A novel approach for natural frequency hallow shift calculation using non contact impulse method

Application of unified constitutive model of 34CrNiMo6 alloy steel and microstructure simulation for flexible skew rolling hollow shafts

The paper presents the numerical investigation results of the gyroscopic moments’ influence on the dynamic behavior of a transmission shaft, specifically on the change in its natural oscillation frequencies with varying rotational speeds and direction. Systems that include shafts are often operated at different rotational speeds. If a shaft is represented as an elastic rod, it can perform natural and forced oscillations. The natural frequencies of the shaft depend on its rotational speed. This behavior is caused by precession motion. Therefore, the oscillatory motion of the shaft is considered in space, accounting for gyroscopic and other inertial forces. As a result, the dependences of the natural oscillation frequencies in the inertial and rotating coordinate systems, plotted using developed software for studying the dynamics of elastic shafts, are shown. The numerical values of the natural oscillation frequencies for an unmoved shaft and its critical rotational speeds for direct and reverse precession are also presented. An analysis of the results and conclusions regarding the possibility of operating the shaft in certain frequency ranges have been made. It is noted that the natural frequencies of elastic shafts during rotation are dependent on the influence of gyroscopic moments. An important parameter is the natural oscillation frequency in the rotating coordinate system, as it is directly related to the elastic body. As the diagrams show, the change in the natural frequency in the rotating and inertial coordinate systems occurs with different intensity when the rotational frequency changes: slowly in the inertial system and rapidly in the rotational system. In addition, when the rotational speed increases in the range up to critical values, the natural oscillation frequency in the rotating coordinate system decreases. With a further increase in rotational speed beyond the critical value, the natural oscillation frequency begins to grow, and the precession movement occurs in the reverse direction.

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher’s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher’s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

Monitoring of civil structures is crucial for their proper functioning. Any crack in a structure changes its static and dynamic behaviours. To detect the damage/crack at the initiating time itself is challenging task in modern time. This paper describes an experimental study to extract the dynamic characteristics of a frame structure using piezo‐electric ceramic (PZT) transducers. Tests are conducted on steel frame to extract the natural frequencies and the experimental mode shapes. Free vibration response is first acquired in the time domain and then transformed into frequency domain using Fast Fourier Transforms (FFT) analyser. Only single PZT patch is sufficient to extract the first nine modes shape of the steel frame .By using numerical model, mode shapes are extracted corresponding to each identified natural frequency. After determining natural frequencies and experimental mode shape, damages can be located by method of Naidu and Soh (2004). This approach can be used for damage/crack detection at ver...

The time-resolved reflectance of photons from a homogeneous tissue was modeled using a Monte Carlo simulation. The data was then converted by fast Fourier transform (FFT) into the frequency domain. In the frequency domain, the phase, (Phi) , and modulation, M, of collected light from a frequency-modulated light source was determined. A comparison of Monte Carlo and diffusion theory was made for various separation distances between the source and collector on the tissue surface. The results showed that Monte Carlo and diffusion theory agreed in the time domain only for times larger than 500 ps after injection of an impulse of photons. In the frequency domain, Monte Carlo and diffusion theory agreed only if the probe separation, r, was at least 2 cm apart for (mu) s' equals (mu) s(1 - g) equals 5 cm-1, or in dimension less units r(mu) s' > 10. The effect of buried absorbed is also tested in the time and frequency domains. A semi-infinite volume of absorber is placed at 0, 3 mm, 6 mm, or (infinity) from the surface of a nonabsorbing tissue. The presence of a deep absorber on the time and frequency domain data show that attenuation of longer pathlength photons causes the phase of collected photons to reduce and the modulation of collected photons to increase. Both effects are indicative of the net shorter pathlength of the ensemble of collected photons.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Dynamics of freely falling perforated disks

