Showing papers by "Charles R. Farrar published in 2004"

Wavelet-based active sensing for delamination detection in composite structures

Abstract: In this paper a signal processing technique is developed to detect delamination on composite structures. In particular, a wavelet-based signal processing technique is developed and combined with an active sensing system to produce a near-real-time, online monitoring system for composite structures. A layer of piezoelectric patches is used to generate an input signal with a specific wavelet waveform and to measure response signals. Then, the response signals are processed by a wavelet transform to extract damage-sensitive features from the original signals. The applicability of the proposed method to delamination identification has been demonstrated by experimental studies of a composite plate under varying temperature and boundary conditions.

Design and performance validation of a wireless sensing unit for structural monitoring applications

Abstract: There exists a clear need to monitor the performance of civil structures over their operational lives. Current commercial monitoring systems suffer from various technological and economic limitations that prevent their widespread adoption. The wires used to route measurements from system sensors to the centralized data server represent one of the greatest limitations since they are physically vulnerable and expensive from an installation and maintenance standpoint. In lieu of cables, the introduction of low-cost wireless communications is proposed. The result is the design of a prototype wireless sensing unit that can serve as the fundamental building block of wireless modular monitoring systems (WiMMS). An additional feature of the wireless sensing unit is the incorporation of computational power in the form of state-of-art microcontrollers. The prototype unit is validated with a series of laboratory and field tests. The Alamosa Canyon Bridge is employed to serve as a full-scale benchmark structure to validate the performance of the wireless sensing unit in the field. A traditional cable-based monitoring system is installed in parallel with the wireless sensing units for performance comparison.

Design of a wireless active sensing unit for structural health monitoring

Abstract: Many academic and commercial researchers are exploring the design and deployment of wireless sensors that can be used for structural monitoring. The concept of intelligent wireless sensors can be further extended to include actuation capabilities. In this study, the design of a wireless sensing unit that has the capability to command active sensors and actuators is proposed for structural monitoring applications. Active sensors are sensors that can input excitations into a structural system and simultaneously monitor the corresponding system's response. The computational core of the wireless active sensing unit is capable of interrogating response data in real time and can be used to execute embedded damage detection analyses. With high-order vibration modes of structural elements exhibiting greater sensitivity to damage than global structural modes, wireless active sensors can play a major role in a structural health monitoring system because they are capable of exciting high-order modes. A computational framework for analyzing piezoelectric based active sensor signals for indications of structural damage is proposed. For illustration, a simple aluminum plate with piezoelectric active sensors mounted to its surface is used.

Plate damage identification using wave propagation and impedance methods

Abstract: This paper illustrates an integrated approach for identifying structural damage in an aluminum plate. Piezoelectric (PZT) materials are used to actuate/sense the dynamic response of the structure. Two damage identification techniques are integrated in this study, including Lamb wave propagations and impedance methods. In Lamb wave propagations, one PZT launches an elastic wave through the structure, and responses are measured by an array of PZT sensors. The changes in both wave attenuation and reflection are used to detect and locate the damage. The impedance method monitors the variations in structural mechanical impedance, which is coupled with the electrical impedance of the PZT. Both methods operate in high frequency ranges at which there are measurable changes in structural responses even for incipient damage such as small cracks or loose connections. This paper summarizes two methods used for damage identification, experimental procedures, and additional issues that can be used as a guideline for future investigations.

Holder exponent analysis for discontinuity detection

Abstract: In this paper, a Holder exponent, a measure of the degree to which a signal is differentiable, is presented to detect the presence of a discontinuity and when the discontinuity occurs in a dynamic signal. This discontinuity detection has potential applications to structural health monitoring because discontinuities are often introduced into dynamic response data as a result of certain types of damage. Wavelet transforms are incorporated with the Holder exponent to capture the time varying nature of discontinuities, and a classification procedure is developed to quantify when changes in the Holder exponent are significant. The proposed Holder exponent analysis is applied to various experimental signals to reveal underlying damage causing events from the signals. Signals being analyzed include acceleration response of a mechanical system with a rattling internal part, acceleration signals of a three-story building model with a loosing bolt, and strain records of an in-situ bridge during construction. The experimental results presented in this paper demonstrate that the Holder exponent can be an effective tool for identifying certain types of events that introduce discontinuities into the measured dynamic response data.

Embedding actuation functionalities in a wireless structural health monitoring system

Abstract: Structural health monitoring is a broad field that encompasses a number of synergetic technologies that together can provide a system to identify and characterize possible damage present within a structure. Such a system should include data acquisition subsystems capable of recording a structure’s response to ambient and external loads. Furthermore, it is now possible to include computational hardware in system designs for local execution of embedded engineering analyses that interrogate recorded response data for indicators correlated to possible damage. This paper describes a prototype wireless actuation and sensing unit which is intended: (1) to collect measurement data from sensors embedded within structural elements that are excited by low-energy actuation elements; (2) to store, manage and locally process the measurement data collected; and (3) to communicate data and results to a structural monitoring system network.

