Optimization of the Robots Fourier Spectrum by Using the Assisted Research, Neural Network, Smart Damper and LabVIEW Instrumentation

2012 ◽  
Vol 245 ◽  
pp. 24-32 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Aurel Oprean
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Spectral Density ◽  
Vibration Analysis ◽  
Manufacturing Systems ◽  
Fourier Spectrum ◽  
Industrial Robots ◽  
Vibration Behavior ◽  
Power Spectral ◽  
The World

The most important things in the dynamic research of industrial robots are the vibration behavior, the transfer function and the vibration power spectral density between some of the robot joints and components. In the world this research is made without the assisted research. In each of the study cases in this paper was used the proper virtual Fourier analyzer and was presented one new method of the assisted vibration analysis. With this research it is possible the optimal choosing the base modulus type to avoid the frequencies from the robot spectrum. In the manufacturing systems, the most important facts are the vibration behavior of the robot, the compatibility with some other components of the system. All the VI where achieved in the LabVIEW soft 8.2 version, from National Instruments, USA. This method and the created virtual LabVIEW instrumentation are generally and they are possible to apply in many other dynamic behavior research.

Schwingungsanalyse an Industrierobotern*/Vibration analysis of industrial robots

2019 ◽  
Vol 109 (09) ◽  
pp. 656-661
Author(s):  
A. Karim ◽  
C. Michalkowski ◽  
A. Lechler ◽  
A. Verl
Keyword(s):  
Modal Analysis ◽  
Active Control ◽  
Normal Mode ◽  
Vibration Analysis ◽  
Industrial Robots ◽  
Vibration Exciter ◽  
Dynamic Vibration ◽  
Vibration Behavior ◽  
Working Space ◽  
Electromagnetic Vibration

Dieser Beitrag untersucht experimentell das dynamische Schwingverhalten eines „KR-500–3 MT“ von Kuka mittels eines elektromagnetischen Schwingerregers (Shaker) an insgesamt 28 Messposen im Arbeitsraum. Diese Untersuchungsmethode ist neuartig, da die Ergebnisse mit einer Modalanalyse mit Impulshammeranregung verglichen werden. Ab der vierten Eigenmode entstehen Unterschiede aufgrund der Anregungsform. Zudem wird an jeder Pose eine Messung mit angezogener Motorbremse und eine mit aktiver Regelung durchgeführt und miteinander verglichen.   This paper explores experimentally the dynamic vibration behavior of a Kuka KR-500 MT, using an electromagnetic vibration exciter (shaker) on a total of 28 measuring poses in the working space. As such studies are not known, the results are compared to a modal analysis with impulse hammer excitation. Starting from the fourth normal mode, differences arise due to the form of excitation. Both measurements are performed and compared with each other on each pose with brakes applied as well as with active control.

Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals

1997 ◽  
Vol 122 (1) ◽  
pp. 12-19 ◽  
Author(s):  
S. V. Kamarthi ◽  
S. R. T. Kumara ◽  
P. H. Cohen
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fourier Transform ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Flank Wear ◽  
Wavelet Representation ◽  
Power Spectral ◽  
Frequency Components ◽  
Ae Signals

This paper investigates a flank wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density of AE signals in turning is sensitive to gradually increasing flank wear. In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. To overcome some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated. This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals for flank wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing flank wear than the Fourier representation. [S1087-1357(00)71401-8]

scholarly journals Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

2020 ◽  
Vol 10 (21) ◽  
pp. 7639
Author(s):  
Md Junayed Hasan ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
Hyun-Kyun Choi ◽  
Dae-Seung Yoo ◽  
...  
Keyword(s):  
Neural Network ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Sleep Stage ◽  
Human Subjects ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Healthy Human ◽  
Power Spectral ◽  
Eeg Data

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

Rotational Turbulent Wind Field Simulation of Wind Turbine

2013 ◽  
Vol 291-294 ◽  
pp. 472-476 ◽  
Author(s):  
Wei He ◽  
De Tian ◽  
Qi Li ◽  
Ning Bo Wang
Keyword(s):  
Spectral Density ◽  
Wind Turbine ◽  
Power Spectral Density ◽  
Wind Velocity ◽  
Fourier Spectrum ◽  
Phase Lag ◽  
Turbulent Wind ◽  
Wind Field Simulation ◽  
Rotational Effect ◽  
Power Spectral

In order to accurately obtain the influence of rotational effect on fluctuating component of turbulent wind acted on wind turbine, rotational Fourier spectrum with considering rotational effect of rotor was deduced. Physical nature of the rotational Fourier spectrum embodied by coherence function and phase lag was indicated. Auto power spectral density and cross power spectral density of rotational Fourier spectrum with introducing phase lag were proposed. The spectrum matrix constructed by the module of rotational Fourier spectrum was decomposed with Cholesky's method, according to the spectrum representation method with introducing phase lag, the random turbulent wind speed field was generated by superposing a set of cosine functions. Finally, an example involving simulation of the longitudinal turbulent wind velocity time series of a 1.5 MW three-bladed pitch regulated wind turbine was investigated. The target spectrum and simulated spectrum were compared. The result shows that the proposed algorithm is more accurate to simulate the fluctuating wind velocity of rotational blade.

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