Vertical axial-flow pumps with bidirectional passages are widely used in applications requiring flow reversal. However, their unique inlet geometry often leads to asymmetric impeller inflow conditions. This study investigates the internal flow behavior and pressure pulsation characteristics of a vertical bidirectional axial-flow pump under design, critical stall, and deep stall conditions using unsteady Reynolds-averaged Navier–Stokes simulations combined with Fast Fourier Transform and wavelet analysis. Results show that the pump reaches peak efficiency at the design point, with critical and deep stall occurring at 0.6 Qdes and 0.5 Qdes, respectively. The head at the deep stall condition shows a further drop of 7.51% compared to the critical stall condition. This progressive performance degradation is attributed to vortex-induced blockage: it initiates with the intensification of the tip leakage vortex and evolves into large-scale separation vortices covering the suction surface under deep stall—a mechanism distinctly influenced by the bidirectional inlet’s stagnant water zone. Inlet asymmetry, reflected by a normalized velocity coefficient (Vn) below 0.6 in the stagnant water zone under design flow, is partially mitigated during stall due to flow confinement. Pressure pulsations at the blade leading edge are dominated by the blade passing frequency (BPF), with amplitudes under critical stall about 3.2 times those at design conditions. At the impeller outlet, critical stall produces a mixed dominant frequency (shaft frequency and BPF), whereas deep stall yields the highest pulsation amplitude (BPF ≈ 4.8 × the design value) resulting from extreme passage blockage. These findings clarify how bidirectional-inlet-induced vortices modulate stall progression and provide theoretical guidance for enhancing the operational stability of such pumps under off-design conditions.

Tradicionalmente, la detección de faltas en máquinas eléctricas se basa en el uso de la Transformada Rápida de Fourier ya que la mayoría de las faltas pueden ser diagnosticadas con ella con seguridad si las máquinas operan en condiciones de régimen estacionario durante un intervalo de tiempo razonable.
\nSin embargo, para aplicaciones en las que las máquinas operan en condiciones de carga y velocidad fluctuantes (condiciones no estacionarias) como por ejemplo los aerogeneradores, el uso de la Transformada Rápida de Fourier debe ser reemplazado por otras técnicas.
\nLa presente tesis desarrolla una nueva metodología para el diagnóstico de máquinas de inducción de rotor de jaula y rotor bobinado operando en condiciones no estacionarias, basada en el análisis de las componentes de falta de las corrientes en el plano deslizamiento frecuencia. La técnica es aplicada al diagnóstico de asimetrías estatóricas, rotóricas y también para la falta de excentricidad mixta.
\nEl diagnóstico de las máquinas eléctricas en el dominio deslizamiento-frecuencia confiere un carácter universal a la metodología ya que puede diagnosticar máquinas eléctricas independientemente de sus características, del modo en el que la velocidad de la máquina varía y de su modo de funcionamiento (motor o generador).
\nEl desarrollo de la metodología conlleva las siguientes etapas:
\n(i) Caracterización de las evoluciones de las componentes de falta de asimetría estatórica, rotórica y excentricidad mixta para las máquinas de inducción de rotores de jaula y bobinados en función de la velocidad (deslizamiento) y la frecuencia de alimentación de la red a la que está conectada la máquina.
\n(ii) Debido a la importancia del procesado de la señal, se realiza una introducción a los conceptos básicos del procesado de señal antes de centrarse en las técnicas actuales de procesado de señal para el diagnóstico de máquinas eléctricas.
\n(iii) La extracción de las componentes de falta se lleva a cabo a través de tres técnicas de filtrado diferentes: filtros basados en la Transformada Discreta Wavelet, en la Transformada Wavelet Packet y con una nueva técnica de filtrado propuesta en esta tesis, el Filtrado Espectral. Las dos primeras técnicas de filtrado extraen las componentes de falta en el dominio del tiempo mientras que la nueva técnica de filtrado realiza la extracción en el dominio de la frecuencia.
\n(iv) La extracción de las componentes de falta, en algunos casos, conlleva el desplazamiento de la frecuencia de las componentes de falta. El desplazamiento de la frecuencia se realiza a través de dos técnicas: el Teorema del Desplazamiento de la Frecuencia y la Transformada Hilbert.
\n(v) A diferencia de otras técnicas ya desarrolladas, la metodología propuesta no se basa exclusivamente en el cálculo de la energía de la componente de falta sino que también estudia la evolución de la frecuencia instantánea de ellas, calculándola a través de dos técnicas diferentes (la Transformada Hilbert y el operador Teager-Kaiser), frente al deslizamiento. La representación de la frecuencia instantánea frente al deslizamiento elimina la posibilidad de diagnósticos falsos positivos mejorando la precisión y la calidad del diagnóstico. Además, la representación de la frecuencia instantánea frente al deslizamiento permite realizar diagnósticos cualitativos que son rápidos y requieren bajos requisitos computacionales.
\n(vi) Finalmente, debido a la importancia de la automatización de los procesos industriales y para evitar la posible divergencia presente en el diagnóstico cualitativo, tres parámetros objetivos de diagnóstico son desarrollados: el parámetro de la energía, el coeficiente de similitud y los parámetros de regresión. El parámetro de la energía cuantifica la severidad de la falta según su valor y es calculado en el dominio del tiempo y en el dominio de la frecuencia (consecuencia de la extracción de las componentes de falta en el dominio de la frecuencia). El coeficiente de similitud y los parámetros de regresión son parámetros objetivos que permiten descartar diagnósticos falsos positivos aumentando la robustez de la metodología propuesta.
\nLa metodología de diagnóstico propuesta se valida experimentalmente para las faltas de asimetría estatórica y rotórica y para el fallo de excentricidad mixta en máquinas de inducción de rotor de jaula y rotor bobinado alimentadas desde la red eléctrica y desde convertidores de frecuencia en condiciones no estacionarias estocásticas.

