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CONTENTS
Volume 6, Number 4, May 2010
 

Abstract
Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defect-sensitive features to perform a multivariate diagnosis of damage. This diagnosis is based on the PCA. The framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a PXI platform that controls the generation and measurement of the ultrasonic signals by means of piezoelectric transducers made of Lead Zirconate Titanate. Although the approach is demonstrated in a beam test, it is argued that the proposed method is general and applicable to any structure that can sustain the propagation of UGWs.

Key Words
ultrasonic guided waves; principal component analysis; fatigue crack detection; structural health monitoring.

Address
Marcello Cammarata; Laboratory for NDE and Structural Health Monitoring studies, Department of Civil and Environmental Engineering, University of Pittsburgh, 963 Benedum Hall, 3700 OHara Street, Pittsburgh, PA 15261, USA
Piervincenzo Rizzo; Department of Civil and Environmental Engineering, University of Pittsburgh, 949 Benedum Hall, 3700 OHara Street, Pittsburgh, PA 15261, USA
Debaditya Dutta; Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Hoon Sohn; Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea

Abstract
Accurate peak response estimation of a seismically excited structure with frictional damping system (FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated the peak response of the structure with FDS by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In case that earthquake excitation is defined probabilistically, corresponding response of the structure with FDS becomes to have probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake excitation generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Coefficients of the proposed PDF are obtained by regression of the statistical distribution of the time history responses. Finally, the correlation between the resulting PDFs and statistical response distribution is investigated.

Key Words
frictional damping system; probabilistic density function; nonlinear system analysis; estimation of peak displacement.

Address
S.H. Lee, K.J. Youn and K.W. Min: Department of Architectural Engineering, Dankook University, Seoul, South Korea
J.H. Park: Department of Architectural Engineering, University of Incheon, Incheon, South Korea

Abstract
The updated finite element model of K??han Highway Bridge on the Flrat River located on the 51st km of Elazl-Malatya highway is obtained by using analytical and experimental results. The 2D and 3D finite element model of the bridge is created by using SAP2000 structural analyses software, and the dynamic characteristics of the bridge are determined analytically. The experimental measurements are carried out by Operational Modal Analysis Method under traffic induced vibrations and the dynamic characteristics are obtained experimentally. The vibration data are gathered from the both box girder and the deck of the bridge, separately. Due to the expansion joint in the middle of the bridge, special measurement points are selected when experimental test setups constitute. Measurement duration, frequency span and effective mode number are determined by considering similar studies in literature. The Peak Picking method in the frequency domain is used in the modal identification. At the end of the study, analytical and experimental dynamic characteristic are compared with each other and the finite element model of the bridge is updated by changing some uncertain parameters such as material properties and boundary conditions. Maximum differences between the natural frequencies are reduced from 10% to 2%, and a good agreement is found between natural frequencies and mode shapes after model updating.

Key Words
ambient vibration; finite element model updating; highway bridge; operational modal analysis; peak picking method.

Address
Alemdar Bayraktar, Ahmet Can Altunisik, Baris Sevim and Temel Turker: Department of Civil Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

Abstract
By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.

Key Words
rotating machinery; fault diagnosis; image recognition technology; artificial immune algorithm.

Address
Zhu Dachang, Feng Yanping and Chen Qiang: College of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, P.R. China
Cai Jinbao: Faculty of Foreign Studies, Jiangxi University of Science and Technology, Ganzhou, P.R. China

Abstract
This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

Key Words
pipeline; possible hazard; principal component analysis; one-class support vector machines; standardization.

Address
Chunfeng Wan: International Institute for Urban Systems Engineering, School of Civil Engineering, Southeast University, Nanjing 210096, China
Akira Mita: System Design Department, Keio University, Hiyoshi, Yokohama 223-8522, Japan


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