Wavelet Analysis of the Pressure Fluctuations of Bottom Outlet of Kamal-Saleh Dam

2009 ◽  
Author(s):  
M. Liaghat ◽  
A. Abdollahi ◽  
F. Daneshmand ◽  
T. Liaghat
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Fourier Analysis ◽  
Spectral Characteristics ◽  
Pressure Fluctuations ◽  
Transient Phenomena ◽  
Service Gate ◽  
Stationary Signals ◽  
Bottom Outlet

Non-stationary signals are frequently encountered in a variety of engineering fields. The inability of conventional Fourier analysis to preserve the time dependence and describe the evolutionary spectral characteristics of non-stationary processes requires tools which allow time and frequency localization beyond customary Fourier analysis. The spectral analysis of non-stationary signals cannot describe the local transient features due to averaging over the duration of the signal [1]. The Fourier Transform (FT) and the short time Fourier transform (STFT) have been often used to measure transient phenomena. These techniques yield good information on the frequency content of the transient, but the time at which a particular disturbance in the signal occurred is lost [2, 3]. Wavelets are relatively new analysis tools that are widely being used in signal analysis. In wavelet analysis, the transients are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave band of frequency. Wavelets do a very good job in detecting the time of the signal, but they give the frequency information in terms of frequency band regions or scales [4]. The main objective of this paper is to use the wavelet transform for analysis of the pressure fluctuations occurred in the bottom-outlet of Kamal-Saleh Dam. The “Kamalsaleh Dam” is located on the “Tire River” in Iran, near the Arak city. The Bottom Outlet of the dam is equipped with service gate and emergency gate. A hydraulic model test is conducted to investigate the dynamic behavior of the service gate of the outlet. The results of the calculations based on the wavelet transform is then compared with those obtained using the traditional Fast Fourier Transform.

THE ADVANTAGE OFTHEWAVELET TRANSFORM IN PROCESSING OF SPEECH SIGNALS

2021 ◽  
Vol 4 (3) ◽  
pp. 37-41
Author(s):  
Sayora Ibragimova ◽  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Speech Processing ◽  
High Performance ◽  
Speech Sound ◽  
Digital Signal ◽  
Speech Signals ◽  
Multi Scale ◽  
Processing Of Speech Signals

This work deals with basic theory of wavelet transform and multi-scale analysis of speech signals, briefly reviewed the main differences between wavelet transform and Fourier transform in the analysis of speech signals. The possibilities to use the method of wavelet analysis to speech recognition systems and its main advantages. In most existing systems of recognition and analysis of speech sound considered as a stream of vectors whose elements are some frequency response. Therefore, the speech processing in real time using sequential algorithms requires computing resources with high performance. Examples of how this method can be used when processing speech signals and build standards for systems of recognition.Key words: digital signal processing, Fourier transform, wavelet analysis, speech signal, wavelet transform

An Analysis of Deviations of Cylindrical Surfaces with the Use of Wavelet Transform

2013 ◽  
Vol 20 (1) ◽  
pp. 139-150 ◽  
Author(s):  
Krzysztof Stępień ◽  
Włodzimierz Makieła
Keyword(s):  
Wavelet Transform ◽  
Fourier Analysis ◽  
Mechanical Engineering ◽  
Science And Technology ◽  
Measurement Data ◽  
Common Method ◽  
Good Tool ◽  
Signal Measurement ◽  
Stationary Signals ◽  
Analyze Data

Abstract Wavelet transform becomes a more and more common method of processing 3D signals. It is widely used to analyze data in various branches of science and technology (medicine, seismology, engineering, etc.). In the field of mechanical engineering wavelet transform is usually used to investigate surface micro- and nanotopography. Wavelet transform is commonly regarded as a very good tool to analyze non-stationary signals. However, to analyze periodical signals, most researchers prefer to use well-known methods such as Fourier analysis. In this paper authors make an attempt to prove that wavelet transform can be a useful method to analyze 3D signals that are approximately periodical. As an example of such signal, measurement data of cylindrical workpieces are investigated. The calculations were performed in the MATLAB environment using the Wavelet Toolbox.

Wavelet Analysis for Gas Turbine Fault Diagnostics

1997 ◽  
Vol 119 (4) ◽  
pp. 870-876 ◽  
Author(s):  
N. Aretakis ◽  
K. Mathioudakis
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Gas Turbines ◽  
Diagnostic Techniques ◽  
Diagnostic Information ◽  
Practical Application ◽  
Acoustic Measurements ◽  
Time Signals ◽  
Existing Data

The application of wavelet analysis to diagnosing faults in gas turbines is examined in the present paper. Applying the wavelet transform to time signals obtained from sensors placed on an engine gives information in correspondence to their Fourier transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure, and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

scholarly journals THE USE OF THE HILBERT HUANG TRANSFORMATION IN THE TESTING OF RAILWAY STRUCTURES

