complex vibration sources significantly alter power generation and processing requirements by varying harvested power, shifting optimal conditioning impedance, inducing voltage fluctuations, and ultimately rendering idealized sinusoidal and random analyses incorrect.

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Section: Random Excitationmentioning

confidence: 99%

Experimental Analysis of a Piezoelectric Energy Harvesting System for Harmonic, Random, and Sine on Random Vibration

Cryns

Hatchell

Santiago-Rojas

et al. 2013

Advances in Acoustics and Vibration

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“…This problem had been gotten more vital due to users demanded from machinery manufacturers to design machines with lower angular velocity since they'd like to diminish the costs of repair and maintenances (Jennions, 2011). While reliability levels were enhanced, rotors encounter to unbalance problem, which requires unplanned maintenance (Hassan et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Unbalance Localization of a Multi-Disc Rotor by Hybridizing Wavelet Transformation and Neural Network

Safari¹,

Eydi²

2022

JIMA

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Rotating machinery is widely working within the industry, and fault diagnosis and prognosis of them may be a vital issue that can save money continually. Unbalance is a crucial fault in rotary systems, and it is focused by many researchers to develop methods to detect that for correcting before global failure happening within the machine. Hence, the establishment of a procedure that will estimate the unbalance location and its specifics are going to be valued and practical for correcting operations. The recent study exemplifies a model that can detect the unbalance parameters, for example, the location of unbalance mass and value of that based on the hybridizing Wavelet Transformation and artificial neural network (ANN) model. The inputs of the model are wavelet coefficients derived from the bearing acceleration signal. It includes two hidden layers constructed by six neurons within each layer. The parameters estimation accuracy was attained 95%, 97%, and 96% for the disc number, the eccentric radius, and unbalance mass values, correspondingly.

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“…However, this is possible only when diagnostic signals, storing as much information about the machine condition as possible, are analyzed. The most frequently used diagnostic signals encompass: phase current [6][7][8][9], mechanical vibration [6,[8][9][10][11][12][13][14][15][16], axial stray flux [6], acoustic signal [16,17], and temperature [18]. The applications of high-calculation power systems enables diagnosing processes in real time.…”

Section: Introductionmentioning

confidence: 99%

“…To detect unbalance, one can use: bispectrum [11], power spectrum [11], power spectral density (PSD) of synchronously sampled stator current [25], full spectrum [12], discrete wavelet transform [7,8], and the Hilbert transform [8,25]. The automation of the diagnostic process can also be obtained by the application of neural networks [9,16], fuzzy logic [12] or classifiers (e.g., nearest mean and support vector machine classifiers) [17].…”

Section: Introductionmentioning

confidence: 99%

The Application of the Bispectrum Analysis to Detect the Rotor Unbalance of the Induction Motor Supplied by the Mains and Frequency Converter

Ewert

2020

Energies

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This article presents the effectiveness of bispectrum analysis for the detection of the rotor unbalance of an induction motor supplied by the mains and a frequency converter. Two diagnostic signals were analyzed, as well as the stator current and mechanical vibrations of the tested motors. The experimental tests were realized for two low-power induction motors, with one and two pole pairs, respectively. The unbalance was modeled using a test mass mounted on a specially prepared disc and directly on the rotor and the influence of this unbalance location was tested and discussed. The results of the bispectrum analysis are compared with results of Fourier transform and the effectiveness of unbalance detection are discussed and compared. The influence of the registration time of the analyzed signal on the quality of fault symptom analyses using both transforms was also tested. It is shown that the bispectrum analysis provides an increased number of fault symptoms in comparison with the classical spectral analysis as well as it is not sensitive to a shorter registration time of the diagnostic signals.

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Analysis of nonlinear vibration-interaction using higher order spectra to diagnose aerospace system faults

Cited by 11 publications

References 11 publications

Experimental Analysis of a Piezoelectric Energy Harvesting System for Harmonic, Random, and Sine on Random Vibration

Experimental Analysis of a Piezoelectric Energy Harvesting System for Harmonic, Random, and Sine on Random Vibration

Unbalance Localization of a Multi-Disc Rotor by Hybridizing Wavelet Transformation and Neural Network

The Application of the Bispectrum Analysis to Detect the Rotor Unbalance of the Induction Motor Supplied by the Mains and Frequency Converter

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