scholarly journals Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 545 ◽  
Author(s):  
Xinlin Qing ◽  
Wenzhuo Li ◽  
Yishou Wang ◽  
Hu Sun
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Transducer ◽  
Piezoelectric Materials ◽  
Development Trend ◽  
Practical Implementation ◽  
Damage State ◽  
Structural Health ◽  
Safety And Reliability ◽  
Aircraft Application

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

scholarly journals Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

2021 ◽  
Vol 11 (12) ◽  
pp. 5727
Author(s):  
Sifat Muin ◽  
Khalid M. Mosalam
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degree Of Freedom ◽  
Absolute Velocity ◽  
Support Vector ◽  
Damage State ◽  
Structural Health ◽  
Cumulative Absolute Velocity

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

scholarly journals Advances in the Structural Health Monitoring of Bridges Using Piezoelectric Transducers

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4312 ◽  
Author(s):  
Yunzhu Chen ◽  
Xingwei Xue
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Materials ◽  
Low Cost ◽  
Rapid Development ◽  
Concrete Strength ◽  
Steel Corrosion ◽  
Fast Response ◽  
Future Application ◽  
Structural Health

With the rapid development of the world’s transportation infrastructure, many long-span bridges were constructed in recent years, especially in China. However, these bridges are easily subjected to various damages due to dynamic loads (such as wind-, earthquake-, and vehicle-induced vibration) or environmental factors (such as corrosion). Therefore, structural health monitoring (SHM) is vital to guarantee the safety of bridges in their service lives. With its wide frequency response range, fast response, simple preparation process, ease of processing, low cost, and other advantages, the piezoelectric transducer is commonly employed for the SHM of bridges. This paper summarizes the application of piezoelectric materials for the SHM of bridges, including the monitoring of the concrete strength, bolt looseness, steel corrosion, and grouting density. For each problem, the application of piezoelectric materials in different research methods is described. The related data processing methods for four types of bridge detection are briefly summarized, and the principles of each method in practical application are listed. Finally, issues to be studied when using piezoelectric materials for monitoring are discussed, and future application prospects and development directions are presented.

scholarly journals Toward the development of standardized procedures for structural health monitoring

2019 ◽  
Author(s):  
Maria Pina Limongelli
Keyword(s):  
Structural Health Monitoring ◽  
Nondestructive Testing ◽  
Health Monitoring ◽  
Structural Response ◽  
Health Conditions ◽  
Practical Implementation ◽  
Civil Structures ◽  
Structural Health ◽  
The World ◽  
Limited Diffusion

<p>Monitoring of structural health conditions is performed using different methods that range from periodic surveys including nondestructive testing at selected locations, to permanent monitoring using network of sensors continuously recording the structural response. These procedures aim at providing detection of possible faults or deterioration processes in order to optimally manage civil structures and infrastructures over the lifecycle. To date several guidelines have been published by different countries all over the world but protocols to apply SHM are generally not defined nor enforced. This is likely to be of the reasons that stand behind the limited diffusion and implementation of SHM for routine operations of condition assessment. In this paper building the principal aspects of the SHM process are presented and the need of the development of protocols for the different phases of the SHM process, from design to practical implementation and use are outlined.</p>

scholarly journals Value of Information of Structural Health Monitoring in Asset Management of Flood Defences

2019 ◽  
Vol 4 (3) ◽  
pp. 56 ◽  
Author(s):  
Wouter Jan Klerk ◽  
Timo Schweckendiek ◽  
Frank den Heijer ◽  
Matthijs Kok
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Asset Management ◽  
Value Of Information ◽  
Relative Magnitude ◽  
Practical Implementation ◽  
Structural Health ◽  
Monitoring Strategies ◽  
Efficient Measure ◽  
Flood Defence

One of the most rapidly emerging measures in infrastructure asset management is Structural Health Monitoring (SHM), which aims at reducing uncertainty in structural performance by using monitoring equipment. As earthen flood defence structures typically have large strength uncertainties, such techniques can be particularly promising. However, insight in the key characteristics for successful SHM for flood defences is lacking, which hampers the practical implementation. In this study, we explore the benefits of pore pressure monitoring, one of the most promising SHM techniques for earthen flood defences. The approach is based on a Bayesian pre-posterior analysis, and results are evaluated based on the Value of Information (VoI) obtained from different monitoring strategies. We specifically investigate the effect on long-term reinforcement decisions. The results show that, next to the relative magnitude of reducible uncertainty, the combination of the probability of having a useful observation and the duration of a SHM effort determine the VoI. As it is likely that increasing loads due to climate change will result in more frequent future reinforcements, the influence of scenarios of different rates of increase in future loads is also investigated. It was found that, in all considered possible scenarios, monitoring yields a positive Value of Information, hence it is an economically efficient measure for flood defence asset management both now and in the future.

scholarly journals Piezoelectric Sensing Techniques in Structural Health Monitoring: A State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3730 ◽  
Author(s):  
Pengcheng Jiao ◽  
King-James I. Egbe ◽  
Yiwei Xie ◽  
Ali Matin Nazar ◽  
Amir H. Alavi
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Smart Materials ◽  
State Of The Art ◽  
Piezoelectric Materials ◽  
Great Promise ◽  
Monitoring Systems ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Health Monitoring Systems

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.

Piezoelectric Transducer Diagnostics via Linear Reciprocity for Guided Wave Structural Health Monitoring

AIAA Journal ◽  
2011 ◽  
Vol 49 (3) ◽  
pp. 621-629 ◽  
Author(s):  
Sang Jun Lee ◽  
Jennifer E. Michaels ◽  
Hoon Sohn ◽  
Thomas E. Michaels
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Transducer ◽  
Guided Wave ◽  
Structural Health

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

Electromechanical Impedance Modeling for Structural Health Monitoring

2012 ◽  
Author(s):  
Liuxian Zhao ◽  
Lingyu Yu ◽  
Mattieu Gresil ◽  
Michael Sutton ◽  
Siming Guo
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Electrical Impedance ◽  
Piezoelectric Materials ◽  
Fiber Composite ◽  
Mechanical Impedance ◽  
Experimental Result ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Aluminum Beam

Electromechanical impedance (EMI) method is an effective and powerful technique in structural health monitoring (SHM) which couples the mechanical impedance of host structure with the electrical impedance measured at the piezoelectric wafer active sensor (PWAS) transducer terminals. Due to the electromechanical coupling in piezoelectric materials, changes in structural mechanical impedance are reflected in the electrical impedance measured at the PWAS. Therefore, the structural mechanical resonances are reflected in a virtually identical spectrum of peaks and valleys in the real part of the measured EMI. Multi-physics based finite element method (MP-FEM) has been widely used for the analysis of piezoelectric materials and structures. It uses finite elements taking both electrical and mechanical DOF’s into consideration, which allows good differentiation of complicated structural geometries and damaged areas. In this paper, MP-FEM was then used to simulate PWAS EMI for the goal of SHM. EMI of free PWAS was first simulated and compared with experimental result. Then the constrained PWAS was studied. EMI of both metallic and glass fiber composite materials were simulated. The first case is the constrained PWAS on aluminum beam with various dimensions. The second case studies the sensitivity range of the EMI approach for damage detection on aluminum beam using a set of specimens with cracks at different locations. In the third case, structural damping effects were also studied in this paper.. Our results have also shown that the imaginary part of the impedance and admittance can be used for sensor self-diagnosis.

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