The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

S. Olutunde Oyadiji - One of the best experts on this subject based on the ideXlab platform.

  • Sampling interval sensitivity analysis for crack detection by stationary wavelet transform
    Structural Control and Health Monitoring, 2011
    Co-Authors: Shuncong Zhong, S. Olutunde Oyadiji
    Abstract:

    The purpose of this paper was to analyze the sampling interval sensitivity for crack detection by stationary wavelet transform (SWT) in order to facilitate the identification of crack locations in beam-like structures. SWT is a redundant transform that doubles the number of input samples at each iteration. It has been shown that the SWT decomposition Detail Coefficient of mode shapes of beam-like structure can provide sensitive crack indication that is useful for damage detection. However, the sampling interval is an important factor that affects the sensitivity of crack detection. Three SWT methods were analyzed, including the method using the SWT of the original mode shape (designated SWT-1), the method using the difference between the Detail Coefficients of the SWT of the left-half and the reconstructed right-half sets of the mode shapes (designated SWT-2), and the method using the difference between the Detail Coefficient of the left-half and right-half sets of the interpolated mode shapes (designated SWT-3). The modal responses of damaged beams with single and multiple cracks are computed using the finite element method. The curve of the peak value of SWT Detail Coefficient versus sampling interval was obtained using a fifth-order polynomial fit method. The effects of crack depth, crack width, and crack locations on the sensitivity of sampling interval on crack detection are investigated. SWT-2 method has a shortcoming in determining whether the cracks are located at the true crack location or its mirror image position at different sampling intervals. In order to overcome this shortcoming, two rules are proposed for the determination of true crack locations and the selection of sampling intervals for single crack or multiple crack detection.

  • Crack detection in simply supported beams using stationary wavelet transform of modal data
    Structural Control and Health Monitoring, 2011
    Co-Authors: Shuncong Zhong, S. Olutunde Oyadiji
    Abstract:

    A new approach, using stationary wavelet transform (SWT), is proposed for crack detection in beam-like structures. SWT is a redundant transform that doubles the number of input samples at each iteration, which can provide a more accurate estimation of the variances and facilitate the identification of salient features in a signal, especially for recognizing noise or signal rupture. The mode shape of a cracked beam with a small crack depth, while apparently a single smooth curve, actually exhibits a local peak or discontinuity in the region of damage. The mode shape ‘signal’ can be approximately considered as that of the intact beam contaminated by ‘noise’ which consists of response noise and the additional response due to the crack. The signal can be decomposed by SWT into a smooth curve, called approximation Coefficient, and a Detail Coefficient curve, which includes crack information, respectively. In this paper, the modal responses of damaged simply supported beams are computed using the finite element method in conjunction with some experimental tests. The effect of noise on the proposed method is also studied. It is shown that SWT of the modal data of cracked simply supported beams provides a better crack indication than conventional discrete wavelet transform. The relationship between SWT Detail Coefficient and crack size (depth and width) are also discussed. A new method based on the average difference of the SWT Detail Coefficient of vibration bending modes of a cracked beam and an intact beam is proposed as a damage index and verified

  • Wavelet-Based Structural Damage Detection
    Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise Parts A B and C, 2007
    Co-Authors: Shuncong Zhong, S. Olutunde Oyadiji
    Abstract:

