Showing papers by "Charles R. Farrar published in 2011"

Machine learning algorithms for damage detection under operational and environmental variability

Abstract: The goal of this article is to detect structural damage in the presence of operational and environmental variations using vibration-based damage identification procedures. For this purpose, four ma...

Structural health monitoring of wind turbines: method and application to a HAWT

Abstract: Structural health monitoring in the context of a Micon 65/13 horizontal axis wind turbine was described in this paper as a process in statistical pattern recognition. Simulation data from a calibrated model with less than 8% error in the first 14 natural frequencies of vibration was used to study the operational response under various wind states as well as the effects of three types of damage in the blade, low speed shaft and yaw joint. It was shown that vertical wind shear and turbulent winds lead to different modal contributions in the operational response of the turbine suggesting that the sensitivity of operational data to damage depends on the wind loads. It is also shown that there is less than a 4% change in the wind turbine natural frequencies given a 25% reduction in the stiffness at the root of one blade. The modal assurance criterion was used to analyse the corresponding changes in modal deflections, and this criterion exhibited nearly orthogonal changes because of the three damage scenarios suggesting that the modal deflection determines which damage is observable at a given frequency for a given wind state. The' modal contribution is calculated as a damage feature, which changes as much as 100% for 50% reductions in blade root stiffness, but only the blade damage is detected using this feature. Operational data was used to study variations in the forced blade response to determine the likelihood that small levels of damage can be detected amidst variations in wind speed across the rotor plane. The standard deviation in measured data was shown to be smallest for the span and edge-wise measurements at 1P due to gravity, which provides the dominant forcing function at this frequency. A 3% change in the response in the span and edge-wise directions because of damage is required to detect a change of three standard deviations in contrast to the 90% change in flap direction response that is required to detect a similar change because of damage. The dynamic displacement in the span direction is then used to extract a damage feature from the simulation data that provides the ability to both locate and quantify the reduction in stiffness in the blade root. Copyright © 2011 John Wiley & Sons, Ltd.

Influence of the Autoregressive Model Order on Damage Detection

Abstract: : An important step for using time-series autoregressive (AR) models for structural health monitoring is the estimation of the appropriate model order. To obtain an optimal AR model order for such processes, this article presents and discusses four techniques based on Akaike information criterion, partial autocorrelation function, root mean squared error, and singular value decomposition. A unique contribution of this work is to provide a comparative study with three different AR models that is carried out to understand the influence of the model order on the damage detection process in the presence of simulated operational and environmental variability. A three-story base-excited frame structure was used as a test bed in a laboratory setting, and data sets were measured for several structural state conditions. Damage was introduced by a bumper mechanism that induces a repetitive impact-type nonlinearity. The operational and environmental effects were simulated by adding mass and by changing the stiffness properties of the columns. It was found that these four techniques do not converge to a unique solution, rather all require somewhat qualitative interpretation to define the optimal model order. The comparative study carried out on these data sets shows that the AR model order range defined by the four techniques provides robust damage detection in the presence of simulated operational and environmental variability.

Transmissibility of non-linear output frequency response functions with application in detection and location of damage in MDOF structural systems

Abstract: Transmissibility is a well-known linear system concept that has been widely applied in the diagnosis of damage in various engineering structural systems. However, in engineering practice, structural systems can behave non-linearly due to certain kinds of damage such as, e.g., breathing cracks. In the present study, the concept of transmissibility is extended to the non-linear case by introducing the Transmissibility of Non-linear Output Frequency Response Functions (NOFRFs). The NOFRFs are a concept recently proposed by the authors for the analysis of non-linear systems in the frequency domain. A NOFRF transmissibility-based technique is then developed for the detection and location of both linear and non-linear damage in MDOF structural systems. Numerical simulation results verify the effectiveness of the new technique. Experimental studies on a three-storey building structure demonstrate the potential to apply the developed technique to the detection and location of damage in practical MDOF engineering structures.

