Time reversal technique for health monitoring of metallic structure using Lamb waves

Time reversal active sensing using Lamb waves is investigated for health monitoring of a metallic structure. Experiments were conducted on an aluminum plate to study the time reversal behavior of A(0) and S-0 Lamb wave modes under narrow band and broad band pulse excitation. Damage in the form of a notch was introduced in the plate to study the changes in the characteristics of the time reversed Lamb wave modes experimentally. Time-frequency analysis of the time reversed signal was carried out to extract the damage information. A measure of damage based on wavelet transform was derived to quantify the hidden damage information in the time reversed signal. It has been shown that time reversal can be used to achieve temporal recompression of Lamb waves under broadband signal excitation. Further, the broad band excitation can also improve the resolution of the technique in detecting closely located defects. This is demonstrated by picking up the reflection of waves from the edge of the plate, from a defect close to the edge of the plate and from defects located near to each other. This study shows the effectiveness of Lamb wave time reversal for temporal recompression of dispersive Lamb waves for damage detection in health monitoring applications. (C) 2009 Elsevier B.V. All rights reserved.

A novel interrogation system for fiber Bragg grating sensor array

Interrogation techniques for fiber Bragg grating sensor arrays need particular attention in the case of structural health monitoring applications involving dynamic strain measurement. Typically the performance of the sensing system is dependent on both the sensor type and the interrogation method employed. A novel interrogation system is proposed here that consists of different interrogation units for each sensor in the array, each unit comprising of a circulator, chirped grating and a Mach-Zehnder interferometer. We present an analysis that consists of tracking the spectral changes as the light passes through various elements in the interrogation system. This is expected to help in the optimization of sensor and interrogation elements leading to improved performance of the health monitoring system.

Ultrasonic performance of the PVDF thin film sensors under thermal fatigue

In the present work, the ultrasonic strain sensing performance of the large area PVDF thin film subjected to the thermal fatigue is studied. The PVDF thin film is prepared using hot press and the piezoelectric phase (beta-phase) has been achieved by thermo-mechanical treatment and poling under DC field. The sensors used in aircrafts for structural health monitoring applications are likely to be subjected to a wide range of temperature fluctuations which may create thermal fatigue in both aircraft structures and in the sensors. Thus, the sensitivity of the PVDF sensors for thermal fatigue needs to be studied for its effective implementation in the structural health monitoring applications. In present work, the fabricated films have been subjected to certain number of thermal cycles which serve as thermal fatigue and are further tested for ultrasonic strain sensitivity at various different frequencies. The PVDF sensor is bonded on the beam specimen at one end and the ultrasonic guided waves are launched with a piezoelectric wafer bonded on another end of the beam. Sensitivity of PVDF sensor in terms of voltage is obtained for increasing number of thermal cycles. Sensitivity variation is studied at various different extent of thermal fatigue. The variation of the sensor sensitivity with frequency due to thermal fatigue at different temperatures is also investigated. The present investigation shows an appropriate temperature range for the application of the PVDF sensors in structural health monitoring.

Energy Harvesting WSNs for Accurately Estimating the Maximum Sensor Reading: Trade-Offs and Optimal Design

Computing the maximum of sensor readings arises in several environmental, health, and industrial monitoring applications of wireless sensor networks (WSNs). We characterize the several novel design trade-offs that arise when green energy harvesting (EH) WSNs, which promise perpetual lifetimes, are deployed for this purpose. The nodes harvest renewable energy from the environment for communicating their readings to a fusion node, which then periodically estimates the maximum. For a randomized transmission schedule in which a pre-specified number of randomly selected nodes transmit in a sensor data collection round, we analyze the mean absolute error (MAE), which is defined as the mean of the absolute difference between the maximum and that estimated by the fusion node in each round. We optimize the transmit power and the number of scheduled nodes to minimize the MAE, both when the nodes have channel state information (CSI) and when they do not. Our results highlight how the optimal system operation depends on the EH rate, availability and cost of acquiring CSI, quantization, and size of the scheduled subset. Our analysis applies to a general class of sensor reading and EH random processes.

Spectral Finite Element Modeling of Complex Structures for Applications in Real-time Structural Health Monitoring

In this paper we discuss the recent progresses in spectral finite element modeling of complex structures and its application in real-time structural health monitoring system based on sensor-actuator network and near real-time computation of Damage Force Indicator (DFI) vector. A waveguide network formalism is developed by mapping the original variational problem into the variational problem involving product spaces of 1D waveguides. Numerical convergence is studied using a h()-refinement scheme, where is the wavelength of interest. Computational issues towards successful implementation of this method with SHM system are discussed.

