scholarly journals Improvement of Accuracy in Damage Localization Using Frequency Slice Wavelet Transform

2012 ◽  
Vol 19 (4) ◽  
pp. 585-596 ◽  
Author(s):  
Xinglong Liu ◽  
Zhongwei Jiang ◽  
Zhonghong Yan
Keyword(s):  
Wavelet Transform ◽  
Damage Identification ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Propagation Distance ◽  
Primary Objective ◽  
Frequency Resolution ◽  
Damage Localization ◽  
Arrival Times ◽  
Time Frequency

Damage localization is a primary objective of damage identification. This paper presents damage localization in beam structure using impact-induced Lamb wave and Frequency Slice Wavelet Transform (FSWT). FSWT is a new time-frequency analysis method and has the adaptive resolution feature. The time-frequency resolution is a vital factor affecting the accuracy of damage localization. In FSWT there is a unique parameter controlling the time-frequency resolution. To improve the accuracy of damage localization, a generalized criterion is proposed to determine the parameter value for achieving a suitable time-frequency resolution. For damage localization, the group velocity dispersion curve (GVDC) of A0Lamb waves in beam is first accurately estimated using FSWT, and then the arrival times of reflection wave from the crack for some individual frequency components are determined. An average operation on the calculated propagation distance is then performed to further improve the accuracy of damage localization.

Noncontact Evaluation of Defects in Thin Plate With Multimodes Lamb’s Waves and Wavelet Transform

2002 ◽  
Author(s):  
Morimasa Murase ◽  
Koichiro Kawashima
Keyword(s):  
Group Velocity ◽  
Thin Plate ◽  
Wavelet Transformation ◽  
Velocity Dispersion ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Dispersion Curves ◽  
Contact Method ◽  
Intact Specimen ◽  
Time Frequency

Multimode’s Lamb waves in aluminum plates with various defects were excited by a Q-switched Nd:YAG laser. The Lamb waves past through the defects were received a laser interferometer. The received signals of the Lamb waves are processed by the wavelet transformation. The wavelet transformation is generally shown on the time-frequency domain. By dividing a propagation distance by the time, the group velocities are identified. In this way, group velocity dispersion maps of multimode’s Lamb waves are constructed with the received temporal signals. By changing the shape of the mother wavelet, Gabor function, we can identify the dispersion curves of the higher mode Lamb waves. The group velocity dispersion maps of a intact specimen agree well on theoretical dispersion curves of S0, A0, S1, A1, S2, A2, and A3 modes. The difference between the dispersion maps of the intact specimen and that with defects clearly visualizes the existence of defects. This non-contact method is effective for inspecting various defects in thin plate structures.

scholarly journals Damage Identification of Large Generator Stator Insulation Based on PZT Sensor Systems and Hybrid Features of Lamb Waves

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2745 ◽  
Author(s):  
Ruihua Li ◽  
Hao Li ◽  
Bo Hu
Keyword(s):  
Power Systems ◽  
Lamb Wave ◽  
Damage Identification ◽  
Lamb Waves ◽  
Fem Simulation ◽  
Correlation Coefficients ◽  
Hybrid Features ◽  
Material Inhomogeneity ◽  
Time Frequency ◽  
Damage Imaging

Large generators are the principal pieces of equipment in power systems, and their operation reliability critically depends on the stator insulation. Damages in stator insulation will gradually lead to the failure and breakdown of generator. Due to the advantages of Lamb waves in Structural health monitoring (SHM), in this study, a distributed piezoelectric (PZT) sensor system and hybrid features of the Lamb waves are introduced to identify stator insulation damage of large generator. A hierarchical probability damage-imaging (PDI) algorithm is proposed to tackle the material inhomogeneity and anisotropy of the stator insulation. The proposed method includes three steps: global detection using correlation coefficients, local detection using Time of flight (ToF) along with the amplitude of damage-scattered Lamb wave, and final images fusion. Wavelet Transform was used to extract the ToF of Lamb wave in terms of the time-frequency domain. Finite Element Modeling (FEM) simulation and experimental work were carried out to identify four typical stator insulation damages for validation, including inner void, inner delamination, puncture, and crack. Results show that the proposed method can precisely identify the location of stator insulation damage, and the reconstruction image can be used to identify the size of stator insulation damage.

scholarly journals Adapted Wavelet Transform for Twisted Blade Diagnosis in Multi Stage Rotor

2019 ◽  
Vol 255 ◽  
pp. 02011
Author(s):  
Ahmed M. Abdelrhman ◽  
M. Salman Leong ◽  
Y.H. Ali ◽  
Iftikhar Ahmad ◽  
Christina G. Georgantopoulou ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Rotor System ◽  
Fault Detection And Diagnosis ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Multi Stage ◽  
The Common ◽  
Detection And Diagnosis ◽  
Twisted Blade

