scholarly journals Ultrasonic Guided Waves-Based Monitoring of Rail Head: Laboratory and Field Tests

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Piervincenzo Rizzo ◽  
Marcello Cammarata ◽  
Ivan Bartoli ◽  
Francesco Lanza di Scalea ◽  
Salvatore Salamone ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Guided Waves ◽  
Detection System ◽  
Damage Index ◽  
Ultrasonic Guided Waves ◽  
Inspection System ◽  
Discrete Wavelet ◽  
Outlier Analysis ◽  
Rail Head ◽  
Rail Inspection

Recent train accidents have reaffirmed the need for developing a rail defect detection system more effective than that currently used. One of the most promising techniques in rail inspection is the use of ultrasonic guided waves and noncontact probes. A rail inspection prototype based on these concepts and devoted to the automatic damage detection of defects in rail head is the focus of this paper. The prototype includes an algorithm based on wavelet transform and outlier analysis. The discrete wavelet transform is utilized to denoise ultrasonic signals and to generate a set of relevant damage sensitive data. These data are combined into a damage index vector fed to an unsupervised learning algorithm based on outlier analysis that determines the anomalous conditions of the rail. The first part of the paper shows the prototype in action on a railroad track mock-up built at the University of California, San Diego. The mock-up contained surface and internal defects. The results from three experiments are presented. The importance of feature selection to maximize the sensitivity of the inspection system is demonstrated here. The second part of the paper shows the results of field testing conducted in south east Pennsylvania under the auspices of the U.S. Federal Railroad Administration.

scholarly journals Damage Detection on Sudden Stiffness Reduction Based on Discrete Wavelet Transform

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Bo Chen ◽  
Zhi-wei Chen ◽  
Gan-jun Wang ◽  
Wei-ping Xie
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Discrete Wavelet Transform ◽  
Sudden Change ◽  
Damage Index ◽  
Time Instant ◽  
Discrete Wavelet ◽  
Building Structures ◽  
Stiffness Reduction ◽  
Shear Building

The sudden stiffness reduction in a structure may cause the signal discontinuity in the acceleration responses close to the damage location at the damage time instant. To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. The signal discontinuity of the structural acceleration responses of an example building is extracted based on the discrete wavelet transform. It is proved that the variation of the first level detail coefficients of the wavelet transform at damage instant is linearly proportional to the magnitude of the stiffness reduction. A new damage index is proposed and implemented to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the effectiveness and reliability of the proposed damage index for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also investigated. The made observations demonstrate that the proposed damage index can accurately identify the sudden damage events if the noise intensity is limited.

scholarly journals Monitoring of Fatigue Crack Propagation by Damage Index of Ultrasonic Guided Waves Calculated by Various Acoustic Features

2019 ◽  
Vol 9 (20) ◽  
pp. 4254 ◽  
Author(s):  
Hashen Jin ◽  
Jiajia Yan ◽  
Weibin Li ◽  
Xinlin Qing
Keyword(s):  
Fatigue Crack ◽  
Crack Propagation ◽  
Frequency Domain ◽  
Fatigue Crack Propagation ◽  
Guided Waves ◽  
Fatigue Cracks ◽  
Damage Index ◽  
Ultrasonic Guided Waves ◽  
Acoustic Features ◽  
Reliable Technique

Under cyclic and repetitive loads, fatigue cracks can be further propagated to a crucial level by accumulation, causing detrimental effects to structural integrity and potentially resulting in catastrophic consequences. Therefore, there is a demand to develop a reliable technique to monitor fatigue cracks quantitatively at an early stage. The objective of this paper is to characterize the propagation of fatigue cracks using the damage index (DI) calculated by various acoustic features of ultrasonic guided waves. A hybrid DI scheme for monitoring fatigue crack propagation is proposed using the linear fusion of damage indices (DIs) and differential fusion of DIs. An experiment is conducted on an SMA490BW steel plate-like structure to verify the proposed hybrid DIs scheme. The experimental results show that the hybrid DIs from various acoustic features can be used to quantitatively characterize the propagation of fatigue cracks, respectively. It is found that the fused DIs calculated by the acoustic features in the frequency domain have an improved reliable manner over those of the time domain. It is also clear that the linear and differential amplitude fusion DIs in the frequency domain are more promising to indicate the propagation of fatigue cracks quantitatively than other fused ones.

Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

2005 ◽  
Vol 127 (3) ◽  
pp. 294-303 ◽  
Author(s):  
Piervincenzo Rizzo ◽  
Ivan Bartoli ◽  
Alessandro Marzani ◽  
Francesco Lanza di Scalea
Keyword(s):  
Guided Waves ◽  
Fourier Transforms ◽  
Damage Index ◽  
Classification Performance ◽  
Discrete Wavelet ◽  
Cross Sectional ◽  
Classification Framework ◽  
Discrete Wavelet Decomposition ◽  
Good Classification ◽  
Good Classification Performance

This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.