Serum proteomics of glioma: methods and applications

The prognosis of patients with glioblastoma, the most malignant adult glial brain tumor, remains poor in spite of advances in treatment procedures, including surgical resection, irradiation and chemotherapy.Genetic heterogeneity of glioblastoma warrants extensive studies in order to gain a thorough understanding of the biology of this tumor. While there have been several studies of global transcript profiling of glioma with the identification of gene signatures for diagnosis and disease management, translation into clinics is yet to happen. Serum biomarkers have the potential to revolutionize the process of cancer diagnosis, grading, prognostication and treatment response monitoring. Besides having the advantage that serum can be obtained through a less invasive procedure, it contains molecules at an extraordinary dynamic range of ten orders of magnitude in terms of their concentrations. While the conventional methods, such as 2DE, have been in use for many years, the ability to identify the proteins through mass spectrometry techniques such as MALDI-TOF led to an explosion of interest in proteomics. Relatively new high-throughput proteomics methods such as SELDI-TOF and protein microarrays are expected to hasten the process of serum biomarker discovery. This review will highlight the recent advances in the proteomics platform in discovering serum biomarkers and the current status of glioma serum markers. We aim to provide the principles and potential of the latest proteomic approaches and their applications in the biomarker discovery process. Besides providing a comprehensive list of available serum biomarkers of glioma, we will also propose how these markers will revolutionize the clinical management of glioma patients.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Characterization of a large-area PVDF thin film for electro-mechanical and ultrasonic sensing applications

Large-area PVDF thin films have been prepared and characterized for quasi-static and high frequency dynamic strain sensing applications. These films are prepared using hot press method and the piezoelectric phase (beta-phase) has been achieved by thermo-mechanical treatment and poling under DC field. The fabricated films have been characterized for quasi-static strain sensing and the linear strain-voltage relationship obtained is promising. In order to evaluate the ultrasonic sensing properties, a PZT wafer has been used to launch Lamb waves in a metal beam on which the PVDF film sensor is bonded at a distance. The voltage signals obtained from the PVDF films have been compared with another PZT wafer sensor placed on the opposite surface of the beam as a reference signal. Due to higher stiffness and higher thickness of the PZT wafer sensors, certain resonance patterns significantly degrade the sensor sensitivity curves. Whereas, the present results show that the large-area PVDF sensors can be superior with the signal amplitude comparable to that of PZT sensors and with no resonance-induced effect, which is due to low mechanical impedance, smaller thickness and larger area of the PVDF film. Moreover, the developed PVDF sensors are able to capture both A(0) and S-0 modes of Lamb wave, whereas the PZT sensors captures only A(0) mode in the same scale of voltage output. This shows promises in using large-area PVDF films with various surface patterns on structures for distributed sensing and structural health monitoring under quasi-static, vibration and ultrasonic situations. (C) 2010 Elsevier B.V. All rights reserved.

Fiber Bragg Grating Sensors for Real Time Monitoring of Evacuation Process

Fiber bragg grating (FBG) sensors have been widely used for number of sensing applications like temperature, pressure, acousto-ultrasonic, static and dynamic strain, refractive index change measurements and so on. Present work demonstrates the use of FBG sensors in in-situ measurement of vacuum process with simultaneous leak detection capability. Experiments were conducted in a bell jar vacuum chamber facilitated with conventional Pirani gauge for vacuum measurement. Three different experiments have been conducted to validate the performance of FBG sensor in monitoring vacuum creating process and air bleeding. The preliminary results of FBG sensors in vacuum monitoring have been compared with that of commercial Pirani gauge sensor. This novel technique offers a simple alternative to conventional method for real time monitoring of evacuation process. Proposed FBG based vacuum sensor has potential applications in vacuum systems involving hazardous environment such as chemical and gas plants, automobile industries, aeronautical establishments and leak monitoring in process industries, where the electrical or MEMS based sensors are prone to explosion and corrosion.

