Abstract: Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.
Abstract: First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.
Abstract: In this study, a spatial wavelet-based crack localization technique for a thick beam is presented. Wavelet scale in spatial wavelet transformation is optimized to enhance crack detection sensitivity. A windowing function is also employed to erase the edge effect of the wavelet transformation, which enables the method to detect and localize cracks near the beam/measurement boundaries. Theoretical model and vibration analysis considering the crack effect are first proposed and performed in MATLAB based on the Timoshenko beam model. Gabor wavelet family is applied to the beam vibration mode shapes derived from the theoretical beam model to magnify the crack effect so as to locate the crack. Relative wavelet coefficient is obtained for sensitivity analysis by comparing the coefficient values at different positions of the beam with the lowest value in the intact area of the beam. Afterward, the optimal wavelet scale corresponding to the highest relative wavelet coefficient at the crack position is obtained for each vibration mode, through numerical simulations. The same procedure is performed for cracks with different sizes and positions in order to find the optimal scale range for the Gabor wavelet family. Finally, Hanning window is applied to different vibration mode shapes in order to overcome the edge effect problem of wavelet transformation and its effect on the localization of crack close to the measurement boundaries. Comparison of the wavelet coefficients distribution of windowed and initial mode shapes demonstrates that window function eases the identification of the cracks close to the boundaries.
Abstract: Wavelet analysis has some strong advantages over Fourier analysis, as it allows a time-frequency domain analysis, allowing optimal resolution and flexibility. As a result, they have been satisfactorily applied in almost all the fields of communication systems including OFDM which is a strong candidate for next generation of wireless technology. In this paper, the performances of wavelet based Multiuser Multiple Input and Multiple Output Orthogonal Frequency Division Multiplexing (MU-MIMO OFDM) systems are analyzed in terms of BER. It has been shown that the wavelet based systems outperform the classical FFT based systems. This analysis also unfolds an interesting result, where wavelet based OFDM system will have a constant error performance using Regularized Channel Inversion (RCI) beamforming for any number of users, and outperforms in all possible scenario in a multiuser environment. An extensive computer simulations show that a PAPR reduction of up to 6.8dB can be obtained with M=64.
Abstract: This paper presents a new strategy of identification
and classification of pathological voices using the hybrid method
based on wavelet transform and neural networks. After speech
acquisition from a patient, the speech signal is analysed in order to
extract the acoustic parameters such as the pitch, the formants, Jitter,
and shimmer. Obtained results will be compared to those normal and
standard values thanks to a programmable database. Sounds are
collected from normal people and patients, and then classified into
two different categories. Speech data base is consists of several
pathological and normal voices collected from the national hospital
“Rabta-Tunis". Speech processing algorithm is conducted in a
supervised mode for discrimination of normal and pathology voices
and then for classification between neural and vocal pathologies
(Parkinson, Alzheimer, laryngeal, dyslexia...). Several simulation
results will be presented in function of the disease and will be
compared with the clinical diagnosis in order to have an objective
evaluation of the developed tool.
Abstract: Insulation used in transformer is mostly oil pressboard insulation. Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharges (PD) cause insulation degradation and premature failure of insulation. Online monitoring of PDs can reduce the risk of catastrophic failure of transformers. There are different techniques of partial discharge measurement like, electrical, optical, acoustic, opto-acoustic and ultra high frequency (UHF). Being non invasive and non interference prone, acoustic emission technique is advantageous for online PD measurement. Acoustic detection of p.d. is based on the retrieval and analysis of mechanical or pressure signals produced by partial discharges. Partial discharges are classified according to the origin of discharges. Their effects on insulation deterioration are different for different types. This paper reports experimental results and analysis for classification of partial discharges using acoustic emission signal of laboratory simulated partial discharges in oil pressboard insulation system using three different electrode systems. Acoustic emission signal produced by PD are detected by sensors mounted on the experimental tank surface, stored on an oscilloscope and fed to computer for further analysis. The measured AE signals are analyzed using discrete wavelet transform analysis and wavelet packet analysis. Energy distribution in different frequency bands of discrete wavelet decomposed signal and wavelet packet decomposed signal is calculated. These analyses show a distinct feature useful for PD classification. Wavelet packet analysis can sort out any misclassification arising out of DWT in most cases.
Abstract: The ability to detect and classify the type of fault
plays a great role in the protection of power system. This procedure
is required to be precise with no time consumption. In this paper
detection of fault type has been implemented using wavelet analysis
together with wavelet entropy principle. The simulation of power
system is carried out using PSCAD/EMTDC. Different types of
faults were studied obtaining various current waveforms. These
current waveforms were decomposed using wavelet analysis into
different approximation and details. The wavelet entropy of such
decompositions is analyzed reaching a successful methodology for
fault classification. The suggested approach is tested using different
fault types and proven successful identification for the type of fault.