    In this paper, a new wavelet-based approach for crack identification in beam-like structures is presented and applied to simply-supported beams with single or multiple cracks. A novel damage index, based on finding the difference between two sets of Detail Coefficients obtained by the use of the Stationary Wavelet Transform (SWT) of two reconstructed sets of modal displacement data of the cracked beam-like structure, is proposed for single crack detection or multiple crack detection. These two sets of mode shape data represent the left half and the modified right half of the modal data of the structure. Currently, SWT is widely used in the field of image processing for image noise reduction and image quality improvement. However, because it can provide an accurate estimate of the variances at each scale and facilitate the identification of salient features in a signal, SWT has great potential in the field of structural damage detection. In this paper, the modal responses of the damaged simply supported beams used are computed using the finite element method (FEM). The modal data generate is decomposed by SWT into a smooth curve, called approximation Coefficient, and Detail Coefficient. It is shown that the Detail Coefficient includes crack information that is useful for structural damage detection. Therefore, a novel damage index, the difference of the SWT Detail Coefficients of two reconstructed sets of modal displacement data, is proposed and employed. The numerical simulation results show that the proposed wavelet-based method has a good anti-noise ability and it does not require the modal parameters of an intact structure as a baseline for crack detection. Therefore, it can be recommended for real applications in structural health monitoring and damage detection.

Shuncong Zhong - One of the best experts on this subject based on the ideXlab platform.

  • Sampling interval sensitivity analysis for crack detection by stationary wavelet transform
    Structural Control and Health Monitoring, 2011
    Co-Authors: Shuncong Zhong, S. Olutunde Oyadiji
    Abstract:

    The purpose of this paper was to analyze the sampling interval sensitivity for crack detection by stationary wavelet transform (SWT) in order to facilitate the identification of crack locations in beam-like structures. SWT is a redundant transform that doubles the number of input samples at each iteration. It has been shown that the SWT decomposition Detail Coefficient of mode shapes of beam-like structure can provide sensitive crack indication that is useful for damage detection. However, the sampling interval is an important factor that affects the sensitivity of crack detection. Three SWT methods were analyzed, including the method using the SWT of the original mode shape (designated SWT-1), the method using the difference between the Detail Coefficients of the SWT of the left-half and the reconstructed right-half sets of the mode shapes (designated SWT-2), and the method using the difference between the Detail Coefficient of the left-half and right-half sets of the interpolated mode shapes (designated SWT-3). The modal responses of damaged beams with single and multiple cracks are computed using the finite element method. The curve of the peak value of SWT Detail Coefficient versus sampling interval was obtained using a fifth-order polynomial fit method. The effects of crack depth, crack width, and crack locations on the sensitivity of sampling interval on crack detection are investigated. SWT-2 method has a shortcoming in determining whether the cracks are located at the true crack location or its mirror image position at different sampling intervals. In order to overcome this shortcoming, two rules are proposed for the determination of true crack locations and the selection of sampling intervals for single crack or multiple crack detection.

  • Crack detection in simply supported beams using stationary wavelet transform of modal data
    Structural Control and Health Monitoring, 2011
    Co-Authors: Shuncong Zhong, S. Olutunde Oyadiji
    Abstract:

    A new approach, using stationary wavelet transform (SWT), is proposed for crack detection in beam-like structures. SWT is a redundant transform that doubles the number of input samples at each iteration, which can provide a more accurate estimation of the variances and facilitate the identification of salient features in a signal, especially for recognizing noise or signal rupture. The mode shape of a cracked beam with a small crack depth, while apparently a single smooth curve, actually exhibits a local peak or discontinuity in the region of damage. The mode shape ‘signal’ can be approximately considered as that of the intact beam contaminated by ‘noise’ which consists of response noise and the additional response due to the crack. The signal can be decomposed by SWT into a smooth curve, called approximation Coefficient, and a Detail Coefficient curve, which includes crack information, respectively. In this paper, the modal responses of damaged simply supported beams are computed using the finite element method in conjunction with some experimental tests. The effect of noise on the proposed method is also studied. It is shown that SWT of the modal data of cracked simply supported beams provides a better crack indication than conventional discrete wavelet transform. The relationship between SWT Detail Coefficient and crack size (depth and width) are also discussed. A new method based on the average difference of the SWT Detail Coefficient of vibration bending modes of a cracked beam and an intact beam is proposed as a damage index and verified

  • Wavelet-Based Structural Damage Detection
    Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise Parts A B and C, 2007
    Co-Authors: Shuncong Zhong, S. Olutunde Oyadiji
    Abstract:

