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1 – 10 of 632Mohamed A. El‐Gebeily, Shafiqur Rehman, Luai M. Al‐Hadhrami and Jaafar AlMutawa
The present study utilizes daily mean time series of meteorological parameters (air temperature, relative humidity, barometric pressure and wind speed) and daily totals of…
Abstract
The present study utilizes daily mean time series of meteorological parameters (air temperature, relative humidity, barometric pressure and wind speed) and daily totals of rainfall data to understand the changes in these parameters during 17 years period i.e. 1990 to 2006. The analysis of the above data is made using continuous and discrete wavelet transforms because it provides a time‐frequency representation of an analyzed signal in the time domain. Moreover, in the recent years, wavelet methods have become useful and powerful tools for analysis of the variations, periodicities, trends in time series in general and meteorological parameters in particular. In present study, both continues and discrete wavelet transforms were used and found to be capable of showing the increasing or decreasing trends of the meterorological parameters with. The seasonal variability was also very well represented by the wavelet analysis used in this study. High levels of compressions were obtained retaining the originality of the signals.
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The purpose of this paper is to provide a fault diagnosis method for rolling bearings. Rolling bearings are widely used in industrial appliances, and their fault diagnosis is of…
Abstract
Purpose
The purpose of this paper is to provide a fault diagnosis method for rolling bearings. Rolling bearings are widely used in industrial appliances, and their fault diagnosis is of great importance and has drawn more and more attention. Based on the common failure mechanism of failure modes of rolling bearings, this paper proposes a novel compound data classification method based on the discrete wavelet transform and the support vector machine (SVM) and applies it in the fault diagnosis of rolling bearings.
Design/methodology/approach
Vibration signal contains large quantity of information of bearing status and this paper uses various types of wavelet base functions to perform discrete wavelet transform of vibration and denoise. Feature vectors are constructed based on several time-domain indices of the denoised signal. SVM is then used to perform classification and fault diagnosis. Then the optimal wavelet base function is determined based on the diagnosis accuracy.
Findings
Experiments of fault diagnosis of rolling bearings are carried out and wavelet functions in several wavelet families were tested. The results show that the SVM classifier with the db4 wavelet base function in the db wavelet family has the best fault diagnosis accuracy.
Originality/value
This method provides a practical candidate for the fault diagnosis of rolling bearings in the industrial applications.
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Parth Sarathi Panigrahy and Paramita Chattopadhyay
The purpose of this paper is to inspect strategic placing of different signal processing techniques like wavelet transform (WT), discrete Hilbert transform (DHT) and fast Fourier…
Abstract
Purpose
The purpose of this paper is to inspect strategic placing of different signal processing techniques like wavelet transform (WT), discrete Hilbert transform (DHT) and fast Fourier transform (FFT) to acquire the qualitative detection of rotor fault in a variable frequency drive-fed induction motor under challenging low slip conditions.
Design/methodology/approach
The algorithm is developed using Q2.14 bit format of Xilinx System Generator (XSG)-DSP design tool in MATLAB. The developed algorithm in XSG-MATLAB can be implemented easily in field programmable gate array, as a provision to generate the necessary VHDL code is available by its graphical user interface.
Findings
The applicability of WT is ensured by the effective procedure of base wavelet selection, which is the novelty of the work. It is found that low-order Daubechies (db) wavelets show decent shape matching with current envelope rather than raw current signal. This fact allows to use db1-based discrete wavelet transform-inverse discrete wavelet transform, where economic and multiplier-less design is possible. Prominent identity of 2sfs component is found even at low FFT points due to the application of suitable base wavelet.
Originality/value
The proposed method is found to be effective and hardware-friendly, which can be used to design a low-cost diagnostic instrument for industrial applications.
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Jianhua Liu, Peng Geng and Hongtao Ma
This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision…
Abstract
Purpose
This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision map is very important the fusion results. In the processing of distinguishing the well-focus part with blur part in an image, the edge between the parts is more difficult to be processed. Coefficient significance is very effective in generating the better decision map to fuse the multifocus images.
Design/methodology/approach
The energy of Laplacian is used in the approximation coefficients of redundant discrete wavelet transform. On the other side, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient.
Findings
Due to the shift-variance of the redundant discrete wavelet and the effectiveness of fusion rule, the presented fusion method is superior to the region energy in harmonic cosine wavelet domain, pixel significance with the cross bilateral filter and multiscale geometry analysis method of Ripplet transform.
Originality/value
In redundant discrete wavelet domain, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient of source images.
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The purpose of this paper is to examine contagion among the major world markets during the last 25 years and propose a new way to analyze contagion with wavelet methods.
Abstract
Purpose
The purpose of this paper is to examine contagion among the major world markets during the last 25 years and propose a new way to analyze contagion with wavelet methods.
Design/methodology/approach
The analysis uses a novel way to study contagion using wavelet methods. The comparison is made between co‐movements at different time scales. Co‐movement methods of the discrete wavelet transform and the continuous wavelet transform are applied.