Use of Relative Baseline Features of Guided Waves for In situ Structural Health Monitoring

Abstract: This article presents a new signal-processing technique, which utilizes ‘‘relative baselines’’ instead of ‘‘pre-stored baselines,’’ for Lamb wave based SHM Several successful SHM methods utilizing wave propagations usually involve recording baseline measurements and comparing them to a newly measured response for structural damage identification However, maintaining an accurate database of baselines remains challenging because of the effects of varying environmental conditions Therefore, in this study, the relative baseline concept is proposed, in which measured Lamb waves are correlated between different sensor-actuator sets, as opposed to being correlated to pre-stored baseline data This study focuses on determining the feature best used for this relative baseline concept, and cross-correlation and power spectral density analysis techniques are performed on data sets recorded from composite and aluminum plates Experiments are performed with these plates under the presence of temperature variations

The application of compressed sensing to detecting damage in structures

Abstract: One of the principal challenges facing the structural health monitoring (SHM) community is taking large, heterogeneous sets of data collected from sensors, and extracting information that allows the estimation of the remaining service life of a structure. Another important challenge is to collect relevant data from a structure in a manner that is cost effective, and respects the size, weight, cost, energy consumption, and bandwidth limitations placed on the system. In this work we explore the suitability of compressed sensing to address both challenges. In this work a digital version of a compressed sensor is implemented on-board a microcontroller similar to those used in embedded SHM sensor nodes. The sensor node is tested in a surrogate SHM application requiring acceleration measurements. Currently the prototype compressed sensor is capable of collecting compressed coefficients from measurements and sending them to an off-board processor for reconstruction using L1 norm minimization. A compressed version of the matched filter known as the smashed filter, has also been implemented on-board the sensor node, and its suitability for detecting structural damage will be discussed.

Feature extraction for structural dynamics model validation

Abstract: This study focuses on defining and comparing response features that can be used for structural dynamics model validation studies Features extracted from dynamic responses obtained analytically or experimentally, such as basic signal statistics, frequency spectra, and estimated timeseries models, can be used to compare characteristics of structural system dynamics By comparing those response features extracted from experimental data and numerical outputs, validation and uncertainty quantification of numerical model containing uncertain parameters can be realized In this study, the applicability of some response features to model validation is first discussed using measured data from a simple test-bed structure and the associated numerical simulations of these experiments Issues that must be considered were sensitivity, dimensionality, type of response, and presence or absence of measurement noise in the response Furthermore, we illustrate a comparison method of multivariate feature vectors for statistical model validation Results show that the outlier detection technique using the Mahalanobis distance metric can be used as an effective and quantifiable technique for selecting appropriate model parameters However, in this process, one must not only consider the sensitivity of the features being used, but also correlation of the parameters being compared

Application of a Wireless Sensor Node to Health Monitoring of Operational Wind Turbine Blades

Abstract: Structural health monitoring (SHM) is a developing field of research with a variety of applications including civil structures, industrial equipment, and energy infrastructure. An SHM system requires an integrated process of sensing, data interrogation and statistical assessment. The first and most important stage of any SHM system is the sensing system, which is traditionally composed of transducers and data acquisition hardware. However, such hardware is often heavy, bulky, and difficult to install in situ. Furthermore, physical access to the structure being monitored may be limited or restricted, as is the case for rotating wind turbine blades or unmanned aerial vehicles, requiring wireless transmission of sensor readings. This study applies a previously developed compact wireless sensor node to structural health monitoring of rotating small-scale wind turbine blades. The compact sensor node collects low-frequency structural vibration measurements to estimate natural frequencies and operational deflection shapes. The sensor node also has the capability to perform high-frequency impedance measurements to detect changes in local material properties or other physical characteristics. Operational measurements were collected using the wireless sensing system for both healthy and damaged blade conditions. Damage sensitive features were extracted from the collected data, and those features were used to classify the structural condition as healthy or damaged.

Embedded processing for SHM with integrated software control of a wireless impedance device

Abstract: Wireless sensor nodes with impedance measurement capabilities, often based on the Analog Devices AD5933 impedance chip and Atmel's 8-bit ATMega 1281 microcontroller, have been demonstrated to be effective in collecting data for localized damage detection (such as for loose bolt detection) and for sensor self-diagnostics. Previouslydeveloped nodes rely on radio telemetry and off-board processing (usually via a PC) to ascertain damage presence or sensor condition. Recent firmware improvements for the wireless impedance device (WID) now allow seamless integration of the WID with SHMTools and mFUSE, an open-source function sequencer and SHM process platform for Matlab. Furthermore, SHM processes developed using mFUSE can be implemented in hardware on the WID, allowing greater autonomy among the sensor nodes to identify and report damage in real time. This paper presents the capabilities of the newly integrated hardware and software, as well as experimental validation.

