Showing papers by "Charles R. Farrar published in 2003"
TL;DR: In this article, Niezrecki et al. summarized the hardware and software issues of impedance-based structural health modi- toring based on piezoelectric materials.
Abstract: In this paper we summarize the hardware and software issues of impedance-based structural health moni- toring based on piezoelectric materials. The basic concept of the method is to use high-frequency structural excitations to monitor the local area of a structure for changes in structural impedance that would indicate imminent damage. A brief overview of research work on experimental and theoretical stud- ies on various structures is considered and several research papers on these topics are cited. This paper concludes with a discussion of future research areas and path forward. Piezoelectric materials acting in the "direct" manner pro- duce an electrical charge when stressed mechanically. Con- versely, a mechanical strain is produced when an electrical field is applied. The direct piezoelectric effect has often been used in sensors such as piezoelectric accelerometers. With the converse effect, piezoelectric materials apply local- ized strains and directly influence the dynamic response of the structural elements when either embedded or surface bonded into a structure. Piezoelectric materials have been widely used in structural dynamics applications because they are lightweight, robust, inexpensive, and come in a variety of forms ranging from thin rectangular patches to complex shapes being used in microelectromechanical systems (MEMS) fabrications. The applications of piezoelectric mate- rials in structural dynamics are too numerous to mention and are detailed in the literature (Niezrecki et al., 2001; Chopra, 2002). The purpose of this paper is to explore the importance and effectiveness of impedance-based structural health mon- itoring from both hardware and software standpoints. Imped- ance-based structural health monitoring techniques have been developed as a promising tool for real-time structural dam- age assessment, and are considered as a new non-destructive evaluation (NDE) method. A key aspect of impedance-based structural health monitoring is the use of piezoceramic (PZT) materials as collocated sensors and actuators. The basis of this active sensing technology is the energy transfer between the actuator and its host mechanical system. It has been shown that the electrical impedance of the PZT material can be directly related to the mechanical impedance of a host structural component where the PZT patch is attached. Uti- lizing the same material for both actuation and sensing not only reduces the number of sensors and actuators, but also reduces the electrical wiring and associated hardware. Fur- thermore, the size and weight of the PZT patch are negligible compared to those of the host structures so that its attach- ment to the structure introduces no impact on dynamic char- acteristics of the structure. A typical deployment of a PZT on a structure being monitored is shown in Figure 1. The first part of this paper (Sections 2 and 3) deals with the theoretical background and design considerations of the impedance-based structural health monitoring. The signal processing of the impedance method is outlined in Section 4. In Section 5, experimental studies using the impedance approaches are summarized and related previous works are listed. Section 6 presents a brief comparison of the imped- ance method with other NDE approaches and, finally, sev- eral future issues are outlined in Section 7. 2. Theoretical Background
1,048 citations
TL;DR: In this article, a structural health monitoring module was implemented by coupling commercially available microelectro-mechanical system sensors and a wireless telemetry unit with damage detection firmware, which can detect damage to the joint.
Abstract: System integration of an online structural health monitoring module was accomplished by coupling commercially available microelectro-mechanical system sensors and a wireless telemetry unit with damage detection firmware. To showcase the capabilities of the integrated monitoring module, a bolted frame structure was constructed, and the preload in one of the bolted joints was controlled by a piezoelectric stack actuator to simulate gradual deterioration of a bolted connection. Two separate damage detection algorithms were used to classify a joint as damaged or undamaged. First, a statistical process control algorithm was used to monitor the correlation of vibration data from two accelerometers mounted across a joint. Changes in correlation were used to detect damage to the joint. For each joint, data were processed locally on a microprocessor integrated with the wireless module, and the diagnosis result was remotely transmitted to the base monitoring station. Second, a more sophisticated damage detection al...
114 citations
19 Aug 2003
TL;DR: In this article, a state-of-the-art design of a wireless sensing unit, which serves as the fundamental building block of wireless modular monitoring systems (WiMMS), has been optimized for structural sensing applications.
Abstract: A state-of-art design of a wireless sensing unit, which serves as the fundamental building block of wireless modular monitoring systems (WiMMS), has been optimized for structural sensing applications. Employing wireless communications as a primary means of data transfer, the high-cost but fragile cables of traditional tethered monitoring systems is eradicated resulting in a low-cost and flexible monitoring infrastructure. An additional innovation is the inclusion of advanced embedded microcontrollers to accommodate the computational tasks of engineering and decision support analysis. To quantify the performance of the wireless sensing unit, field validation upon a full-scale benchmark structure is undertaken. The Alamosa Canyon Bridge in New Mexico is instrumented with wireless sensing units and a traditional cable-based monitoring system in parallel. Forced vibrations are applied to the bridge and monitored using both (wireless and tethered) data acquisition systems. Recorded time-history measurements are used to identify the modal properties of the structural system. The performance of the wireless sensing units is compared to that of the commercial wire-based monitoring system.
95 citations
TL;DR: In this article, the authors proposed a damage sensitive feature that takes advantage of the nonlinearities associated with discontinuities introduced into the dynamic response data as a result of certain types of damage.
82 citations
TL;DR: The performance of the SPRT is improved by integrating extreme values statistics, which specifically models behavior in the tails of the distribution of interest into the SPRTs, which improves the early identification of conditions that could lead to performance degradation and safety concerns.
