Jeannette R. Wait

Bio: Jeannette R. Wait is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Structural health monitoring & Lamb waves. The author has an hindex of 10, co-authored 13 publications receiving 776 citations.

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.

Structural Health Monitoring Using Modular Wireless Sensors

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...

Using state space predictive modeling with chaotic interrogation in detecting joint preload loss in a frame structure experiment

Abstract: This work explores the role of steady-state dynamic analysis in the vibration-based structural health monitoring field. While more traditional approaches focus on transient or stochastic vibration analysis, the method described here utilizes a geometric portrait of system dynamics to extract information about the steady-state response of the structure to sustained excitation. The approach utilizes the fundamental properties of chaotic signals to produce low-dimensional response data which are then analyzed for features which indicate the degree to which the dynamics have been altered by damage. A discussion of the fundamental issues involved in the approach is presented along with experimental evidence of the approach's ability to discriminate among several damage scenarios.

Integrated Structural Health Assessment Using Piezoelectric Active Sensors

Abstract: This paper illustrates an integrated approach for identifying structural damage. The method presented utilizes piezoelectric (PZT) materials to actuate/sense the dynamic response of the structures. Two damage identification techniques are integrated in this study, including impedance methods and Lamb wave propagations. The impedance method monitors the variations in structural mechanical impedance, which is coupled with the electrical impedance of the PZT patch. In Lamb wave propagations, one PZT patch acting as an actuator 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. Both the Lamb wave and impedance methods operate in high frequency ranges at which there are measurable changes in structural responses even for incipient damage such as small cracks, debonding, or loose connections. The combination of the local impedance method with the wave propagation based approach allows a better characterization of the system’s structural integrity. The paper concludes with experimental results to demonstrate the feasibility of this integrated active sensing technology.

Nonlinear Time Series Analysis.

Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

A summary review of wireless sensors and sensor networks for structural health monitoring

Abstract: In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered monitoring systems. A benefit of wireless structural monitoring systems is that they are inexpensive to install because extensive wiring is no longer required between sensors and the data acquisition system. Researchers are discovering that wireless sensors are an exciting technology that should not be viewed as simply a substitute for traditional tethered monitoring systems. Rather, wireless sensors can play greater roles in the processing of structural response data; this feature can be utilized to screen data for signs of structural damage. Also, wireless sensors have limitations that require novel system architectures and modes of operation. This paper is intended to serve as a summary review of the collective experience the structural engineering community has gained from the use of wireless sensors and sensor networks for monitoring structural performance and health.

Effects of environmental and operational variability on structural health monitoring.

Abstract: Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system’s vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.

The fundamental axioms of structural health monitoring

Abstract: Based on the extensive literature that has developed on structural health monitoring over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or gen eral principles, have emerged. The intention of this paper is to explicitly state and justify these axioms. In so doing, it is hoped that two subsequent goals are facilitated. First, the statement of such axioms will give new researchers in the field a starting point that alleviates the need to review the vast amounts of literature in this field. Second, the authors hope to stimulate discussion and thought within the community regarding these axioms.