A statistical pattern recognition paradigm for structural health monitoring

Abstract: Funding for this investigation was provided by Los Alamos National Laboratory’s Directed Research and Development funds.

Developing impact and fatigue damage prognosis solutions for composites

Abstract: An approach to developing a damage prognosis solution that integrates advanced sensing technology, data interrogation procedures for damage detection, novel model validation and uncertainty quantification techniques, and reliability-based decision-making algorithms is summarized in this article. In parallel, experimental efforts are underway to deliver a proof-of-principle technology demonstration by assessing impact damage and predicting the subsequent fatigue damage accumulation in a composite plate. This article provides an overview of the various technologies that are being integrated to address this damage prognosis problem.

Piezoelectric Structural Excitation using a Wireless Active Sensing Unit

Abstract: Strong interest in applying wireless sensing technologies within structural monitoring systems has grown in recent years. Wireless sensors are capable of passively collecting response measurements of a dynamic structural system at low-costs. However, the role of wireless sensing within structural monitoring systems can be expanded if sensors are provided a direct interface to the physical system in which they are installed. Capable of exciting a structural system through actuators, a wireless “active” sensor would be a valuable tool in structural control and damage detection applications. In this study, a wireless active sensing unit is proposed and fabricated. After fabrication of a prototype, a series of validation tests are conducted to assess the unit’s performance. A piezoelectric pad mounted to an aluminum plate is commanded by the wireless active sensing unit to impart lowenergy Lamb waves in the plate surface. Simultaneously, the same unit collects response measurements obtained from a second piezoelectric pad also surface mounted to the plate. To illustrate the potential of the wireless active sensing unit to locally perform system identification analyses, the computational core is provided the task of calculating autoregressive time-series models using input-output time-history data collected from the excited system.

Applications of nonlinear system identification to structural health monitoring.

Abstract: The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). In many cases damage causes a structure that initially behaves in a predominantly linear manner to exhibit nonlinear response when subject to its operating environment. The formation of cracks that subsequently open and close under operating loads is an example of such damage. The damage detection process can be significantly enhanced if one takes advantage of these nonlinear effects when extracting damage-sensitive features from measured data. This paper will provide an overview of nonlinear system identification techniques that are used for the feature extraction process. Specifically, three general approaches that apply nonlinear system identification techniques to the damage detection process are discussed. The first two approaches attempt to quantify the deviation of the system from its initial linear characteristics that is a direct result of damage. The third approach is to extract features from the data that are directly related to the specific nonlinearity associated with the damaged condition. To conclude this discussion, a summary of outstanding issues associated with the application of nonlinear system identification techniques to the SHM problem is presented.

Piezo-sensor self-diagnostics using electrical impedance measurements.

Abstract: This paper present the piezoelectric sensor self-diagnostic procedure that performs in-situ monitoring of the operational status of piezoelectric materials (PZT) used for sensors and actuators in structural health monitoring (SHM) applications. The use of active-sensing piezoelectric materials has received considerable attention in the SHM community. A critical aspect of the piezoelectric active-sensing technologies is that usually large numbers of distributed sensors and actuators are needed to perform the required monitoring process. The sensor/actuator self-diagnostic procedure, where the sensors/actuators are confirmed to be functioning properly during operation, is therefore a critical component to successfully complete the SHM process and to minimize the false indication regarding the structural health. The basis of this procedure is to track the changes in the capacitive value of piezoelectric materials resulting from the sensor failure, which is manifested in the imaginary part of the measured electrical admittances. Furthermore, through the analytical and experimental investigation, it is confirmed that the bonding layer between the PZT and a host structure significantly contributes to the measured capacitive values. Therefore, by monitoring the imaginary part of the admittances, one can quantitatively assess the degradation of the mechanical/electrical properties of the PZT and its attachment to a host structure. This papermore » concludes with an experimental example to demonstrate the feasibility of the proposed sensor-diagnostic procedure.« less

Minimizing misclassification of damage using extreme values statistics

Abstract: Structural health monitoring is a process of evaluating and ensuring the safety and integrity of structural systems by converting sensor data into structural health monitoring information. The first and most important objective of structural health monitoring is to ascertain with confidence if damage is present or not. The idea is to characterize only the normal condition of a structure and the baseline data are used as a reference. When data are measured during continuous monitoring, the new data are compared with the reference. A significant deviation from the reference will signal damage. This decision-making procedure necessitates the establishment of a decision boundary. Choosing the decision boundary is often based on the assumption that the distribution of the data is Gaussian in nature. While the problem of damage detection focuses attention on the outliers or extreme values of the data, i.e. those points in the tails of the distribution, the establishment of the decision boundary based on the normality assumption weighs the central population of the data. This unwarranted assumption about the nature of the data distribution can significantly impair the performance of a structural health monitoring system by increasing false positive and negative indications of damage. In this study, a robust and automated damage classifier is developed by properly modeling the tails of the distribution using extreme value statistics. 1 Assistant Professor 2 Research Associate 3 Professor 4 Technical Staff Member

Online Damage Detection for Theme Park Rides

Abstract: Funding for this project is provided by the Department of Energy through the internal funding programs at Los Alamos National Laboratory known as Technology Maturation Fund and Laboratory Directed Research and Development Fund. The authors acknowledge our industrial partner for providing experimental data from roller coaster rides in Orlando, Florida.

