A Feasibility Study on Embedded Micro-Electromechanical Sensors and Systems (MEMS) for Monitoring Highway Structures, TR-575, 2011

Micro-electromechanical systems (MEMS) provide vast improvements over existing sensing methods in the context of structural health monitoring (SHM) of highway infrastructure systems, including improved system reliability, improved longevity and enhanced system performance, improved safety against natural hazards and vibrations, and a reduction in life cycle cost in both operating and maintaining the infrastructure. Advancements in MEMS technology and wireless sensor networks provide opportunities for long-term continuous, real-time structural health monitoring of pavements and bridges at low cost within the context of sustainable infrastructure systems. The primary objective of this research was to investigate the use of MEMS in highway structures for health monitoring purposes. This study focused on investigating the use of MEMS and their potential applications in concrete through a comprehensive literature review, a vendor survey, and a laboratory study, as well as a small-scale field study. Based on the comprehensive literature review and vendor survey, the latest information available on off-the-shelf MEMS devices, as well as research prototypes, for bridge, pavement, and traffic applications were synthesized. A commercially-available wireless concrete monitoring system based on radio-frequency identification (RFID) technology and off-the-shelf temperature and humidity sensors were tested under controlled laboratory and field conditions. The test results validated the ability of the RFID wireless concrete monitoring system in accurately measuring the temperature both inside the laboratory and in the field under severe weather conditions. In consultation with the project technical advisory committee (TAC), the most relevant MEMS-based transportation infrastructure research applications to explore in the future were also highlighted and summarized.

A Preliminary Review of Behavioural Biometrics for Health Monitoring in the Elderly

This article explores the potential of ICT-based biometrics for monitoring the health status of the elderly people. It departs from specific ageing and biometric traits to then focus on behavioural biometric traits like handwriting, speech and gait to finally explore their practical application in health monitoring of elderly.

Transducer Loading Effect on the Performance of PZT-Based SHM Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Optimal Frequency Range Selection for PZT Transducers in Impedance-Based SHM Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Experimental damage detection grammian matrix

Structural Health Monitoring (SHM) has diverse potential applications, and many groups work in the development of tools and techniques for monitoring structural performance. These systems use arrays of sensors and can be integrated with remote or local computers. There are several different approaches that can be used to obtain information about the existence, location and extension of faults by non destructive tests. In this paper an experimental technique is proposed for damage location based on an observability grammian matrix. The dynamic properties of the structure are identified through experimental data using the eigensystem realization algorithm (ERA). Experimental tests were carried out in a structure through varying the mass of some elements. Output signals were obtained using accelerometers.

Structural damage identification and location using grammian matrices

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modeling and Experimental Aspects of Self-healing Bolted Joint through Shape Memory Alloy Actuators

Bolted joints are a form of mechanical coupling largely used in machinery due to their reliability and low cost. Failure of bolted joints can lead to catastrophic events, such as leaking, train derailments, aircraft crashes, etc. Most of these failures occur due to the reduction of the pre-load, induced by mechanical vibration or human errors in the assembly or maintenance process. This article investigates the application of shape memory alloy (SMA) washers as an actuator to increase the pre-load on loosened bolted joints. The application of SMA washer follows a structural health monitoring procedure to identify a damage (reduction in pre-load) occurrence. In this article, a thermo-mechanical model is presented to predict the final pre-load achieved using this kind of actuator, based on the heat input and SMA washer dimension. This model extends and improves on the previous model of Ghorashi and Inman [2004, "Shape Memory Alloy in Tension and Compression and its Application as Clamping Force Actuator in a Bolted Joint: Part 2 - Modeling," J. Intell. Mater. Syst. Struct., 15:589-600], by eliminating the pre-load term related to nut turning making the system more practical. This complete model is a powerful but complex tool to be used by designers. A novel modeling approach for self-healing bolted joints based on curve fitting of experimental data is presented. The article concludes with an experimental application that leads to a change in joint assembly to increase the system reliability, by removing the ceramic washer component. Further research topics are also suggested.

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Experimental analysis of the effect of the structure area on the PZT-based SHM systems

The structural health monitoring (SHM) systems based on electromechanical (E/M) impedance technique have been widely investigated. Although many studies indicate the reliability of this technique, some practical considerations still have to be considered in real applications. This paper presents an experimental analysis of the effect of the structure area on the system's performance. The results indicate that the sensitivity of the system to detect damage decreases significantly when the host structure has large cross-section area. Copyright © 2009 by ASME.

A PZT-based technique for SHM using the coherence function

This paper presents a new approach for damage detection in structural health monitoring systems exploiting the coherence function between the signals from PZT (Lead Zirconate Titanate) transducers bonded to a host structure. The physical configuration of this new approach is similar to the configuration used in Lamb wave based methods, but the analysis and operation are different. A PZT excited by a signal with a wide frequency range acts as an actuator and others PZTs are used as sensors to receive the signal. The coherences between the signals from the PZT sensors are obtained and the standard deviation for each coherence function is computed. It is demonstrated through experimental results that the standard deviation of the coherence between the signals from the PZTs in healthy and damaged conditions is a very sensitive metric index to detect damage. Tests were carried out on an aluminum plate and the results show that the proposed methodology could be an excellent approach for structural health monitoring (SHM) applications.

Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

Damage Detection Based on Electromechanical Impedance Principle and Principal Components

This paper presents a novel time domain approach for Structural Health Monitoring (SHM) systems based on Electromechanical Impedance (EMI) principle and Principal Component Coefficients (PCC), also known as loadings. Differently of typical applications of EMI applied to SHM, which are based on computing the Frequency Response Function (FRF), in this work the procedure is based on the EMI principle but all analysis is conducted directly in time-domain. For this, the PCC are computed from the time response of PZT (Lead Zirconate Titanate) transducers bonded to the monitored structure, which act as actuator and sensor at the same time. The procedure is carried out exciting the PZT transducers using a wide band chirp signal and getting their time responses. The PCC are obtained in both healthy and damaged conditions and used to compute statistics indexes. Tests were carried out on an aircraft aluminum plate and the results have demonstrated the effectiveness of the proposed method making it an excellent approach for SHM applications. Finally, the results using EMI signals in both frequency and time responses are obtained and compared. © The Society for Experimental Mechanics 2014.

Leakage detection in water pipe networks using electromechanical impedance (EMI) based technique

The electromechanical impedance (EMI) technique has been successfully used in structural health monitoring (SHM) systems on a wide variety of structures. The basic concept of this technique is to monitor the structural integrity by exciting and sensing a piezoelectric transducer, usually a lead zirconate titanate (PZT) wafer bonded to the structure to be monitored and excited in a suitable frequency range. Because of the piezoelectric effect, there is a relationship between the mechanical impedance of the host structure, which is directly related to its integrity, and the electrical impedance of the PZT transducer, obtained by a ratio between the excitation and the sensing signals.This work presents a study on damage (leaks) detection using EMI based method. Tests were carried out in a rig water system built in a Hydraulic Laboratory for different leaks conditions in a metallic pipeline. Also, it was evaluated the influence of the PZT position bonded to the pipeline. The results show that leaks can effectively be detected using common metrics for damage detection such as RMSD and CCDM. Further, it was observed that the position of the PZT bonded to the pipes is an important variable and has to be controlled.