This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wavelet transform and casing vibration. The common detection method (FFT) is effective only if sever blade faults occurred while the minor faults usually remain undetected. Wavelet analysis as alternative technique is still unable to fulfill the fault detection and diagnosis accurately due to its inadequate time-frequency resolution. In this paper, wavelet is adapted and its time-frequency is improved. Experimental study was undertaken to simulate multi stages rotor system. Results showed that the adapted wavelet analysis is effective in twisted blade diagnosis compared to the conventional one.

scholarly journals Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 573 ◽  
Author(s):  
Hossam Selim ◽  
Miguel Delgado Prieto ◽  
José Trull ◽  
Luis Romeral ◽  
Crina Cojocaru
Keyword(s):  
Wavelet Transform ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Ultrasonic Waves ◽  
Internal Defects ◽  
Destructive Testing ◽  
Arrival Times ◽  
Time Frequency ◽  
Ultrasonic Methods ◽  
Ultrasound Inspection

Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry.

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

Structural Health Monitoring of Thin Aluminum Plate Using Acoustic Sensors

2012 ◽  
Vol 622-623 ◽  
pp. 1389-1395
Author(s):  
R. Nishanth ◽  
K. Lingadurai ◽  
V. Malolan ◽  
Gowrishankar Wuriti ◽  
M.R.M. Babu
Keyword(s):  
Lamb Wave ◽  
Damage Identification ◽  
Lamb Waves ◽  
Time Domain Analysis ◽  
Ultrasonic Waves ◽  
Aluminum Plate ◽  
Identification System ◽  
Time Frequency ◽  
Thin Aluminum ◽  
Aluminum Plates

SHM is defined as “an emerging technology that can be defined as continuous, autonomous, real time, in-service monitoring of the physical condition of a structure by means of embedded or attached sensors with minimum manual intervention” .SHM provides the ability of a system to detect adverse changes within a system’s structure to enhance reliability and reduce maintenance costs. There are different Non-Destructive techniques like acoustic emission, ultrasonic, acousto-ultrasonic, guided ultrasonic waves or Lamb waves which are nowadays investigated for the development of an efficient and user-friendly damage identification system. This paper deals with the latter which is based on Lamb wave propagation. It has been developed especially for distinguishing different kinds of damages. The Lamb wave-based active SHM method uses piezoelectric (PZT) sensors to transmit and receive Lamb waves in a thin Aluminum plate. The Lamb wave modes (AO &SO) travel into the structure and are reflected by the structural boundaries, discontinuities, and damage. By studying their propagation and reflection, the presence of defect in the structure is determined. Laboratory level experiments have been carried out on thin Aluminum plates with angular, horizontal and vertical defect. The obtained waveform is filtered to avoid unwanted noise & disturbances using Savitzky-Golay filtering. The filtered waveforms are compared to differentiate the defects. Short Time Fourier Transform has been carried out on the acquired waveform. This study provides significant insight into the problem of identifying localized damages in the structure using PZT and dispersion of signal after they interact with different types of damage. Those small defects like the horizontal one that may be nearly missed in time domain analysis can also be clearly identified in the STFT analysis. Moreover the occurrence of So mode is also clearly seen. Thus, Lamb waves generated by PZT sensors and time-frequency analysis techniques could be used effectively for damage detection in aluminum plate. This study has given a complete idea of the working and the basic requirements of SHM system.

Time Frequency Analysis of Dispersive Waves by Means of Wavelet Transform

1995 ◽  
Vol 62 (4) ◽  
pp. 841-846 ◽  
Author(s):  
Kikuo Kishimoto ◽  
Hirotsugu Inoue ◽  
Makoto Hamada ◽  
Toshikazu Shibuya
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Gabor Wavelet ◽  
Flexural Wave ◽  
Lateral Impact ◽  
Dispersive Waves ◽  
Time Frequency Analysis ◽  
Arrival Times ◽  
Time Frequency ◽  
Wide Range

A new approach is presented for investigating the dispersive character of structural waves. The wavelet transform is applied to the time-frequency analysis of dispersive waves. The flexural wave induced in a beam by lateral impact is considered. It is shown that the wavelet transform using the Gabor wavelet effectively decomposes the strain response into its time-frequency components. In addition, the peaks of the time-frequency distribution indicate the arrival times of waves. By utilizing this fact, the dispersion relation of the group velocity can be accurately identified for a wide range of frequencies.