Detection of Defects in Welded Structures With Complex Geometry Using Statistical Method Based on Discrete Wavelet Transform

2009 ◽  
Author(s):  
Tsun-Yen Wu ◽  
I. Charles Ume ◽  
Matthew D. Rogge
Keyword(s):  
Wavelet Transform ◽  
Energy Distribution ◽  
Discrete Wavelet Transform ◽  
Statistical Method ◽  
Defect Detection ◽  
Complex Geometry ◽  
Inspection System ◽  
Discrete Wavelet ◽  
Electromagnetic Acoustic Transducer ◽  
The Difference

In this paper, an inspection system and a defect detection method are presented. A welded sample with complex geometry was placed on an inspection system and inspected by generating ultrasound on one side of the weld and receiving on the other with an electromagnetic acoustic transducer (EMAT) sensor. Ultrasonic signals along the weld were acquired at locations with 1 mm distance between inspections. In order to detect the presence of defects, a statistical method based on Discrete Wavelet Transform (DWT) is implemented. Energy of each location along the weld is calculated and useful information indicating presence of defects is extracted by DWT using different mother wavelets. By comparing the energy distribution obtained from a particular sample, or a target, with a baseline energy distribution, defect locations are predicted. The baseline energy distribution is obtained by averaging energy distributions calculated for all inspected samples. The difference between a target and the reference is viewed as an indication of presence of defects. The results showed that the method can isolate signal changes that were caused by defects. Comparison to destructive cut-checks shows the accuracy of defect detection is high.

Predictions of defect detection performance of air-coupled ultrasonic rail inspection system

2017 ◽  
Vol 17 (3) ◽  
pp. 684-705 ◽  
Author(s):  
Stefano Mariani ◽  
Francesco Lanza di Scalea
Keyword(s):  
Field Test ◽  
Defect Detection ◽  
Guided Waves ◽  
Numerical Study ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Inspection System ◽  
False Alarms ◽  
Operating Variables ◽  
Rail Inspection

A rail inspection system based on ultrasonic guided waves and non-contact (air-coupled) ultrasound transduction is under development at the University of California at San Diego. The system targets defects in the rail head that are major causes of train accidents. Because of the high acoustic impedance mismatch between air and steel, the non-contact system poses severe challenges and questions on the defect detection performance. This article presents an extensive numerical study, conducted with a local interaction simulation approach, to model the ultrasound propagation and interaction with defects in the proposed system. This model was used to predict the expected detection performance of the system in the presence of various defects of different sizes and positions, and at varying levels of signal-to-noise ratios. When possible, operating variables for the model were chosen consistently with the field test of an experimental prototype that was conducted in 2014. The defect detection performance was evaluated through the computation of receiver operating characteristic curves in terms of probability of detection versus probability of false alarms. The study indicates that despite the challenges of non-contact probing of the rail, quite satisfactory inspection performance can be expected for a variety of defect types, sizes, and positions. Beyond the specific cases examined in this article, this numerical framework can also be used in the future to examine a larger variety of field test conditions.

Damage Detection in CFRP Bonded Structures by Using Fiber Bragg Grating Sensors as Ultrasonic Wave Receivers

2007 ◽  
Vol 334-335 ◽  
pp. 1137-1140
Author(s):  
Nobuo Takeda ◽  
Y. Okabe ◽  
J. Kuwahara ◽  
Toshimichi Ogisu ◽  
Seiji Kojima
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Fiber Bragg Grating ◽  
Correlation Coefficient ◽  
Ultrasonic Wave ◽  
Detection System ◽  
Damage Index ◽  
Adhesive Layer ◽  
Ultrasonic Waves ◽  
Bragg Grating

The authors developed a damage detection system that generates ultrasonic waves with a piezo-ceramic actuator and receives them by a fiber Bragg grating (FBG) sensor. In this research, this system was applied to evaluate debonding progress in CFRP skin/stringer bonded structures. FBG sensors were bonded on the stringer or embedded in the adhesive layer. Then, ultrasonic wave at 300kHz was propagated through the debonded region, and the wavelet transform was applied to the received waveform. After that, a new damage indexand a correlation coefficient were calculated from the distribution of the wavelet transform coefficient. As a result, the damage index increased and the correlation coefficient decreased with an increase in the debonded area. Hence the length of the debonding between the skin and the stringer could be well evaluated.

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