Showing papers by "Charles R. Farrar published in 2010"

Energy Harvesting and Wireless Energy Transmission for Embedded SHM Sensor Nodes

Abstract: In this article, we present experimental investigations using energy harvesting and wireless energy transmission to power wireless structural health monitoring sensor nodes. The goal of this study is to develop sensing systems that can be permanently embedded within a host structure without the need for an on-board power source. With this approach the required energy will be harvested from the ambient environment, or periodically delivered by a radio-frequency energy source to supplement conventional harvesting approaches. This approach combines several transducer types to harvest energy from multiple sources, providing a more robust solution that does not rely on a single energy source. Both piezoelectric and thermoelectric transducers are considered as energy harvesters to extract the ambient energy commonly available on civil structures such as bridges. Methods of increasing the efficiency, energy storage medium, target applications and the integrated use of energy harvesting sources with wireless ener...

Multi-scale wireless sensor node for health monitoring of civil infrastructure and mechanical systems

Abstract: This paper presents recent developments in an extremely compact, wireless impedance sensor node (the WID3, Wireless Impedance Device) for use in high-frequency impedance-based structural health monitoring (SHM), sensor diagnostics and validation, and low-frequency (< ~1 kHz) vibration data acquisition The WID3 is equipped with an impedance chip that can resolve measurements up to 100 kHz, a frequency range ideal for many SHM applications An integrated set of multiplexers allows the end user to monitor seven piezoelectric sensors from a single sensor node The WID3 combines on-board processing using a microcontroller, data storage using flash memory, wireless communications capabilities, and a series of internal and external triggering options into a single package to realize a truly comprehensive, self-contained wireless active-sensor node for SHM applications Furthermore, we recently extended the capability of this device by implementing low-frequency analog-to-digital and digital-to-analog converters so that the same device can measure structural vibration data The compact sensor node collects relatively low-frequency acceleration measurements to estimate natural frequencies and operational deflection shapes, as well as relatively high-frequency impedance measurements to detect structural damage Experimental results with application to SHM, sensor diagnostics and low-frequency vibration data acquisition are presented

A structural decomposition approach for detecting, locating, and quantifying nonlinearities in chain‐like systems

Abstract: Experimental data from a test-bed structure tested at the Los Alamos National Laboratory are used to evaluate the effectiveness and reliability of a data-driven non-parametric technique to identify nonlinearities in uncertain MDOF chain-like systems. The results of this study showed that the proposed approach was able, in a stochastic framework, to confidently detect the presence of nonlinearities, accurately locate the structural section where the nonlinear effects were observed, and provide an estimate of the severity of the nonlinearity. Copyright © 2010 John Wiley & Sons, Ltd.

A Bayesian experimental design approach to structural health monitoring

Abstract: Optimal system design for SHM involves two primarily challenges The first is the derivation of a proper performance function for a given system design The second is the development of an efficient optimization algorithm for choosing a design that maximizes, or nearly maximizes the performance function In this paper we will outline how an SHM practitioner can construct the proper performance function by casting the entire design problem into a framework of Bayesian experimental design The approach demonstrates how the design problem necessarily ties together all steps of the SHM process

Complexity: A New Axiom for Structural Health Monitoring?

Abstract: : The basic purpose of the paper is simple; having proposed a set of axioms or "basic truths" regarding Structural Health Monitoring (SHM) in a previous paper, the authors would like to extend the set by the proposal for a new axiom. This axiom relates to an observation that the presence of damage in a structure or system usually results in increased complexity of measured responses or features. It is argued that this observation could lead to principled means of selecting effective features for SHM.

SHMTools: a new embeddable software package for SHM applications

Abstract: This paper describes a new software package, SHMTools, for prototyping algorithms for various structural health monitoring (SHM) applications. The software includes a set of standardized MATLAB routines covering three main stages of SHM: data acquisition, feature extraction, and feature classification for damage identification. A subset of the software in SHMTools is embeddable, which consists of Matlab functions that can be cross-compiled into generic "C" programs to be run on a target hardware. The software is designed to accommodate multiple sensing modalities, including piezoelectric active-sensing, which have become widely used in SHM practice. The software package, standardized datasets, and detailed documentation are publicly available for use by the SHM community. The details of this software will be discussed, along with several example processes to demonstrate its utility.

