scholarly journals Method of reconstructing dynamic load characteristics for durability test of heavy semitrailer under different road conditions

2021 ◽  
Vol 23 (3) ◽  
pp. 548-558
Author(s):  
Arkadiusz Czarnuch ◽  
Marek Stembalski ◽  
Tomasz Szydłowski ◽  
Damain Batory
Keyword(s):  
Time Domain ◽  
Frequency Response Function ◽  
Durability Test ◽  
The Road ◽  
Road Load ◽  
Local Roads ◽  
Load Characteristics ◽  
Physical Variables ◽  
Data Signal ◽  
The Time Domain

The aim of the article is to present and validate a methodology for collecting road load data on a vehicle, driving on roads and analysis of a drive data signal under the wheel in the time domain, using FRF (Frequency Response Function) and the MTS 320 eight-poster inertia reacted road simulator. The elaborated drive data, was used to control the actuators forcing the movements of the wheels and the coupling part of the semi-trailer during durability tests. The road tests were carried out by registering physical variables in the time domain, by a set of sensors mounted on a vehicle. The data was collected from roads categorized as motorways, national and local roads. Differences between the variability of the parameters, collected on the roads and the variability of the drive data under the wheel, were determined for the particular types of roads, for loaded and unloaded vehicle. The obtained accuracy of reconstruction of the road load data conditions was as high as 97%. Therefore, the proposed method is suitable for reliable durability tests with use of the road simulator.

A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

2021 ◽  
Author(s):  
Yongzhi Qu ◽  
Gregory W. Vogl ◽  
Zechao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Time Domain ◽  
Frequency Response Function ◽  
Frequency Component ◽  
Operating Conditions ◽  
Input Output ◽  
The Fourier Transform ◽  
The Time Domain

Abstract The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.

scholarly journals The Researches on Damage Detection Method for Truss Structures

2018 ◽  
Vol 38 ◽  
pp. 03030
Author(s):  
Meng Hong Wang ◽  
Xiao Nan Cao
Keyword(s):  
Numerical Simulation ◽  
Frequency Response ◽  
Response Function ◽  
Time Domain ◽  
Experimental Analysis ◽  
Frequency Response Function ◽  
Principal Component ◽  
Truss Structures ◽  
Time Domain Signal ◽  
The Time Domain

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

Apodisation in the time domain

1992 ◽  
Vol 2 (4) ◽  
pp. 615-620
Author(s):  
G. W. Series
Keyword(s):  
Time Domain ◽  
The Time Domain

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

scholarly journals Broadband spectroscopy of astrophysical ice analogues

2019 ◽  
Vol 629 ◽  
pp. A112 ◽  
Author(s):  
B. M. Giuliano ◽  
A. A. Gavdush ◽  
B. Müller ◽  
K. I. Zaytsev ◽  
T. Grassi ◽  
...  
Keyword(s):  
Refractive Index ◽  
Optical Properties ◽  
Time Domain ◽  
Complex Refractive Index ◽  
Far Infrared ◽  
Infrared Region ◽  
Thz Radiation ◽  
The Real ◽  
Thz Range ◽  
The Time Domain

Context. Reliable, directly measured optical properties of astrophysical ice analogues in the infrared and terahertz (THz) range are missing from the literature. These parameters are of great importance to model the dust continuum radiative transfer in dense and cold regions, where thick ice mantles are present, and are necessary for the interpretation of future observations planned in the far-infrared region. Aims. Coherent THz radiation allows for direct measurement of the complex dielectric function (refractive index) of astrophysically relevant ice species in the THz range. Methods. We recorded the time-domain waveforms and the frequency-domain spectra of reference samples of CO ice, deposited at a temperature of 28.5 K and annealed to 33 K at different thicknesses. We developed a new algorithm to reconstruct the real and imaginary parts of the refractive index from the time-domain THz data. Results. The complex refractive index in the wavelength range 1 mm–150 μm (0.3–2.0 THz) was determined for the studied ice samples, and this index was compared with available data found in the literature. Conclusions. The developed algorithm of reconstructing the real and imaginary parts of the refractive index from the time-domain THz data enables us, for the first time, to determine the optical properties of astrophysical ice analogues without using the Kramers–Kronig relations. The obtained data provide a benchmark to interpret the observational data from current ground-based facilities as well as future space telescope missions, and we used these data to estimate the opacities of the dust grains in presence of CO ice mantles.

EPR light beams and non-Gaussian quantum distributions in the time domain

2009 ◽  
Vol 6 (7) ◽  
pp. 577-580
Author(s):  
N. H. Adamyan ◽  
H. H. Adamyan ◽  
G. Yu. Kryuchkyan
Keyword(s):  
Time Domain ◽  
Non Gaussian ◽  
The Time Domain ◽  
Light Beams ◽  
Quantum Distributions

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

scholarly journals Diagnostically Oriented Experiments and Modelling of Switched Reluctance Motor Dynamic Eccentricity

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3857
Author(s):  
Jakub Lorencki ◽  
Stanisław Radkowski ◽  
Szymon Gontarz
Keyword(s):  
Time Domain ◽  
Test Bench ◽  
Switched Reluctance Motor ◽  
Industrial Applications ◽  
Switched Reluctance ◽  
Static And Dynamic Analysis ◽  
Geometry Model ◽  
Reluctance Motor ◽  
Dynamic Eccentricity ◽  
The Time Domain

The article compares the results of experimental and modelling research of switched reluctance motor at two different operational states: one proper and one with mechanical fault, i.e., with dynamic eccentricity of the rotor. The experiments were carried out on a test bench and then the results were compared with mathematical modelling of quasi-static and dynamic analysis of 2D geometry model. Finally, it was examined how the operation with dynamic eccentricity fault of the motor affected its main physical parameter—the phase current. The analysis was presented in the frequency domain using the Fast Fourier Transform (FFT); however, individual current waveforms in the time domain are also shown for comparison. Applying results of the research could increase reliability of the maintenance of SRM and enhance its application in vehicles for special purposes as well as its military and industrial applications.

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