Akustika ◽  
2021 ◽  
pp. 29-35
Author(s):  
Jaroslav Smutný ◽  
Dušan Janoštík ◽  
Viktor Nohál
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Mathematical Analysis ◽  
Measurement Method ◽  
Calculation Procedure ◽  
Short Term ◽  
Transient Phenomena ◽  
Hilbert Huang Transform ◽  
Recommendations For Practice ◽  
Dynamic Phenomena

The aim of the paper is to introduce a less used method for the evaluation of non-stationary and especially transient phenomena in railway structures to the wider professional public. This method may find wide application in many technical and other fields. It is the so-called Hilbert-Huang transform. In this paper, its application in the study of dynamic phenomena occurring in a selected superstructure structure is shown. The calculation procedure of the presented transform differs from traditional tools, which include, for example, the short-term Fourier transform or the Wavelet transform. The paper includes a mathematical analysis and description of this transformation. Furthermore, the paper contains a description of the measurement method used, a discussion of the results obtained and recommendations for practice.

scholarly journals A Comparative Study of Reassigned Conventional Wavelet Transform for Machinery Faults Detection

2015 ◽  
Vol 773-774 ◽  
pp. 90-94 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
M. Salman Leong ◽  
Lim Meng Hee ◽  
Wai Keng Ngui
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Spectrum Analysis ◽  
Vibration Spectrum ◽  
Frequency Resolution ◽  
Transient Faults ◽  
Mother Wavelet ◽  
Time Frequency ◽  
Simulated Signal

Application of Fast Fourier Transform (FFT) in machinery faults detection is known to be only effective if fault is of repetitive in nature and considering severe. While minor and transient faults are usually remain undetected based on vibration spectrum analysis. Wavelet analysis is relatively new technique which is still suffered from inadequately in its time-frequency resolution. In this paper, ahmedrabak_time wavelet is proposed based on the wavelet reassignment technique for Morlet mother wavelet. The proposed wavelet analysis is compared to the conventional wavelet analysis for machinery faults detection based on simulated signal. The results showed that the proposed wavelet has a better resolution than conventional wavelet analysis which could clearly indicate the presence and the location of the fault.

scholarly journals Wavelet Analysis for Gas Turbine Fault Diagnostics

1996 ◽  
Author(s):  
N. Aretakis ◽  
K. Mathioudakis
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Gas Turbines ◽  
Diagnostic Techniques ◽  
Diagnostic Information ◽  
Practical Application ◽  
Acoustic Measurements ◽  
Time Signals ◽  
Existing Data

The application of wavelet analysis to diagnosing faults in Gas Turbines is examined in the present paper. Applying the Wavelet Transform to time signals obtained from sensors placed on an engine, gives information which is in correspondence to their Fourier Transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the Wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

Special affine wavelet transform and the corresponding Poisson summation formula

2021 ◽  
pp. 2050086
Author(s):  
Firdous A. Shah ◽  
Aajaz A. Teali ◽  
Azhar Y. Tantary
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Inversion Formula ◽  
Summation Formula ◽  
Poisson Summation Formula ◽  
Time Frequency ◽  
Fundamental Properties ◽  
Stationary Signals ◽  
Poisson Summation

In the article, “Windowed special affine Fourier transform” in J. Pseudo-Differ. Oper. Appl. (2020), we introduced the notion of windowed special affine Fourier transform (WSAFT) as a ramification of the special affine Fourier transform. Keeping in view the fact that the WSAFT is not befitting for in the context of non-stationary signals, we continue our endeavor and introduce the notion of the special affine wavelet transform (SAWT) by combining the merits of the special affine Fourier and wavelet transforms. Besides studying the fundamental properties of the SAWT including orthogonality relation, inversion formula and range theorem, we also demonstrate that the SAWT admits the constant [Formula: see text]-property in the time–frequency domain. Moreover, we formulate an analog of the well-known Poisson summation formula for the proposed SAWT.

scholarly journals On the use of wavelets to reveal oscillatory patterns in stellar flare emission

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170253 ◽  
Author(s):  
J. López-Santiago
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Light Curve ◽  
Background Noise ◽  
Light Curves ◽  
Theme Issue ◽  
Background Spectrum ◽  
Confidence Levels ◽  
Careful Treatment ◽  
Stationary Signals

Wavelet analysis is a powerful tool to investigate non-stationary signals such as amplitude modulated sinusoids or single events lasting for a small percentage of the observing time. Wavelet analysis can be used, for example, to reveal oscillations in the light curve of stars during coronal flares. A careful treatment of the background in the wavelet scalogram is necessary to determine robust confidence levels required to distinguish between patterns caused by actual oscillations and noise. This work describes the method using synthetic light curves and investigates the effect of background noise when determining confidence levels in the scalogram. The result of this analysis shows that the wavelet transform is able to reveal oscillatory patterns even when frequency-dependent noise is dominant. However, their significance in the wavelet scalogram may be reduced, depending on the assumed background spectrum. To show the power of wavelet analysis, the light curve of a well-known flaring star is analysed. It shows two oscillations overlapped. The lower-frequency oscillation is not mentioned in previous works in the literature. This result demonstrates the need for correctly characterizing the background noise of the signal. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

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