SHM of wind turbine blades using piezoelectric active-sensors

Abstract: This paper presents a variety of structural health monitoring (SHM) techniques, based on the use of piezoelectric active-sensors, used to determine the structural integrity of wind turbine blades. Specifically, Lamb wave propagations, frequency response functions, and time series based methods are utilized to estimate the condition of wind turbine blades. For experiments, a 1m section of a 9m CX100 blade is used. Overall, these three methods yielded a sufficient damage detection capability to warrant further investigation into field deployment. A full-scale fatigue test of a CX-100 wind turbine blade is also conducted. This paper summarizes considerations needed to design such SHM systems, experimental procedures and results, and practical implementation issues that can be used as guidelines for future investigations.

Time series predictive models of piezoelectric active-sensing for SHM applications

Abstract: In this paper, the use of time domain data from piezoelectric active-sensing techniques is investigated for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used in SHM because of their proven advantages. Especially, the use of known and repeatable inputs at high frequency ranges makes the development of SHM signal processing algorithm easier and more efficient. However, to date, most of these techniques have been based on frequency domain analyses, such as impedance-based or high-frequency response functions (FRF) -based SHM techniques. Even with Lamb wave propagations, most researchers adopt frequency domain or wavelets analysis for damage-sensitive feature extraction. This process usually requires excessive averaging to reduce measurement noise and more computational resources, which is not ideal from both memory and power consumption standpoints. Therefore in this study, we investigate the use of autoregressive models with exogenous inputs (ARX) with the measured time series data from piezoelectric active-sensors. The test structure considered in this study is a composite plate, where several damage conditions were manually imposed. The performance of this technique is compared to that of traditional autoregressive models, traditionally used in low-frequency passive sensing SHM applications, and that of FRF-based analysis, and its superior capability for SHM is demonstrated.

Machine learning algorithms to damage detection under operational and environmental variability

Abstract: The goal of this paper is to detect structural damage in the presence of operational and environmental variations using vibration-based damage identification procedures. For this purpose, four machine learning algorithms are applied based on auto-associative neural networks, factor analysis, Mahalanobis distance, and singular value decomposition. A baseexcited three-story frame structure was tested in laboratory environment to obtain time series data from an array of sensors under several structural state conditions. Tests were performed with varying stiffness and mass conditions with the assumption that these sources of variability are representative of changing operational and environmental conditions. Damage was simulated through nonlinear effects introduced by a bumper mechanism that induces a repetitive, impacttype nonlinearity. This mechanism intends to simulate the cracks that open and close under dynamic loads or loose connections that rattle. The unique contribution of this study is a direct comparison of the four proposed machine learning algorithms that have been reported as reliable approaches to separate structural conditions with changes resulting from damage from changes caused by operational and environmental variations.

Wireless energy transmission to supplement energy harvesters in sensor network applications

Abstract: In this paper we present a method for coupling wireless energy transmission with traditional energy harvesting techniques in order to power sensor nodes for structural health monitoring applications. The goal of this study is to develop a system that can be permanently embedded within civil structures without the need for on-board power sources. Wireless energy transmission is included to supplement energy harvesting techniques that rely on ambient or environmental, energy sources. This approach combines several transducer types that harvest ambient energy with wireless transmission sources, providing a robust solution that does not rely on a single energy source. Experimental results from laboratory and field experiments are presented to address duty cycle limitations of conventional energy harvesting techniques, and the advantages gained by incorporating a wireless energy transmission subsystem. Methods of increasing the efficiency, energy storage medium, target applications and the integrated use of energy harvesting sources with wireless energy transmission will be discussed.

