Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms

Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted on the vehicle rather than on the bridge to be monitored, with clear advantages in terms of cost and flexibility. This work aims at further exploring the feasibility and effectiveness of novel tools for indirect health monitoring of railway structures, by introducing a higher level of accuracy in damage modelling, achieve more close-to-reality results. A numerical study is carried out by means of a FE 3D model of a short span Warren truss bridge, simulating the dynamic interaction of the bridge/track/train structure. Two kinds of defects are simulated, the first one affecting the connection between the lower chord and the side diagonal member, the second one involving the joint between the cross-girder and the lower chord. Accelerations gathered from the train bogie in different working conditions and for different intensities of the damage level are analyzed through two time-frequency algorithms, namely Continuous Wavelet and Huang-Hilbert transforms, to evaluate their robustness to disturbing factors. Compared to previous studies, a complete 3D model of the rail vehicle, together with a 3D structural scheme of the bridge in place of the 2D equivalent scheme widely adopted in the literature, allow a more detailed and realistic representation of the effects of the bridge damage on the vehicle dynamics. Good numerical results are obtained from both the two algorithms in the case of the time-invariant track profile, whereas the Continuous Wavelet Transform is found to be more robust when a deterioration of track irregularity is simulated.

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Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response

Vibration ◽

10.3390/vibration4020028 ◽

2021 ◽

Vol 4 (2) ◽

pp. 422-445

Author(s):

Md Riasat Azim ◽

Mustafa Gül

Keyword(s):

Damage Detection ◽

Damage Identification ◽

Numerical Study ◽

Damage Index ◽

Strain Analysis ◽

Time History ◽

Principal Component ◽

Element Model ◽

Railway Bridges ◽

Truss Bridge

Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis.

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A numerical study of the effect of the number of turns of coil on the heat produced in the induction heating process in the 3d model

Iranian Journal of Physics Research ◽

10.29252/ijpr.18.3.408 ◽

2018 ◽

Vol 18 (3) ◽

pp. 408-419

Author(s):

A J shokri ◽

M H Tavakoli ◽

A Sabouri Dodaran ◽

M S Akhondi Khezrabad ◽

◽

...

Keyword(s):

Induction Heating ◽

3D Model ◽

Numerical Study ◽

Heating Process

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Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽

10.3390/e23010119 ◽

2021 ◽

Vol 23 (1) ◽

pp. 119

Author(s):

Tao Wang ◽

Changhua Lu ◽

Yining Sun ◽

Mei Yang ◽

Chun Liu ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Convolutional Neural Network ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Time Frequency ◽

Ecg Signals ◽

Rr Interval ◽

Frequency Components ◽

Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

Energies ◽

10.3390/en14154514 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4514

Author(s):

Vincent Becker ◽

Thilo Schwamm ◽

Sven Urschel ◽

Jose Alfonso Antonino-Daviu

Keyword(s):

Condition Monitoring ◽

False Alarms ◽

Signature Analysis ◽

Variable Speed ◽

Monitoring Methods ◽

Time Frequency ◽

Motor Current ◽

Load Point ◽

Bearing Defects ◽

Current Signature

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

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Simulation Study of Damage Identification Method Based on Lamb Wave Scattering in Aluminium Plate

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.2611 ◽

2012 ◽

Vol 433-440 ◽

pp. 2611-2618

Author(s):

Zhen Hua Tian ◽

Hong Yuan Li ◽

Hong Xu

Keyword(s):

Energy Distribution ◽

Lamb Wave ◽

Damage Identification ◽

Optimization Method ◽

Propagation Path ◽

Transient Dynamic Analysis ◽

Time Frequency ◽

Damage Location ◽

Identification Method ◽

Short Time

The propagation of scattering Lamb wave in plate was simulated using transient dynamic analysis in ANSYS. In order to extract the characteristic information of received signal for damage identification, the short time Fourier transform based on time-frequency analysis was utilized, and then the energy distribution and envelop of received signal were obtained. Based on the displacement contour of simulation and energy distribution, the propagation of scattering wave in plate with a through hole was examined. Also, a mathematic relationship between damage location and scattering signal was developed, with the help of wave propagation path through actuator, damage and sensor. A nonlinear optimization method was applied on the mathematic relationship to obtain the damage location. The damage identification method using scattering Lamb wave was therefore established.