    In this paper, a new wavelet-based approach for crack identification in beam-like structures is presented and applied to simply-supported beams with single or multiple cracks. A novel damage index, based on finding the difference between two sets of Detail Coefficients obtained by the use of the Stationary Wavelet Transform (SWT) of two reconstructed sets of modal displacement data of the cracked beam-like structure, is proposed for single crack detection or multiple crack detection. These two sets of mode shape data represent the left half and the modified right half of the modal data of the structure. Currently, SWT is widely used in the field of image processing for image noise reduction and image quality improvement. However, because it can provide an accurate estimate of the variances at each scale and facilitate the identification of salient features in a signal, SWT has great potential in the field of structural damage detection. In this paper, the modal responses of the damaged simply supported beams used are computed using the finite element method (FEM). The modal data generate is decomposed by SWT into a smooth curve, called approximation Coefficient, and Detail Coefficient. It is shown that the Detail Coefficient includes crack information that is useful for structural damage detection. Therefore, a novel damage index, the difference of the SWT Detail Coefficients of two reconstructed sets of modal displacement data, is proposed and employed. The numerical simulation results show that the proposed wavelet-based method has a good anti-noise ability and it does not require the modal parameters of an intact structure as a baseline for crack detection. Therefore, it can be recommended for real applications in structural health monitoring and damage detection.

Usha Shenoy - One of the best experts on this subject based on the ideXlab platform.

  • ISCAS (3) - Classification of power system faults using wavelet transforms and probabilistic neural networks
    Proceedings of the 2003 International Symposium on Circuits and Systems 2003. ISCAS '03., 1
    Co-Authors: K.h. Kashyap, Usha Shenoy
    Abstract:

    Automation of power system fault identification using information conveyed by the wavelet analysis of power system transients is proposed. The Probabilistic Neural Network (PNN) for detecting the type of fault is used. The work presented in this paper is focused on identification of simple power system faults. Wavelet Transform (WT) of the transient disturbance caused as a result of the occurrence of a fault is performed. The Detail Coefficient for each type of simple fault is characteristic in nature. PNN is used for distinguishing the Detail Coefficients and hence the faults.

Messaoud Benidir - One of the best experts on this subject based on the ideXlab platform.

  • Multiresolution wavelet-based QRS complex detection algoritm suited toseveral abnormal morphologies
    IET Signal Processing, 2014
    Co-Authors: Fatiha Bouaziz, Daoud Boutana, Messaoud Benidir
    Abstract:

    The electrocardiogram (ECG) signal is considered as one of the most important tools in clinical practice in order to assess the cardiac status of patients. In this study, an improved QRS (Q wave, R wave, S wave) complex detection algorithm is proposed based on the multiresolution wavelet analysis. In the first step, high frequency noise and baseline wander can be distinguished from ECG data based on their specific frequency contents. Hence, removing corresponding Detail Coefficients leads to enhance the performance of the detection algorithm. After this, the author's method is based on the power spectrum of decomposition signals for selecting Detail Coefficient corresponding to the frequency band of the QRS complex. Hence, the authors have proposed a function g as the combination of the selected Detail Coefficients using two parameters λ1 and λ2, which correspond to the proportion of the frequency ranges of the selected Detail compared with the frequency range of the QRS complex. The proposed algorithm is evaluated using the whole arrhythmia database. It presents considerable capability in cases of low signal-to-noise ratio, high baseline wander and abnormal morphologies. The results of evaluation show the good detection performance; they have obtained a global sensitivity of 99.87%, a positive predectivity of 99.79% and a percentage error of 0.34%.

  • Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies
    IET Signal Processing, 2014
    Co-Authors: Fatiha Bouaziz, Daoud Boutana, Messaoud Benidir
    Abstract:

    International audienceThe electrocardiogram (ECG) signal is considered as one of the most important tools in clinical practice in order to assess the cardiac status of patients. In this study, an improved QRS (Q wave, R wave, S wave) complex detection algorithm is proposed based on the multiresolution wavelet analysis. In the first step, high frequency noise and baseline wander can be distinguished from ECG data based on their specific frequency contents. Hence, removing corresponding Detail Coefficients leads to enhance the performance of the detection algorithm. After this, the author's method is based on the power spectrum of decomposition signals for selecting Detail Coefficient corresponding to the frequency band of the QRS complex. Hence, the authors have proposed a function g as the combination of the selected Detail Coefficients using two parameters λ1 and λ2, which correspond to the proportion of the frequency ranges of the selected Detail compared with the frequency range of the QRS complex. The proposed algorithm is evaluated using the whole arrhythmia database. It presents considerable capability in cases of low signal-to-noise ratio, high baseline wander and abnormal morphologies. The results of evaluation show the good detection performance; they have obtained a global sensitivity of 99.87%, a positive predectivity of 99.79% and a percentage error of 0.34%

Nagy I Elkalashy - One of the best experts on this subject based on the ideXlab platform.

  • DWT and Bayesian technique for enhancing earth fault protection in MV networks
    2009 IEEE PES Power Systems Conference and Exposition, 2009
    Co-Authors: Nagy I Elkalashy, Matti Lehtonen, Naser Tarhuni
    Abstract:

    In this paper, a Bayesian selectivity technique is introduced to identify the faulty feeder in compensated medium voltage (MV) networks. The proposed technique is based on a conditional probabilistic method applied on features extracted from the residual currents only using the Discrete Wavelet Transform (DWT). DWT enhances to localize initial transients generated in the network due to the fault event. The absolute sum of a window of the DWT Detail Coefficient is used to detect the fault. The conditional probability provides the selectivity decision. The fault cases occurring at different locations in a compensated 20 kV network are simulated by ATP/EMTP concerning practical fault case such as arcing faults. Test results corroborate the efficacy of proposed technique.

  • Operation evaluation of DWT-based earth fault detection in unearthed MV networks
    2008 12th International Middle-East Power System Conference, 2008
    Co-Authors: Nagy I Elkalashy, Matti Lehtonen, H.a. Darwish, Abdel-maksoud I. Taalab, M.a. Izzularab
    Abstract:

    A novel selectivity technique to estimate the faulty feeder in MV networks was introduced in [1]. This technique depended on the directionality of discrete wavelet transform (DWT) Detail Coefficient of a residual current of each feeder with respect to the DWT Detail Coefficient of the residual voltage. The algorithm efficacy has been examined with high impedance arcing fault due to leaning trees. In this paper, the algorithm performance is tested with resistance earth faults over a wide range of the fault resistance values (1 mOmega to 100 kOmega) as well as concerning practical fault cases such as arcing faults. The fault cases occurring at different locations in an unearthed 20 kV network are simulated by ATP/EMTP. Test cases confirm the efficacy of the proposed technique.

  • DTW-based detection of high impedance fault due to leaning trees in compensated MV networks
    2007
    Co-Authors: Nagy I Elkalashy, Matti Lehtonen, H.a. Darwish, Abdel-maksoud I. Taalab, M.a. Izzularab
    Abstract:

    Features of faults due to leaning trees are extracted using discrete wavelet transform (DWT) and an absolute sum of the Detail Coefficient d3 over a period of power frequency cycle is used as a detector. DWT is processed on the residual voltage at different measuring nodes allocated in a wide area of the network and such correlation of DWT performance at different nodes can be carried out using distributed wireless sensors. Therefore, the fault detection is confirmed by numerous detectors. Other fault features that can enhance the detection security are that the initial transients are frequently repeated and therefore localized with each current zero crossing. The fault detection selectivity is carried out considering the multiplications of DWT Detail Coefficients of the residual current and voltage at each measuring nodes. A sum over two cycles is then computed to estimate the direction of the transient power and therefore to discriminate between the healthy and faulty sections. Test cases prove with evidence the efficacy of proposed technique.