Nondestructive Evaluation of Structures

Abstract: The process of implementing a damage identification strategy for aerospace systems is referred to as structural health monitoring (SHM). Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's current or future performance. A wide variety of highly effective local non-destructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis and in a more autonomous manner. Recent research has begun to recognize that the SHM problem is fundamentally one of statistical pattern recognition, and a strategy to address this problem as it applies to aerospace structures is described herein. To conclude this discussion, technical challenges that must be addressed if SHM is to gain wider application in the aerospace industry are summarized. Keywords: damage detection; data normalization; feature extraction; health and usage monitoring; operational evaluation; pattern recognition; sensors; dtatistical classification; structural health monitoring; pattern recognition

Autoregressive modeling with state-space embedding vectors for damage detection under operational and environmental variability

Abstract: A nonlinear time series approach is presented to detect damage in systems by using a state-space reconstruction to infer the geometrical structure of a deterministic dynamical system from observed time series response at multiple locations. The unique contribution of this approach is using a Multivariate Autoregressive (MAR) model of a baseline condition to predict the state space, where the model encodes the embedding vectors rather than scalar time series. A hypothesis test is established that the MAR model will fail to predict future response if damage is present in the test condition, and this test is investigated for robustness in the context of operational and environmental variability. The applicability of this approach is demonstrated using acceleration time series from a base-excited 3-story frame structure.

Monitoring of bolted joints using piezoelectric active-sensing for aerospace applications

Abstract: This paper is a report of an initial investigation into tracking and monitoring the integrity of bolted joints using piezoelectric active-sensors. The target application of this study is a fitting lug assembly of unmanned aerial vehicles (UAVs), where a composite wing is mounted to a UAV fuselage. The SHM methods deployed in this study are impedance-based SHM techniques, time-series analysis, and high-frequency response functions measured by piezoelectric active-sensors. Different types of simulated damage are introduced into the structure, and the capability of each technique is examined and compared. Additional considerations encountered in this initial investigation are made to guide further thorough research required for the successful field deployment of this technology.

Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation

Abstract: This paper gives a brief overview of a new project at LANL in structural damage identification for wind turbines. This project makes use of modeling capabilities and sensing technology to understand realistic blade loading on large turbine blades, with the goal of developing the technology needed to automatically detect early damage. Several structural health monitoring (SHM) techniques using piezoelectric active materials are being investigated for the development of wireless, low power sensors that interrogate sections of the wind turbine blade using Lamb wave propagation data, frequency response functions (FRFs), and time-series analysis methods. The modeling and sensor research will be compared with extensive experimental testing, including wind tunnel experiments, load and fatigue tests, and ultrasonic scans - on small- to mid-scale turbine blades. Furthermore, this study will investigate the effect of local damage on the global response of the blade by monitoring low-frequency response changes.

Multi-source energy harvester to power sensing hardware on rotating structures

Abstract: The U.S. Department of Energy (DOE) proposes to meet 20% of the nation's energy needs through wind power by the year 2030. To accomplish this goal, the industry will need to produce larger (>100m diameter) turbines to increase efficiency and maximize energy production. It will be imperative to instrument the large composite structures with onboard sensing to provide structural health monitoring capabilities to understand the global response and integrity of these systems as they age. A critical component in the deployment of such a system will be a robust power source that can operate for the lifespan of the wind turbine. In this paper we consider the use of discrete, localized power sources that derive energy from the ambient (solar, thermal) or operational (kinetic) environment. This approach will rely on a multi-source configuration that scavenges energy from photovoltaic and piezoelectric transducers. Each harvester is first characterized individually in the laboratory and then they are combined through a multi-source power conditioner that is designed to combine the output of each harvester in series to power a small wireless sensor node that has active-sensing capabilities. The advantages/disadvantages of each approach are discussed, along with the proposed design for a field ready energy harvester that will be deployed on a small-scale 19.8m diameter wind turbine.