Abstract: In this work, the primary compressive strength
components of human femur trabecular bone are qualitatively
assessed using image processing and wavelet analysis. The Primary
Compressive (PC) component in planar radiographic femur trabecular
images (N=50) is delineated by semi-automatic image processing
procedure. Auto threshold binarization algorithm is employed to
recognize the presence of mineralization in the digitized images. The
qualitative parameters such as apparent mineralization and total area
associated with the PC region are derived for normal and abnormal
images.The two-dimensional discrete wavelet transforms are utilized
to obtain appropriate features that quantify texture changes in medical
images .The normal and abnormal samples of the human femur are
comprehensively analyzed using Harr wavelet.The six statistical
parameters such as mean, median, mode, standard deviation, mean
absolute deviation and median absolute deviation are derived at level
4 decomposition for both approximation and horizontal wavelet
coefficients. The correlation coefficient of various wavelet derived
parameters with normal and abnormal for both approximated and
horizontal coefficients are estimated. It is seen that in almost all cases
the abnormal show higher degree of correlation than normals. Further
the parameters derived from approximation coefficient show more
correlation than those derived from the horizontal coefficients. The
parameters mean and median computed at the output of level 4 Harr
wavelet channel was found to be a useful predictor to delineate the
normal and the abnormal groups.
Abstract: –In this paper the damage in clamped-free, clampedclamped and free-free beam are analyzed considering samples
without and with structural modifications. The damage location is
investigated by the use of the bispectrum and wavelet analysis. The
mathematical models are obtained using 2D elasticity theory and the
Finite Element Method (FEM). The numerical and experimental data
are approximated using the Particle Swarm Optimizer (PSO) method
and this way is possible to adjust the localization and the severity of
the damage. The experimental data are obtained through
accelerometers placed along the sample. The system is excited using
impact hammer.
Abstract: Wavelet transform or wavelet analysis is a recently
developed mathematical tool in applied mathematics. In numerical
analysis, wavelets also serve as a Galerkin basis to solve partial
differential equations. Haar transform or Haar wavelet transform has
been used as a simplest and earliest example for orthonormal wavelet
transform. Since its popularity in wavelet analysis, there are several
definitions and various generalizations or algorithms for calculating
Haar transform. Fast Haar transform, FHT, is one of the algorithms
which can reduce the tedious calculation works in Haar transform. In
this paper, we present a modified fast and exact algorithm for FHT,
namely Modified Fast Haar Transform, MFHT. The algorithm or
procedure proposed allows certain calculation in the process
decomposition be ignored without affecting the results.
Abstract: Nowadays Multilevel inverters are widely using in various applications. Modulation strategy at fundamental switching frequency like, SHEPWM is prominent technique to eliminate lower order of harmonics with less switching losses and better harmonic profile. The equations which are formed by SHE are highly nonlinear transcendental in nature, there may exist single, multiple or even no solutions for a particular MI. However, some loads such as electrical drives, it is required to operate in whole range of MI. In order to solve SHE equations for whole range of MI, intelligent techniques are well suited to solve equations so as to produce lest %THDV. Hence, this paper uses Continuous genetic algorithm for minimising harmonics. This paper also presents wavelet based analysis of harmonics. The developed algorithm is simulated and %THD from FFT analysis and Wavelet analysis are compared. MATLAB programming environment and SIMULINK models are used whenever necessary.
Abstract: Electromyography (EMG) signal processing has been investigated remarkably regarding various applications such as in rehabilitation systems. Specifically, wavelet transform has served as a powerful technique to scrutinize EMG signals since wavelet transform is consistent with the nature of EMG as a non-stationary signal. In this paper, the efficiency of wavelet transform in surface EMG feature extraction is investigated from four levels of wavelet decomposition and a comparative study between different mother wavelets had been done. To recognize the best function and level of wavelet analysis, two evaluation criteria, scatter plot and RES index are recruited. Hereupon, four wavelet families, namely, Daubechies, Coiflets, Symlets and Biorthogonal are studied in wavelet decomposition stage. Consequently, the results show that only features from first and second level of wavelet decomposition yields good performance and some functions of various wavelet families can lead to an improvement in separability class of different hand movements.
Abstract: This paper presents modern vibration signalprocessing
techniques for vehicle gearbox fault diagnosis, via the
wavelet analysis and the Squared Envelope (SE) technique. The
wavelet analysis is regarded as a powerful tool for the detection of
sudden changes in non-stationary signals. The Squared Envelope
(SE) technique has been extensively used for rolling bearing
diagnostics. In the present work a scheme of using the Squared
Envelope technique for early detection of gear tooth pit. The pitting
defect is manufactured on the tooth side of a fifth speed gear on the
intermediate shaft of a vehicle gearbox. The objective is to
supplement the current techniques of gearbox fault diagnosis based
on using the raw vibration and ordered signals. The test stand is
equipped with three dynamometers; the input dynamometer serves as
the internal combustion engine, the output dynamometers introduce
the load on the flanges of output joint shafts. The gearbox used for
experimental measurements is the type most commonly used in
modern small to mid-sized passenger cars with transversely mounted
powertrain and front wheel drive; a five-speed gearbox with final
drive gear and front wheel differential. The results show that the
approaches methods are effective for detecting and diagnosing
localized gear faults in early stage under different operation
conditions, and are more sensitive and robust than current gear
diagnostic techniques.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.