Wavelet-based feature extraction for automatic defect classification in strands by ultrasonic structural monitoring

Rizzo, Piervincenzo,Lanza di Scalea, Francesco Techno-Press 2006 Smart Structures and Systems, An International Jou Vol.2 No.3

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.

A review on the latest advancements in the non-invasive evaluation/monitoring of dental and trans-femoral implants

Piervincenzo Rizzo 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.1

Dental implants and transcutaneous prostheses (trans-femoral implants) improve the quality of life of millions of peoplebecause they represent the optimal treatments to edentulism and amputation, respectively. The clinical procedures adoptedby surgeons to insert these implants are well established. However, there is uncertainty on the outcomes of the post-operationrecovery because of the uncertainty associated with the osseointegration process, which is defi ned as the direct, structural andfunctional contact between the living bone and the fi xture. To guarantee the long-term survivability of dental or trans-femoralimplants doctors sometimes implement non-invasive techniques to monitor and evaluate the progress of osseointegration. This may be done by measuring the stability of the fi xture or by assessing the quality of the bone-fi xture interface. In addition,care providers may need to quantify the structural integrity of the bone-implant system at various moments during thepatients recovery. The accuracy of such non-invasive methods reduce recovery and rehabilitation time, and may increasethe survival rate of the therapies with undisputable benefi ts for the patients. This paper provides a comprehensive review ofclinically-approved and emerging non-invasive methods to evaluate/monitor the osseointegration of dental and orthopedicimplants. A discussion about advantages and limitations of each method is provided based on the outcomes of the casespresented. The review on the emerging technologies covers the developments of the last decade, while the discussion aboutthe clinically approved systems focuses mostly on the latest (2017–2018) fi ndings. At last, the review also provides somesuggestions for future researches and developments in the area of implant monitoring.

Bridge health monitoring in the United States: A review

Rizzo, Piervincenzo,Enshaeian, Alireza Techno-Press 2021 Structural monitoring and maintenance Vol.8 No.1

The assessment of bridges' health has become a relevant component of the maintenance paradigm especially in those countries in which many structures are rated in poor condition and/or are over 50 years old. Additionally, the permanent monitoring of bridges helps engineers in validating the design prediction of bridge structural response to external loads. With more than 600,000 highway bridges, 46.4% of which rated as fair and 7.6% rated in poor condition, United States is one of those countries in which the installation of reliable bridge health monitoring systems is strategically necessary to minimize and optimize repair and rehabilitation costs and to minimize the risk of failures. In this paper, a thorough review of the scientific literature on structural health monitoring systems installed in U.S. bridges over the last 20 years is presented. This review aims to offer interested readers a holistic perspective of recent and current state-of-the-art bridge health monitoring systems and to extract a "general paradigm" that is common to many real structures. The review, conducted through a comprehensive search of peer-reviewed documents available in the scientific literature, discusses more than sixty bridges in terms of the instrumentation used, scope of the monitoring, and main outcomes. Overall, it was found that the monitoring systems provide a valuable tool to compare the structural responses predicted using analytical or numerical tools with the real response of the given structures. Owing to the relative short time span of the monitoring period, most of the monitoring systems did not flag any serious structural flaws that required the closure of the bridge monitored.

Application of principal component analysis and wavelet transform to fatigue crack detection in waveguides

Marcello Cammarata,Piervincenzo Rizzo,Debaditya Dutta,손훈 국제구조공학회 2010 Smart Structures and Systems, An International Jou Vol.6 No.4

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.

Application of principal component analysis and wavelet transform to fatigue crack detection in waveguides

Cammarata, Marcello,Rizzo, Piervincenzo,Dutta, Debaditya,Sohn, Hoon Techno-Press 2010 Smart Structures and Systems, An International Jou Vol.6 No.4

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.

Wavelet-based feature extraction for automatic defect classification in strands by ultrasonic structural monitoring

Francesco Lanza di Scalea,Piervincenzo Rizzo 국제구조공학회 2006 Smart Structures and Systems, An International Jou Vol.2 No.3

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multi-dimensional analysis can provide excellent classification performance for notch-type defects in strands.

A Nonlinear Acoustic Technique for Crack Detection in Metallic Structures

Dutta, Debaditya,Sohn, Hoon,Harries, Kent A.,Rizzo, Piervincenzo SAGE Publications 2009 Structural health monitoring Vol.8 No.3

<P>A crack detection technique based on nonlinear acoustics is investigated in this study. Acoustic waves at a chosen frequency are generated using an actuating lead zirconate titanate (PZT) transducer, and they travel through the target structure before being received by a sensing PZT wafer. Unlike an undamaged medium, a cracked medium exhibits high acoustic nonlinearity which is manifested as harmonics in the power spectrum of the received signal. Experimental results also indicate that the harmonic components increase nonlinearly in magnitude with increasing amplitude of the input signal. The proposed technique identifies the presence of cracks by looking at the two aforementioned features: harmonics and their nonlinear relationship to the input amplitude. The effectiveness of the technique has been tested on aluminum and steel specimens. The behavior of these nonlinear features as crack propagates in the steel beam has also been studied.</P>

Indirect structural health monitoring of a simplified laboratory-scale bridge model

Fernando Cerda,Siheng Chen,Jacobo Bielak,James H. Garrett,Piervincenzo Rizzo,Jelena Kovačević 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.13 No.5

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

Indirect structural health monitoring of a simplified laboratory-scale bridge model

Cerda, Fernando,Chen, Siheng,Bielak, Jacobo,Garrett, James H.,Rizzo, Piervincenzo,Kovacevic, Jelena Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.13 No.5

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.