An Eigenvalue Based Acoustic Impedance Measurement Technique

1991 ◽  
Vol 113 (2) ◽  
pp. 250-254 ◽  
Author(s):  
A. J. Hull ◽  
C. J. Radcliffe
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Response ◽  
Measurement Technique ◽  
Acoustic Impedance ◽  
Laboratory Tests ◽  
Impedance Measurement ◽  
Wave Pattern ◽  
Impedance Tube ◽  
One Dimensional

A method is developed for measuring acoustic impedance. The method employs a one-dimensional tube or duct with excitation at one end and an unknown acoustic impedance at the termination end. Microphones placed in the tube are then employed to measure the frequency response of the system from which acoustic impedance of the end is calculated. This method uses fixed instrumentation and takes advantage of modern Fast Fourier Transform analyzers. Conventional impedance tube methods have errors resulting from movement of microphones to locate the maxima and minima of the wave pattern in the impedance tube or require phase matched microphones with specific microphone spacing. This technique avoids these problems by calculating the acoustic impedance from measured duct eigenvalues. Laboratory tests of the method are presented to demonstrate its accuracy.

scholarly journals The effect of fuel mixture on engine vibrations

2019 ◽  
Vol 177 (2) ◽  
pp. 136-138
Author(s):  
Radosław WRÓBEL ◽  
Łukasz ŁOZA ◽  
Piotr HALLER ◽  
Radosław WŁOSTOWSKI
Keyword(s):  
Fourier Transform ◽  
Comparative Analysis ◽  
Data Acquisition ◽  
Fast Fourier Transform ◽  
Frequency Response ◽  
Fuel Mixture ◽  
Graphical Form

In the article, the authors analyze the effect of a fuel mixture (iso-octane, butanol and ethanol) on the generation of engine vibrations. The paper presents the results in the form of frequency response (using the Fast Fourier Transform – FFT) for three mixtures of different proportions. The measurements were made with the use of accelerometers and data acquisition cards, conditioning the received signal. The vibration component, in the form of acceleration, will be subjected to a FFT and presented in graphical form (periodogram). The authors put a special emphasis on a comparative analysis, indicating changes in harmonics, which may be a potential cause of engine degradation.

scholarly journals Multiple Memory Structure Bit Reversal Algorithm Based on Recursive Patterns of Bit Reversal Permutation

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
K. K. L. B. Adikaram ◽  
M. A. Hussein ◽  
M. Effenberger ◽  
T. Becker
Keyword(s):  
Fourier Transform ◽  
Fourier Analysis ◽  
Fast Fourier Transform ◽  
Processing Time ◽  
Memory Structure ◽  
One Dimensional ◽  
Reversal Process ◽  
Index Mapping ◽  
Increasing Demand ◽  
Memory Structures

With the increasing demand for online/inline data processing efficient Fourier analysis becomes more and more relevant. Due to the fact that the bit reversal process requires considerable processing time of the Fast Fourier Transform (FFT) algorithm, it is vital to optimize the bit reversal algorithm (BRA). This paper is to introduce an efficient BRA with multiple memory structures. In 2009, Elster showed the relation between the first and the second halves of the bit reversal permutation (BRP) and stated that it may cause serious impact on cache performance of the computer, if implemented. We found exceptions, especially when the said index mapping was implemented with multiple one-dimensional memory structures instead of multidimensional or one-dimensional memory structure. Also we found a new index mapping, even after the recursive splitting of BRP into equal sized slots. The four-array and the four-vector versions of BRA with new index mapping reported 34% and 16% improvement in performance in relation to similar versions of Linear BRA of Elster which uses single one-dimensional memory structure.

scholarly journals Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

2019 ◽  
Vol 9 (7) ◽  
pp. 1462 ◽  
Author(s):  
Wan-Ju Lin ◽  
Shih-Hsuan Lo ◽  
Hong-Tsu Young ◽  
Che-Lun Hung
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Fourier Transform ◽  
Deep Learning ◽  
Fast Fourier Transform ◽  
Short Term Memory ◽  
Intelligent System ◽  
Milling Process ◽  
Vibration Signals ◽  
One Dimensional

The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.

scholarly journals USING WAVELETS WITH A RECTANGULAR AMPLITUDE-FREQUENCY RESPONSE TO FILTER SIGNALS

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Aleksandr Shurbin
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Frequency Response ◽  
Frequency Domain ◽  
Digital Filters ◽  
Bandpass Filter ◽  
Amplitude Frequency Response

The wavelet transform is the transmission of a signal through a bandpass filter. The design of wavelets with a rectangular amplitude-frequency response makes it possible to obtain almost ideal digital filters. The wavelet transform is calculated in the frequency domain using the fast Fourier transform.

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