The flickers caused by wind farm should be assessed accurately because they become a limiting factor for integrating wind turbines into a grid. The traditional flickermeter uses the fast Fourier transform (FFT), weighting in the frequency domain, and the inverse FFT process to weight the flickers to simulate the eye‐brain sensitivity to the lamp illumination behavior. However, because of the lower frequency resolution, spectral leakage will occur when the flickers transform into the frequency domain via FFT, then it may cause runaways in both ends of the weighted flickers and the runaways are more serious in the low‐frequency band, finally the assessment accuracy cannot meet the design criteria required by IEC Standard 61000‐4‐15 because the flicker frequencies of wind farm are in the low‐frequency band. An improved digital flickermeter implementation in the hybrid time and frequency domain is proposed in this article. Simulations and actual measurements show that the mirror image extension can improve the runaway through changing the phase angle of the flicker equivalently to make the results more accurate in the low‐frequency band when the frequency resolution decreases. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Applications like fuel cells, batteries or food analysis are typical fields of interest for electrical impedance spectroscopy (EIS).Another field with promising results are EIS measurements in semiconductor (sc) gas sensor applications [1].Due to their high sensitivity and low cost, sc gas sensors are ideally suited for different applications, especially in the field of safety and security.The major drawbacks of sc gas sensors, i.e. poor selectivity and sensor baseline drift, can be overcome by using temperature cycled operation (TCO) with DC measurement [2] or EIS, both in combination with intelligent signal processing.Currently, EIS for sc gas sensors is only possible in laboratory environments using commercial impedance analyzers, which are characterized by high cost, poor mobility and long acquisition time.However, for efficient development of application specific sc gas sensor systems and/or a self-monitoring strategy [3] extensive field tests are necessary.Therefore, a new hardware concept for EIS of sc gas sensors based on FPGA-technology was developed.This concept also allows a combination of EIS with TCO for the first time.Additionally, state-of-the-art feature extraction methods are applied to sc gas sensor data and new features were tested [4]. MotivationDue to their high sensitivity and low cost, semiconductor gas sensors are ideally suited for safety and security applications like fire and leakage detection [5,6].The major drawbacks of this type of sensor are the poor selectivity, the sensor baseline drift and sensor poisoning.Especially for safety and security applications these drawbacks must be overcome to allow a proper function of the system.Temperature cycled operation (TCO) and electrical impedance spectroscopy (EIS) -both in combination with intelligent signal processing -are the main approaches for improving both selectivity and stability.In TCO mode the sensor temperature is changed periodically [2] and the DC resistance of the sensor is read-out continually during this cycle.For EIS the sensor temperature is usually kept constant and the sensor impedance is measured over a wide frequency range, e.g. during a frequency sweep [1,7,8].To detect a sensor malfunction due to sensor poisoning a self-monitoring strategy can be realized by combining TCO und EIS (fig. 1) [3].This strategy is based on redundant information from both modes which are evaluated separately and comparing both results.Additional information can be used for selectivity enhancement.