Mixed ionic and electronic conduction in zirconia and its applications in metallurgy

Mixed ionic and electronic conduction in Zr02-based solid electrolytes was studied.The effect of impurities and second-phase particles on the mixed conduction parameter, P,, was measured for different types of ZrOZ electrolytes. The performance of solid-state sensors incorporating ZrOZ electrolytes is sometimes limited by electronic conduction in ZrOZ, especially at temperatures >I800 K. Methods for eliminating or minimizing errors in measured emf due to electronically driven transport of oxygen anions are discussed. Examples include probes for monitoring oxygen content in liquid steel as well as the newly developed sulfur sensor based on a ZrOz(Ca0) + CaS electrolyte. The use of mixed conducting ZrOZ as a semipermeable membrane or chemically selective sieve for oxygen at high temperatures is discussed. Oxygen transport from liquid iron to CO + C& gas mixtures through a ZrOZ membrane driven by a chemical potential gradient, in the absence of electrical leads or imposed potentials, was experimentally observed.

Applications of solid electrolytes in galvanic sensors

The concepts and theoretical origins of conduction domains for solid electrolytes and electrode polarization are outlined briefly. The point electrode made of the ' solid electrolyte material is useful for deflecting the semipermeability flux away from the electrode. The emf of galvanic sensors consisting of two solid electrolytes in intimate contact with each other and in which transport occurs by a common ion is reviewed. The voltage of such cells depends on the chemical potential of the active species at the interface between the two electrolytes, which can be evaluated from the transport properties of electrolytes using a numerical procedure. The factors governing the speed of response of solid electrolyte gas sensors are analyzed. A rigorous expression for the emf of non-isothermal galvanic sensors and the criterion for the design of temperature compensated reference electrodes for nonisothermal galvanic sensors are outlined. Non-isothermal sensors are useful for the continuous monitoring of concentrations or chemical potentials in reactive systems at high temperatures. The principles of operation of galvanic sensors for oxygen, sulphur, oxides of sulphur (SOx,x=2,3), carbon, oxides uf carbon (COx,x= 1,2), oxides of nitrogen (NOx,x= 1,2) and silicon are discussed. The use of auxiliary electrodes in galvanic sensors to expand the detection capability of known solid electrolytes to a large number of species is explained with reference to sensors for sulphur and oxides of sulphur (SOx,x=2,3).Finally the cause of the common errors in galvanic measurements and test for the correct functioning of galvanic sensors is given.

Resource Usage Monitoring in Clouds

Monitoring of infrastructural resources in clouds plays a crucial role in providing application guarantees like performance, availability, and security. Monitoring is crucial from two perspectives - the cloud-user and the service provider. The cloud user’s interest is in doing an analysis to arrive at appropriate Service-level agreement (SLA) demands and the cloud provider’s interest is to assess if the demand can be met. To support this, a monitoring framework is necessary particularly since cloud hosts are subject to varying load conditions. To illustrate the importance of such a framework, we choose the example of performance being the Quality of Service (QoS) requirement and show how inappropriate provisioning of resources may lead to unexpected performance bottlenecks. We evaluate existing monitoring frameworks to bring out the motivation for building much more powerful monitoring frameworks. We then propose a distributed monitoring framework, which enables fine grained monitoring for applications and demonstrate with a prototype system implementation for typical use cases.

Mobile agent based node monitoring protocol for MANETs

Mobile ad-hoc network is a wireless ad-hoc network with dynamic network topology. The Dynamicity, due to the random node movement, and scarcity of resources lead to a challenge in monitoring the nodes in a MANET. Monitoring the lack of resources (bandwidth, buffer, and energy), misbehavior, and mobility at node level remains, a challenge. In a MANET the proposed protocol uses both static as well as mobile agents, where the mobile agents migrate to different clusters of the zones respectively, collect the node status information periodically, and provide a high level information to the static agent (which resides at the central node) by analyzing the raw information at the nodes. This, in turn, reduces the network traffic and conserves the workload of the central node, where a static agent is available with high level information and in coordination with other modules. The protocol has been tested in different size MANETs with variable number of nodes and applications. The results shown in the simulation indicates the effectiveness of the protocol.

Remote sensing applications in water resources

With the introduction of the earth observing satellites, remote sensing has become an important tool in analyzing the Earth's surface characteristics, and hence in supplying valuable information necessary for the hydrologic analysis. Due to their capability to capture the spatial variations in the hydro-meteorological variables and frequent temporal resolution sufficient to represent the dynamics of the hydrologic processes, remote sensing techniques have significantly changed the water resources assessment and management methodologies. Remote sensing techniques have been widely used to delineate the surface water bodies, estimate meteorological variables like temperature and precipitation, estimate hydrological state variables like soil moisture and land surface characteristics, and to estimate fluxes such as evapotranspiration. Today, near-real time monitoring of flood, drought events, and irrigation management are possible with the help of high resolution satellite data. This paper gives a brief overview of the potential applications of remote sensing in water resources.