A NOVEL R-PEAK DETECTION METHOD COMBINING ENERGY AND WAVELET TRANSFORM IN ELECTROCARDIOGRAM SIGNAL

2014 ◽  
Vol 26 (01) ◽  
pp. 1450007 ◽  
Author(s):  
Xiuling Liu ◽  
Jianli Yang ◽  
Xiaoyu Zhu ◽  
Suiping Zhou ◽  
Hongrui Wang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Peak Detection ◽  
Frequency Resolution ◽  
Detection Accuracy ◽  
Energy Window ◽  
Qrs Complex ◽  
Time Frequency ◽  
Qrs Morphology ◽  
Morphology Changes

QRS complex is the most important part in electrocardiogram (ECG) as it contains the most important information of heart activities. R-peak detection is the first, yet crucial, step in most ECG automatic diagnose methods. Due to the existence of noise in ECG signals and changes in QRS morphology, most existing methods are not robust in different conditions. In the field of intelligent remote health caring, in addition to the detection accuracy, timeliness is also an important research issue. In this paper, wavelet transform and energy window transform are introduced, which form the basis of a novel R-peak detection method. Wavelet transform is used to efficiently reduce noise and highlight useful ECG signal for it has good time-frequency resolution characters, and energy window transform converts time domain signal to energy domain, which makes it easier to isolate QRS complex from other signals. As a result, influence from QRS morphology changes can be effectively alleviated. To validate the effectiveness of this new method, ECG records of MIT-BIH arrhythmia database are used in the experiments. The experiment results show that the proposed method is efficient and robust to noise and QRS morphology changes. The computational cost of the proposed method has also been evaluated.

Spectral decomposition of seismic data with continuous-wavelet transform

Geophysics ◽  
2005 ◽  
Vol 70 (6) ◽  
pp. P19-P25 ◽  
Author(s):  
Satish Sinha ◽  
Partha S. Routh ◽  
Phil D. Anno ◽  
John P. Castagna
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Scale ◽  
Fixed Time ◽  
Frequency Resolution ◽  
Continuous Wavelet ◽  
Window Length ◽  
Time Frequency ◽  
Frequency Map

This paper presents a new methodology for computing a time-frequency map for nonstationary signals using the continuous-wavelet transform (CWT). The conventional method of producing a time-frequency map using the short time Fourier transform (STFT) limits time-frequency resolution by a predefined window length. In contrast, the CWT method does not require preselecting a window length and does not have a fixed time-frequency resolution over the time-frequency space. CWT uses dilation and translation of a wavelet to produce a time-scale map. A single scale encompasses a frequency band and is inversely proportional to the time support of the dilated wavelet. Previous workers have converted a time-scale map into a time-frequency map by taking the center frequencies of each scale. We transform the time-scale map by taking the Fourier transform of the inverse CWT to produce a time-frequency map. Thus, a time-scale map is converted into a time-frequency map in which the amplitudes of individual frequencies rather than frequency bands are represented. We refer to such a map as the time-frequency CWT (TFCWT). We validate our approach with a nonstationary synthetic example and compare the results with the STFT and a typical CWT spectrum. Two field examples illustrate that the TFCWT potentially can be used to detect frequency shadows caused by hydrocarbons and to identify subtle stratigraphic features for reservoir characterization.

Instantaneous spectral attributes using scales in continuous-wavelet transform

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. WA137-WA142 ◽  
Author(s):  
Satish Sinha ◽  
Partha Routh ◽  
Phil Anno
Keyword(s):  
Wavelet Transform ◽  
Frequency Spectrum ◽  
Continuous Wavelet Transform ◽  
Time Window ◽  
Frequency Resolution ◽  
Rock Properties ◽  
Center Frequency ◽  
Continuous Wavelet ◽  
Frequency Spectra ◽  
Time Frequency

Instantaneous spectral properties of seismic data — center frequency, root-mean-square frequency, bandwidth — often are extracted from time-frequency spectra to describe frequency-dependent rock properties. These attributes are derived using definitions from probability theory. A time-frequency spectrum can be obtained from approaches such as short-time Fourier transform (STFT) or time-frequency continuous-wavelet transform (TFCWT). TFCWT does not require preselecting a time window, which is essential in STFT. The TFCWT method converts a scalogram (i.e., time-scale map) obtained from the continuous-wavelet transform (CWT) into a time-frequency map. However, our method includes mathematical formulas that compute the instantaneous spectral attributes from the scalogram (similar to those computed from the TFCWT), avoiding conversion into a time-frequency spectrum. Computation does not require a predefined window length because it is based on the CWT. This technique optimally decomposes a multiscale signal. For nonstationary signal analysis, spectral decomposition from [Formula: see text] has better time-frequency resolution than STFT, so the instantaneous spectral attributes from CWT are expected to be better than those from STFT.

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