A statistical pattern recognition paradigm for structural health monitoring

Abstract: The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure 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. Our approach is to address the SHM problem in the context of a statistical pattern recognition paradigm (Farrar, Nix and Doebling, 2001). In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition, (3) Feature Extraction, and (4) Statistical Model Development for Feature Discrimination. When one attempts to apply this paradigm to data from 'real-world' structures, it quickly becomes apparent that data cleansing, normalization, fusion and compression, which can be implemented with either hardware or software, are inherent in Parts 2-4 of this paradigm. The authors believe that all approaches to SHM, as well as all traditional non-destructive evaluation procedures (e.g. ultrasonic inspection, acoustic emissions, active thermography) can be cast in the context of this statistical pattern recognition paradigm. It should be noted that the statistical modeling portion of the structural health monitoring process has received themore » least attention in the technical literature. The algorithms used in statistical model development usually fall into the three categories of group classification, regression analysis or outlier detection. The ability to use a particular statistical procedure from one of these categories will depend on the availability of data from both an undamaged and damaged structure. This paper will discuss each portion of the SHM statistical pattern recognition paradigm.« less

Structural Health Monitoring Using Macro-Fiber Composites and Impedance Methods

Abstract: A current technique that has been the subject of a great deal of study in the structural health monitoring community is the impedance method, which uses high frequency responses to monitor a local area of a structure for changes in structural impedance that indicate damage. To date, piezoceramic sensors, whose electrical impedance is directly coupled with the structure’s mechanical impedance, have been used as both sensors and actuators for impedance measurements and this practice is fairly well developed. However because these sensors are ceramic, they are brittle, making them vulnerable to accidental breakage. They also conform poorly to curved surfaces. In this study macro-fiber composite (MFC) patches, which are flexible in nature, are examined for feasibility of use with the impedance method. Two test structures with both MFC patches and PZTs are considered. Both traditional linear features and newer nonlinear features are used for structural health monitoring. High frequency responses of the MFC patches are examined and they yield damage identification results that are comparable to piezoelectric impedance signals and indicate that MFCs are suitable for impedance sensing and other high-frequency structural health monitoring.

An Integrated Active Sensing System for Damage Identification and Prognosis

Abstract: This paper illustrates an integrated approach for identifying structural damage. Two damage identification techniques, Lamb wave propagation and impedance-based methods, are investigated utilizing piezoelectric (PZT) actuators/sensors. The Lamb wave propagation and the impedance methods operate in high frequency ranges (typically > 30 kHz) at which there are measurable changes in structural responses even for incipient damage such as small cracks, debonding, delamination, and loose connections. In Lamb wave propagation, one PZT is used to launch an elastic wave through the structure, and responses are measured by an array of sensors. The technique used for the Lamb wave propagation method looks for the possibility of damage by tracking changes in transmission velocity and wave attenuation/reflections. Experimental results show that this method works well for surface anomalies. The impedance method monitors the variations in structural mechanical impedance, which is coupled with the electrical impedance of the PZT. Through monitoring the measured electrical impedance and comparing it to a baseline measurement, a decision can be made about whether or not structural damage has occurred or is imminent. In addition, significant advances have been made recently by incorporating advanced statistic-based signal processing techniques into the impedance methods. To date, several sets of experiments havemore » been conducted on a cantilevered aluminum plate and composite plate to demonstrate the feasibility of this combined active sensing technology.« less

Nonlinear feature identification of impedance-based structural health monitoring

Abstract: The impedance-based structural health monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of structural health monitoring applications. Relying on high frequency local excitations (typically>20 kHz), this technique is very sensitive to minor changes in structural integrity in the near field of piezoelectric sensors. Several damage sensitive features have been identified and used coupled with the impedance methods. Most of these methods are, however, limited to linearity assumptions of a structure. This paper presents the use of experimentally identified nonlinear features, combined with impedance methods, for structural health monitoring. Their applicability to for damage detection in various frequency ranges is demonstrated using actual impedance signals measured from a portal frame structure. The performance of the nonlinear feature is compared with those of conventional impedance methods. This paper reinforces the utility of nonlinear features in structural health monitoring and suggests that their varying sensitivity in different frequency ranges may be leveraged for certain applications.