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Strengthening and widening of steel pony truss bridges

Canadian Journal of Civil Engineering ◽

10.1139/l77-026 ◽

1977 ◽

Vol 4 (2) ◽

pp. 214-225

Author(s):

Baidar Bakht ◽

Paul F. Csagoly

Keyword(s):

Traffic Safety ◽

Carrying Capacity ◽

Point Of View ◽

Load Carrying Capacity ◽

Truss Bridges ◽

Load Carrying ◽

Live Loads ◽

Pros And Cons ◽

Pony Truss Bridge ◽

Truss Bridge

There are many thousands of existing pony truss bridges in North America which were constructed in the earlier part of this century and are still serving as important traffic carriers. The present economic situation demands that these bridges should usefully serve their purpose for as long as is safely possible.These bridges could be found inadequate for either or both of the following reasons. With the exception of remote areas, operational traffic safety would require two 12-ft lanes plus adequate shoulders. Many of these old bridges are therefore unsatisfactory from the geometrical point of view. Some bridges were designed for live loads that are only a fraction of present commercial vehicle weights.A computer-oriented method of rigorous analysis of lateral buckling behaviour of pony truss bridges is briefly discussed. The method is implemented through a computer program which has been validated by experimental data. It is expected that the program would predict realistic values of load-carrying capacity of such bridges and would help to avoid many an unnecessary replacement.Various methods of strengthening and widening pony truss bridges, and their pros and cons, are discussed. It is shown that the strengthening of a few components of a pony truss bridge does not always lead to an increase in the load-carrying capacity of the bridge.

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Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

Methods of Information in Medicine ◽

10.3414/me09-01-0054 ◽

2010 ◽

Vol 49 (03) ◽

pp. 230-237 ◽

Cited By ~ 44

Author(s):

K. Lweesy ◽

N. Khasawneh ◽

M. Fraiwan ◽

H. Wenz ◽

H. Dickhaus ◽

...

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Discriminant Analysis ◽

Sleep Stage ◽

Continuous Wavelet ◽

Eeg Signal ◽

Linear Discriminant ◽

Time Frequency ◽

Mother Wavelets ◽

Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

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

Sensors ◽

10.3390/s18092745 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2745 ◽

Cited By ~ 6

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.

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An Effective Damage Evaluation Method for Crane Girder Based on SVI and LSSVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.370 ◽

2014 ◽

Vol 602-605 ◽

pp. 370-374

Author(s):

Hong Bo Xu ◽

Jia Yu Li

Keyword(s):

Evaluation Method ◽

Damage Identification ◽

Health Assessment ◽

Support Vector ◽

Damage Evaluation ◽

Damage Level ◽

Variation Index ◽

Stiffness Variation ◽

Actual Damage

Health assessment of the girder is crucial to an overhead traveling crane. This paper presents an intelligent damage identification method for the girder based on stiffness variation index (SVI) and least squares support vector machine (LSSVM). In the method, the SVI indicators, which have high resolution to environmental noise, serve as the damage feature to detect damage locations. Moreover, the SVI indicators are input to the LSSVM classifier for identifying the actual damage level of the girder. A case study on girder damage identification demonstrates that the method could determine the actual conditions of the girder structure accurately.

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Ground Penetrating Radar for Belowground Phenotyping of High-biomass Grasses for Soil Carbon Sequestration

10.21203/rs.3.rs-1041672/v1 ◽

2021 ◽

Author(s):

Matthew Wolfe ◽

Da Huo ◽

Henry Ruiz-Guzman ◽

Brody Teare ◽

Tyler Adams ◽

...

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Carbon Capture ◽

Large Scale ◽

Agricultural Practices ◽

Signal Amplitude ◽

Belowground Biomass ◽

Continuous Wavelet ◽

Time Frequency ◽

Non Invasive

Abstract AimsMany governments and companies have committed to moving to net-zero emissions by 2030 or 2050 to tackle climate change, which require the development of new carbon capture and sequestration/storage (CCS) techniques. A proposed method of sequestration is to deposit carbon in soils as plant matter including root mass and root exudates. Adding perennial traits such as rhizomes to crops as part of a sequestration strategy would result in annual crop regrowth from rhizome meristems rather than requiring replanting from seeds which would in turn encourage no-till agricultural practices. Integrating these traits into productive agriculture requires a belowground phenotyping method compatible with high throughput breeding and selection methods (i.e., is rapid, inexpensive, reliable, and non-invasive), however none currently exist. MethodsGround penetrating radar (GPR) is a non-invasive subsurface sensing technology that shows potential as a phenotyping technique. In this study, a prototype GPR antenna array was used to scan roots of the perennial sorghum hybrid, PSH09TX15. A-scan level time-domain analyses and B-scan level time/frequency analyses using the continuous wavelet transform were utilized to extract features of interest from the acquired radargrams. ResultsOf six A-scan diagnostic indices examined, the standard deviation of signal amplitude correlated most significantly with belowground biomass. Time frequency analysis using the continuous wavelet transform yielded high correlations of B-scan features with belowground biomass. ConclusionThese results demonstrate that continued refinement of GPR data analysis workflows should yield a highly applicable phenotyping tool for breeding efforts in environments where selection is otherwise impractical on a large scale.

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