Impedance‐Based Structural Health Monitoring for Aerospace Applications

Abstract: Various experimental studies have demonstrated that an impedance-based approach to structural health monitoring (SHM) can be an effective means of structural damage detection. Using the self-sensing and active-sensing capabilities of piezoelectric materials, the electromechanical impedance response can be monitored to provide a qualitative indication of the local health of a structure. The impedance method also has applications in the field of sensor self-diagnostics and validation for determining the operational status of piezoelectric active sensors used in SHM. This chapter presents an overview and recent applications of the impedance-based structural health-monitoring technique. Keywords: damage detection; piezoelectric transducers; sensor validation; impedance method; structural health monitoring; wave propagations

SHMTools: a general-purpose software tool for SHM applications

Abstract: This paper describes a new software package for various structural health monitoring (SHM) applications. The software is a set of standardized MATLAB routines covering three main stages of SHM: data acquisition, feature extraction, and feature classification for damage identification. A subset of the software in SHMTools is embeddable, which consists of Matlab functions that can be cross-compiled into generic 'C' programs to be run on a target hardware. The software is also designed to accommodate multiple sensing modalities, including piezoelectric active-sensing, which has been widely used in SHM practice. The software package, including standardized datasets, are publicly available for use by the SHM community. The details of this embeddable software will be discussed, along with several example processes that can be used for guidelines for future use of the software.

Reliability-Based Framework for Damage Prognosis of Adhesively-Bonded Joints in Composite UAV Wings

Abstract: The extensive use of lightweight composite materials in unmanned aerial vehicles (UAVs) drastically increases the sensitivity to both fatigue- and impact-induced damage of their critical structural components. The skin-to-spar adhesive joints are considered one of the most fatigue sensitive subcomponents of a lightweight composite UAV wing with the damage progressively evolving from the wing root. Therefore, a system capable of monitoring these joints, assessing the structural integrity of the wing, identifying a condition-based maintenance, and predicting the remaining life (damage prognosis) is ultimately needed. In contribution to this goal, the paper presents the theoretical development of a novel probabilistic methodology for predicting the remaining service life of adhesively-bonded joints in composite UAV wings. Non-destructive evaluation techniques and Bayesian inference are used to (i) assess the current state of damage and, (ii) update the probability distribution of the damage extent at various locations. A probabilistic model for future aerodynamic loads—turbulence and maneuver induced—and a mechanics-based damage model for the adhesive interfaces are then used to propagate damage through the joints. Combined local (e.g., exceedance of a critical damage size) and global (e.g., exceedance of the flutter instability boundary) failure criteria are finally employed to compute the probability of failure at future times by abstracting the UAV wing as a series system. ∆

Real-time condition assessment of RAPTOR telescope systems

Abstract: The RAPid Telescopes for Optical Response (RAPTOR) observatory network consists of several robotic astronomical telescopes primarily designed to search for astrophysical transients called a gamma-ray bursts (GRBs). Although intrinsically bright, GRBs are difficult to detect because of their short duration. Typically, they are first observed by satellites that then relay the coordinates of the GRB to a ground station which, in turn, distributes the coordinates over the internet so that ground based observers can perform follow-up observations. Typically the ground based observations begin after the GRB has ended and only residual emiSSion (the 'afterglow') is left. However, if the satellite relays the GRB coordinates quickly enough, a 'fast' robotic telescope on the ground may be able to catch the GRB in progress. The RAPTOR telescope system is one of only a few in the world to have accomplished this feat. In order to achieve these results, the RAPTOR telescopes must operate autonomously at a high duty-cycle and in peak operating condition. Currently the telescopes are maintained in an ad hoc manner, often in a run-to-failure mode. The RAPTOR project could benefit greatly from a structural health monitoring (SHM) system, especially as more complex units are added to the suite ofmore » telescopes. This paper will summarize preliminary results from an SHM study performed on one of the RAPTOR telescopes. Damage scenarios that are of concern and that have been previously observed are first summarized. Then a specific study of damage to the telescope drive mechanism is presented where the data acquisition system is first described. Next, damage detection algorithms are developed with LANL's new publically available software SHMTools and the results of this process are discussed in detail. The paper will conclude with a summary of future planned refinemenls of the RAPTOR SHM system.« less