Abstract: The primary objective of damage detection is to ascertain with confidence if damage is present or not within a structure of interest. In this study, a damage classification problem is cast in the context of the statistical pattern recognition paradigm. First, a time prediction model, called an autoregressive and autoregressive with exogenous inputs (AR-ARX) model, is fit to a vibration signal measured during a normal operating condition of the structure. When a new time signal is recorded from an unknown state of the system, the prediction errors are computed for the new data set using the time prediction model. When the structure undergoes structural degradation, it is expected that the prediction errors will increase for the damage case. Based on this premise, a damage classifier is constructed using a sequential hypothesis testing technique called the sequential probability ratio test (SPRT). The SPRT is one form of parametric statistical inference tests, and the adoption of the SPRT to damage detectio...
74 citations
TL;DR: The Los Alamos Damage Prognosis Initiative as discussed by the authors is an initiative of the Los A. Alamos National Laboratory through Laboratory Directed Research Development (LDRD), which was established by the Department of Energy through laboratory directed research development.
Abstract: Funding for the Los Alamos Damage Prognosis Initiative is being provided by the Department of
Energy through Laboratory Directed Research Development. In addition to the authors, the
Damage Prognosis research team includes Los Alamos staff members Matt Bement, Irene
Beyerlein, Norm Hunter, Cheng Liu, Brett Nadler, and Jeni Wait. Los Alamos graduate research
assistants Tim Fasel, Jan Goethals and Trevor Tippetts, and. Professor Dan Inman and graduate
student David Allen at Virginia Tech.
31 citations
19 Aug 2003
TL;DR: In this paper, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored.
Abstract: In this study, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored. Damage sensitive features that explicitly consider the nonlinear system input/output relationships produced by damage are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS is useful in this case because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the current method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based method using piezoelectric (PZT) material as both an actuator and a sensor is then proposed as a possible solution to the problem of damage localization.
10 citations
TL;DR: The study summarized in this paper proposes a damage sensitive feature that takes advantage of the nonlinearities associated with discontinuities introduced into the dynamic response data as a result of certain types of damage.
8 citations
18 Aug 2003
TL;DR: The Graphical User Interface (GUI) of the DIAMOND II software is based on the idea of GLASS (Graphical Linking and Assembly of Syntax Structure) technology, which is currently being implemented at LANL.
Abstract: At Los Alamos National Laboratory (LANL), various algorithms for structural health monitoring problems have been explored in the last 5 to 6 years. The original DIAMOND (Damage Identification And MOdal aNalysis of Data) software was developed as a package of modal analysis tools with some frequency domain damage identification algorithms included. Since the conception of DIAMOND, the Structural Health Monitoring (SHM) paradigm at LANL has been cast in the framework of statistical pattern recognition, promoting data driven damage detection approaches. To reflect this shift and to allow user-friendly analyses of data, a new piece of software, DIAMOND II is under development. The Graphical User Interface (GUI) of the DIAMOND II software is based on the idea of GLASS (Graphical Linking and Assembly of Syntax Structure) technology, which is currently being implemented at LANL. GLASS is a Java based GUI that allows drag and drop construction of algorithms from various categories of existing functions. In the platform of the underlying GLASS technology, DIAMOND II is simply a module specifically targeting damage identification applications. Users can assemble various routines, building their own algorithms or benchmark testing different damage identification approaches without writing a single line of code.
7 citations
01 Jan 2003
TL;DR: In this article, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established Damage sensitive features that explicitly consider nonlinear system input/output relationships are extracted from the ARX model Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier.
Abstract: In this paper, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established Damage sensitive features that explicitly consider nonlinear system input/output relationships are extracted from the ARX model Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated with an impedance-based method that uses piezoelectric (PZT) material as both actuators and sensors The analyzed data is obtained from a laboratory experiment of a three-story building modelCopyright © 2003 by ASME
01 Jan 2003
TL;DR: In this article, the use of statistically rigorous algorithms combined with active-sensing impedance methods for damage identification in engineering systems is presented, where the authors propose to use statistical pattern recognition methods to address damage classification and data mining issues associated with the examination of large numbers of impedance signals for health monitoring applications.
Abstract: This paper presents the use of statistically rigorous algorithms combined with active-sensing impedance methods for damage identification in engineering systems. In particular, we propose to use statistical pattern recognition methods to address damage classification and data mining issues associated with the examination of large numbers of impedance signals for health monitoring applications. The impedance-based structural health monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of damage identification applications. Relying on high frequency local excitations (typically>30 kHz), this technique is very sensitive to minor changes in structural integrity in the near field of piezoelectric sensors. In this study, in order to diagnosis damage with levels of statistical confidence, the impedance-based monitoring is cast in the context of an outlier detection framework. A modified autoregressive model with exogenous inputs (ARX) in the frequency domain is developed. The damage sensitive feature is then computed by differentiating the measured impedance and the output of the ARX model. Furthermore, because of the non-Gaussian nature of the feature distribution tails, extreme value statistics (EVS) are employed to develop a robust damage classifier. By incorporating EVS, we establish a rigorous impedance-based health monitoring algorithm, which is able to provide structural systems with self-contained and selfdiagnostic components. This paper concludes with a numerical example on a 5 degree-of-freedom system and an experimental investigation on a multi-story building model to demonstrate the performance of the proposed concept.