Escape and evade control policies for ensuring the physical security of nonholonomic, ground-based, unattended mobile sensor nodes

Abstract: In order to realize the wide-scale deployment of high-endurance, unattended mobile sensing technologies, it is vital to ensure the self-preservation of the sensing assets Deployed mobile sensor nodes face a variety of physical security threats including theft, vandalism and physical damage Unattended mobile sensor nodes must be able to respond to these threats with control policies that facilitate escape and evasion to a low-risk state In this work the Precision Immobilization Technique (PIT) problem has been considered The PIT maneuver is a technique that a pursuing, car-like vehicle can use to force a fleeing vehicle to abruptly turn ninety degrees to the direction of travel The abrupt change in direction generally causes the fleeing driver to lose control and stop The PIT maneuver was originally developed by law enforcement to end vehicular pursuits in a manner that minimizes damage to the persons and property involved It is easy to imagine that unattended autonomous convoys could be targets of this type of action by adversarial agents This effort focused on developing control policies unattended mobile sensor nodes could employ to escape, evade and recover from PIT-maneuver-like attacks The development of these control policies involved both simulation as well as small-scale experimental testing The goal of this work is to be a step toward ensuring the physical security of unattended sensor node assets

Piezoelectric active sensing techniques for damage detection on wind turbine blades

Abstract: This paper presents the performance of a variety of structural health monitoring (SHM) techniques, based on the use of piezoelectric active sensors, to determine the structural integrity of a 9m CX-100 wind turbine blade (developed by Sandia National Laboratory). First, the dynamic characterization of a CX-100 blade is performed using piezoelectric transducers, where the results are compared to those by conventional accelerometers. Several SHM techniques, including Lamb wave propagations, frequency response functions, and time series based methods are then utilized to analyze the condition of the wind turbine blade. The main focus of this research is to assess and construct a performance matrix to compare the performance of each method in identifying incipient damage, with a special consideration given the issues related to field deployment. Experiments are conducted on a stationary, full length CX-100 wind turbine blade. This examination is a precursor for planned full-scale fatigue testing of the blade and subsequent tests to be performed on an operational CX-100 Rotor Blade to be flown in the field.

A full-scale fatigue test of 9-m CX-100 wind turbine blades

Abstract: : This paper presents the SHM result of a 9m CX-100 wind turbine blade under full-scale fatigue loads. The test was performed at the National Renewable Energy Laboratory. The 9-meter blade was instrumented with piezoelectric transducers, accelerometers, acoustic emission sensors, and foil strain gauges on the surface of the blade. The blade underwent fatigue excitation at 1.8 Hz for defined intervals, and data from the sensors were collected between and during fatigue loading sessions. The data were measured at multi-scale, high frequency ranges for identifying fatigue damage initiation, and low-frequency ranges for assessing damage progression. High and Low frequency response functions, time series based methods, and Lamb wave date measured by piezoelectric transducers were utilized to analyze the condition of the turbine blade, along with other sensing systems (acoustic emission). A specially designed hardware developed by Los Alamos National Laboratory was also implemented for performance comparison. This paper summarizes considerations needed to design such SHM systems, experimental procedures and results, and additional issues that can be used as guidelines for future investigations.

Input Estimation from Measured Structural Response

Abstract: This report will focus on the estimation of unmeasured dynamic inputs to a structure given a numerical model of the structure and measured response acquired at discrete locations While the estimation of inputs has not received as much attention historically as state estimation, there are many applications where an improved understanding of the immeasurable input to a structure is vital (eg validating temporally varying and spatiallyvarying load models for large structures such as buildings and ships) In this paper, the introduction contains a brief summary of previous input estimation studies Next, an adjoint-based optimization method is used to estimate dynamic inputs to two experimental structures The technique is evaluated in simulation and with experimental data both on a cantilever beam and on a three-story frame structure The performance and limitations of the adjoint-based input estimation technique are discussed