Findings
Clear signs of contagion among the major markets are found. Short time scale co‐movements increase during the major crisis while long time scale co‐movements remain approximately at the same level. In addition, gradually increasing interdependence between markets is found.
Research limitations/implications
Because of the chosen method, the approach is limited to large data sets.
Practical implications
The research has practical implications to portfolio managers etc. who wish to have better view of the dynamics of the international equity markets.
Originality/value
The research uses novel wavelet methods to analyze world equity markets. These methods allow the markets to be analyzed in the whole state space.
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Shekhar Mishra and Sathya Swaroop Debasish
This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.
Abstract
Purpose
This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.
Design/methodology/approach
The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.
Findings
The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.
Originality/value
To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.
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Vahid Behjat and Abolfazl Vahedi
Interturn winding faults, one of the most important causes of power transformers failures, cannot be detected by existing detection methods until they develop into high‐level…
Abstract
Purpose
Interturn winding faults, one of the most important causes of power transformers failures, cannot be detected by existing detection methods until they develop into high‐level faults with more severe damage to the transformer. The purpose of this paper is to describe development of a new discrete wavelet transform (DWT) based approach for detection of winding interturn faults.
Design/methodology/approach
The following approach was accomplished for development of the proposed fault detection method in this study. The DWT was first applied to decompose the terminal current signals of a transformer, which in turn were obtained from simulations using a finite elements method model of the transformer, into a series of wavelet components. Based on the characteristic features associated with interturn faults extracted from the decomposed waveforms of the terminal currents, a detection scheme was developed. An experimental setup was used to validate the proposed detection method.
Findings
The results of this study demonstrate the efficacy of DWT applied on terminal currents of the transformer to identify interturn faults on the windings well before such faults lead to a catastrophic failure. It is believed that, based on the present findings, there definitely exists scope for improving interturn fault diagnosis with wavelet transform.
Research limitations/implications
Performing more detailed studies to find all relevant characteristics of the wavelet transform in this application, identifying the location of the faulted turns along winding, applying the method for indicating early stages of turn insulation deterioration and evaluating other type of wavelets for this application would be some future directions of this research.
Practical implications
With the proposed method, it is becoming possible to detect early signs of the fault occurrence, so that the necessary corrective actions can be taken to prevent long‐lasting outages and reduce down times of the faulty power transformer. The method will be particularly useful as a complement for the classical protection devices of the power transformers.
Originality/value
Some recent studies have been carried out regarding the application of DWT for discrimination between an internal fault and other disturbances such as magnetizing inrush and external faults. This paper extends those studies for the detection of interturn faults using more quantitative and qualitative characteristics features.
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Ramazan Yildirim and Mansur Masih
The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe…
Abstract
The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).
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P.I.J. Keeton and F.S. Schlindwein
Provides an introduction into wavelets and illustrates their application with two examples. The wavelet transform provides the analyst with a scaleable time‐frequency…
Abstract
Provides an introduction into wavelets and illustrates their application with two examples. The wavelet transform provides the analyst with a scaleable time‐frequency representation of the signal, which may uncover details not evidenced by conventional signal processing techniques. The signals used in this paper are Doppler ultrasound recordings of blood flow velocity taken from the internal carotid artery and the femoral artery. Shows how wavelets can be used as an alternative signal processing tool to the short time Fourier transform for the extraction of the time‐frequency distribution of Doppler ultrasound signals. Implements wavelet‐based adaptive filtering for the extraction of maximum blood velocity envelopes in the post processing of Doppler signals.
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Saeed Reza Allahkaram and Mehdi Khodayari
The aim of this paper is to show that the use of energy distribution plot (EDP), usually employed by researchers to characterize the behavior of electrochemical signals in the…
Abstract
Purpose
The aim of this paper is to show that the use of energy distribution plot (EDP), usually employed by researchers to characterize the behavior of electrochemical signals in the framework of wavelet transform, could provide better understanding of the electrochemical behavior of a corroding surface if used along with the plot that is obtained from the standard deviation (SD) of partial signals (SDPS). A partial signal (PS) is obtained by limiting the inverse discrete wavelet transform to one crystal, and hence an SDPS is obtained by computing the SD of the corresponding PS.
Design/methodology/approach
The electrochemical current signals, obtained from two identical working electrodes (carbon steel electrodes) exposed to simulated concrete pore solution, sparged simultaneously with SO2 and CO2 were studied using wavelet transforms.
Findings
The results show two steps of passive oxide layer formation: formation of defective passive oxide layer, and strengthening of the passive oxide layer. The passive oxide layer breakdown where CO2 as well as SO2 are involved occurred at a pH of approximately 11. Both the EDP and SDPS plots should be used, simultaneously, to characterize the processes occurring on the surfaces of the exposed electrodes.
Practical implications
The results that were obtained can be regarded as the basis for better understanding and improvement of the noise analysis method.
Originality/value
This paper studies the corrosion behavior of carbon steel rebar before and after the simultaneous introduction of CO2 and SO2 gases in simulated pore solution, using EDP and SDPS plots obtained from the electrochemical current signals at different pH values.
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