scholarly journals Wavelet-based audio features of DC motor sound

2021 ◽  
Vol 34 (1) ◽  
pp. 71-88
Author(s):  
Djordje Damnjanovic ◽  
Dejan Ciric ◽  
Zoran Peric
Keyword(s):  
Fourier Transform ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Dc Motor ◽  
Feature Representation ◽  
Production Environment ◽  
Audio Features ◽  
The Fourier Transform ◽  
Approximation Coefficients ◽  
Motor Production

The usage of wavelets is widespread in many fields nowadays, especially in signal processing. Their nature provides some advantages in comparison to the Fourier transform, and therefore many applications rely on wavelets rather than on other methods. The decomposition of wavelets into detail and approximation coefficients is one of the methods to extract representative audio features. They can be used in signal analysis and further classification. This paper investigates the usage of various wavelet families in the wavelet decomposition to extract audio features of direct current (DC) motor sounds recorded in the production environment. The purpose of feature representation and analysis is the detection of DC motor failures in motor production. The effects of applying different wavelet families and parameters in the decomposition process are studied using sounds of more than 60 motors. Time and frequency analysis is also done for the tested DC motor sounds.

Analyzing Variations in Roundness Profile Parameters During the Wavelet Decomposition Process Using the Matlab Environment

2011 ◽  
Vol 18 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Stanisław Adamczak ◽  
Włodzimierz Makieła
Keyword(s):  
Surface Texture ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Decomposition Process ◽  
Profile Measurement ◽  
Texture Measurement ◽  
Spearman’S Rank Correlation ◽  
The Fourier Transform

Analyzing Variations in Roundness Profile Parameters During the Wavelet Decomposition Process Using the Matlab Environment Signal analysis performed during surface texture measurement frequently involves applying the Fourier transform. The method is particularly useful for assessing roundness and cylindrical profiles. Since the wavelet transform is becoming a common tool for signal analysis in many metrological applications, it is vital to evaluate its suitability for surface texture profiles. The research presented in this paper focused on signal decomposition and reconstruction during roundness profile measurement and the effect of these processes on the changes in selected roundness profile parameters. The calculations were carried out on a sample of 100 roundness profiles for 12 different forms of mother wavelets using MATLAB. The use of Spearman's rank correlation coefficients allowed us to evaluate the relationship between the two chosen criteria for selecting the optimal mother wavelet.

SOME MATHEMATICAL TOOLS FOR THE ANALYSIS OF BIOLOGICAL SIGNALS

2007 ◽  
Vol 07 (02) ◽  
pp. 215-227 ◽  
Author(s):  
MICHELE NICHELATTI ◽  
PAOLO PETTAZZONI ◽  
GIOVANNI PALLOTTI
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Special Focus ◽  
Transient Phenomena ◽  
Time Frequency ◽  
Window Functions ◽  
Biological Signals ◽  
Recent Developments ◽  
The Fourier Transform

The paper presents an informal review of some techniques available for signal analysis. In the interpretation of biomedical signals, the individuation of hidden transient phenomena in the spectrum can have a crucial role for diagnostic purposes. Since most biological signals are nonstationary, the Fourier transform is not sufficient to detect possible transient phenomena in the spectrum; therefore, some improvements in the Fourier transform technique have been carried out by means of window functions in the transformation kernel. Some of the most important features of recent developments in signal analysis are discussed here, with special focus on the uncertainty principle governing any time–frequency analysis.

scholarly journals A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3041
Author(s):  
Eduardo Trutié-Carrero ◽  
Diego Seuret-Jimenez ◽  
José M. Nieto-Jalil
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Noise Ratio ◽  
The Fourier Transform ◽  
Non Stationary Signal

This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods.

scholarly journals Wavelet Transforms Applications and Interpretation Based on the Signal Genesis

2019 ◽  
Vol 9 (1S) ◽  
pp. 30-37
Keyword(s):  
Fourier Transform ◽  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Ct Scanner ◽  
Additional Signal ◽  
Spectral Components ◽  
Radar Echo ◽  
Frequency Components ◽  
The Fourier Transform ◽  
Insight Into

A signal from any measurement system provides insight into its genesis, thereby enabling an understanding of a certain activity or phenomenon. Seismic signals, radar echo signals, physiological signals, signals from specially fabricated instruments such as MRI, CT scanner all provide information by using an analysis that resolves the signal into its frequency components. While the Fourier transform and its fast – evaluating algorithm known as FFT are standard for such analysis, there are presently additional signal transforms in use, of which “ Wavelets” or Wavelet transform or wavelet decomposition are becoming very important. If the Fourier transform resolved the signal into its spectral components of Sine and Cosine waves, the Wavelets do the same in terms of non- sinusoidal oscillatory wave-shapes of burst – like appearance. This paper deals with the choice of wavelet transforms based on signal genesis and the interpretation required from the analysis of the signal, that one is expected to infer.

IIR implementation of wavelet decomposition for digital signal analysis

1992 ◽  
Vol 28 (5) ◽  
pp. 513 ◽  
Author(s):  
F. Argenti ◽  
G. Benelli ◽  
A. Sciorpes
Keyword(s):  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Digital Signal

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

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