Heart period variability has been considered for clinical assessment of autonomic function, determining the presence of haemorrhage or disease states, and for predicting mortality from traumatic injury. However, for heart period variability to be clinically useful, a number of important methodological issues should be addressed, including the minimum number of R-R intervals (RRI) required for accurate derivation, and the reproducibility of these metrics. ECGs were recorded for > or =10 min in 18 resting, supine subjects (12 M/6 F; 19-55 years). Heart period variability analyses included 21 time, frequency and complexity domain metrics. For assessment of minimum RRIs required, measurements were made from ECG recordings of 5 min down to 30 s for time and frequency domain metrics, and from 800 RRIs down to 100 RRIs for complexity metrics, by methodical truncation of the data set. Inter-subject variability was assessed by calculating the range and co-efficient of variation (%CV) across all subjects. Two independent 30 s or 100 RRI ECG segments were used to assess intra-subject variability via calculation of %CV in each subject. Six time and frequency domain metrics were robust down to 30 s of data, while five complexity metrics were robust down to 100 RRIs. All time and frequency domain metrics (except for RRI) exhibited high inter-subject variability (CVs > or = 30.0%), while five of eleven complexity metrics displayed low inter-subject variability (CVs < or = 8.5%). In the assessment of intra-subject variability in metrics valid with 30 s or 100 RRIs of ECG, only one time domain and four complexity metrics had CVs < 10%. Metrics that are highly reproducible and require few RRIs are advantageous for patient monitoring as less time is required to assess physiological status and initiate early interventions. Based on our analyses from healthy, resting humans, we have identified a select cohort of heart period variability metrics that performed well in regards to these two criteria.

The calculation of natural frequencies is very significant at the design phase of marine propulsion shafts. And added mass of propellers has high effect on natural frequencies. At present, the effect of wake shedding from the propeller’s trailing edge on added mass is unclear. Then their influence on propulsion shafts natural frequencies is also vague. So in this study, the influence of wake shedding on propellers added mass and propulsion shafts natural frequencies is evaluated. Our results show that first, for the first lateral natural frequency, the max relative error reaches up to 6.52 % if the wake shedding is ignored. So for the lateral vibration, the wake should be considered. Second for the first torsion natural frequency, the max relative error amounts to 5.10 % when the skew angle is 108 degrees. So for the torsion vibration, the wake should also be considered when the skew angle is large. Third, for the axial vibration, the influence of wake is little and may be ignored.

초록 In this study, the scattered far-field due to a cavity embedded in infinite media subjected to the incident SH-wave was calculated by the boundary element method. The effects of cavity shape and di stance between internal cavity and internal point in infinite media were considered. The scattered far-field of the frequency domain was transformed into the signal of the time domain by using the Inverse Fast Fourier Transform(IFFT). It was found that the amplitude of scattered signal in time domain decreased with the increase of the distance between the detecting points of ultrasonic scattered field and the center of internal cavity in media. In addition, the time delay was clearly found in time domain waveform as the distance between the detecting points of ultrasonic scattered field and the center of internal cavity was gradually increased

As carbon fiber reinforced polymer composites (CFRPs) are multifunctional materials in which the damage is coupled with the change in material electrical resistance, the use of electrical conductivity monitoring can provide real-time information concerning the damage state through examining the change in resistance/electric potential. It has been shown that, in some recent work, resistance measurement allows the monitoring of the in-situ evolution of various internal damage nucleation and growth phenomena such as fiber fractures, interply matrix cracks and interply delamination. However, one of the common difficulties in measuring the small changes of electrical signals due to failures in the CFRPs is the contamination of extraneous noises during the detection process. In this paper, signal analysis procedures are applied to minimize or eliminate the contaminated noise during the damage detection process, as to maximize the accuracy of the damage detection process. In addition to the traditional time signal averaging technique, the Fast Fourier Transform (FFT) is applied to filter at the narrow band near the input frequency as to eliminate the unwanted noises. However, based on laboratory experiments, the authors have found that the frequency filtering techniques will retain significant inaccuracy when extraneous noises are in the neighborhood of the input frequency, and, in some cases, accurate measurement of the electrical signals can be susceptable when noise components are significantly higher than the original input signal. To overcome this drawback, the use of a joint time-frequency technique with wavelet transforms has been introduced. As a joint time-frequency signal analysis tool, the wavelet transforms offer simultaneous interpretation of the signal in both time and frequency domains which results in signal filtering effects in both time and frequency domains. Experimental results by the authors have shown significant improvement in accuracy when using the wavelets than those of the FFT and the time averaging techniques. This paper demonstrates the wavelet transform applications in electrical signal noise reduction of CFRPs as well as the use of both continuous wavelet (CWT) and discrete wavelet (DWT) transforms. It has been found that when noise is small, the DWT is more appropriate as the original signal can be reconstructed to offer better comparative information. The use of CWT will provide better results when random noises are significantly higher. Although the use of CWT does not have the ability revert back to the original signal, the comparison of the CWT coefficients can offer more valuable information concerning the damage occurrences while the repeated applications of DWT will result in accumulated errors such that the original shapes of the wavelet cannot be correctly reconstructed.

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.