Structural Damage Identification in Stiffened Plate Fatigue Specimens Using Piezoelectric Active Sensing

Abstract: This paper presents a guided wave structural health monitoring (SHM) technique, based on the use of piezoelectric active-sensors, used to determine the structural integrity of stiffened aluminum plates. For damage detection, the transmitted power between piezoelectric transducers used for Lamb wave propagation is utilized to analyze the extent of damage in the structure. Damage initiation and propagation were successfully monitored with the guided wave technique. Overall, these methods yielded sufficient damage detection capability to warrant further investigation into field deployment. This paper summarizes considerations needed to design such SHM systems, experimental procedures and results, and recommendations that can be used as guidelines for future investigations.

Characterization of satellite components assembly for responsive space applications

Abstract: The rapid deployment of satellites is hindered by the need to flight-qualify their components and the resulting mechanical assembly. Conventional methods for qualification testing of satellite components are costly and time consuming. Furthermore, full-scale vehicles must be subjected to launch loads during testing. The focus of this research effort was to assess the performance of Structural Health Monitoring (SHM) techniques to replace the high-cost qualification procedure and to localize faults introduced by improper assembly. SHM techniques were applied on a small-scale structure representative of a responsive satellite. The test structure consisted of an extruded aluminum spaceframe covered with aluminum shear plates, which was assembled using bolted joints. Multiple piezoelectric patches were bonded to the test structure and acted as combined actuators and sensors. Piezoelectric Active-sensing based wave propagation and frequency response function techniques were used in conjunction with finite element modeling to capture the dynamic properties of the test structure. Areas improperly assembled were identified and localized. This effort primarily focused on determining whether or not bolted joints on the structure were properly tightened.

Integration of SHM into Bridge Management Systems: Case Study—Z24 Bridge

Abstract: Bridge Management System (BMS) is a decision-support tool developed to assist the authorities in determining how and when to make decisions regarding maintenance, repair, and rehabilitation of structures in a systematic way. However, despite the advances in BMS modeling, the condition assessment activities still rely heavily on visual inspections, which inherently produce widely variable results. On the other hand, the goal of Structural Health Monitoring is to improve the safety and reliability of aerospace, civil, and mechanical infrastructure by detecting damage before it reaches a critical state. To achieve this goal, technology is being developed to replace qualitative visual inspection and time-based maintenance procedures with more quantifiable and automated damage assessment processes. It is the authors’ belief that for the activities related to bridge safety and maintenance should be based on visual inspections along with results from long-term monitoring. Over the last decade, the authors have realized that research in both fields has been conducted separately. Therefore, in order to develop a more reliable bridge safety and maintenance process, this paper summarizes the foundation of an approach to integrate both fields. The applicability of this approach is then demonstrated on data from the Z24 Bridge in Switzerland.

Towards the development of tamper-resistant, ground-based mobile sensor nodes

Abstract: Mobile sensor nodes hold great potential for collecting field data using fewer resources than human operators would require and potentially requiring fewer sensors than a fixed-position sensor array. It would be very beneficial to allow these mobile sensor nodes to operate unattended with a minimum of human intervention. In order to allow mobile sensor nodes to operate unattended in a field environment, it is imperative that they be capable of identifying and responding to external agents that may attempt to tamper with, damage or steal the mobile sensor nodes, while still performing their data collection mission. Potentially hostile external agents could include animals, other mobile sensor nodes, or humans. This work will focus on developing control policies to help enable a mobile sensor node to identify and avoid capture by a hostile un-mounted human. The work is developed in a simulation environment, and demonstrated using a non-holonomic, ground-based mobile sensor node. This work will be a preliminary step toward ensuring the cyber-physical security of ground-based mobile sensor nodes that operate unattended in potentially unfriendly environments.

SHMTools"" for SHM and sensor diagnostics: lug assembly applications

Abstract: SHMTools is a free, open-source set of standardized MATLAB software tools for Structural Health Monitoring (SHM) research. The software package includes a library of compatible SHM algorithms. This paper is a report of an initial investigation into application of SHMtools for 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 time-series analysis, and high-frequency response functions measured by piezoelectric activesensors. In addition, this software is also used for monitoring the functionality of piezoelectric transducers in SHM. Practical implementation issues, including temperature changes, are also considered in this study.