Michael D. Todd
Other affiliations: University of California, WellDynamics, University of California, Berkeley ...read more
Bio: Michael D. Todd is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Structural health monitoring & Sensor node. The author has an hindex of 42, co-authored 270 publications receiving 5375 citations. Previous affiliations of Michael D. Todd include University of California & WellDynamics.
Papers published on a yearly basis
TL;DR: Some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes are defined.
Abstract: This paper reviews the development of energy harvesting for low-power embedded structural health monitoring (SHM) sensing systems. A statistical pattern recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized followed by a discussion of SHM sensor network paradigms, power requirements for these networks, and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes. Finally, it is noted that many of the technologies discussed herein are applicable to powering any type of low-power embedded sensing system regardless of the application.
TL;DR: A novel approach for optimal sensor and/or actuator placement for structural health monitoring (SHM) applications by implementing an appropriate statistical model of the wave propagation and feature extraction process within a detection theory framework.
Abstract: This paper introduces a novel approach for optimal sensor and/or actuator placement for structural health monitoring (SHM) applications. Starting from a general formulation of Bayes risk, we derive a global optimality criterion within a detection theory framework. The optimal configuration is then established as the one that minimizes the expected total presence of either type I or type II error during the damage detection process. While the approach is suitable for many sensing/actuation SHM processes, we focus on the example of active sensing using guided ultrasonic waves by implementing an appropriate statistical model of the wave propagation and feature extraction process. This example implements both pulse-echo and pitch-catch actuation schemes and takes into account line-of-site visibility and non-uniform damage probabilities over the monitored structure. The optimization space is searched using a genetic algorithm with a time-varying mutation rate. We provide three actuator/sensor placement test problems and discuss the optimal solutions generated by the algorithm.
TL;DR: In this article, a mobile-host based wireless energy transmission system is proposed to provide both power and data interrogation commands to sensor nodes for structural health monitoring (SHM) applications.
Abstract: A major challenge impeding the deployment of wireless sensor networks for structural health monitoring (SHM) is developing a means to supply power to the sensor nodes in an efficient manner. In this paper, we explore possible solutions to this challenge by using a mobile-host based wireless energy transmission system to provide both power and data interrogation commands to sensor nodes. The mobile host features the capability of wirelessly transmitting energy to sensor nodes on an as-needed basis. In addition, it serves as a central data repository and processing center for the data collected from the sensing network. The wirelessly transmitted microwave energy is captured by a receiving antenna, transformed into DC power by a rectifying circuit, and stored in a storage medium to provide the required energy to the sensor node. The application of wireless energy transmission is targeted toward SHM sensor nodes that have been recently developed by the authors, which can be used to collect peak mechanical displacements or piezoelectric impedance measurements. This paper will describe considerations needed to design such energy transmission systems, experimental procedure and results, method of increasing the efficiency, energy conditioning circuits and storage medium, and target applications. Experimental results from a field test on the Alamosa Canyon Bridge in southern New Mexico will also be presented.
TL;DR: This paper developed a wireless impedance sensor node equipped with a low-cost integrated circuit chip that can measure and record the electrical impedance of a piezoelectric transducer, a microcontroller that performs local computing and a wireless telemetry module that transmits the structural information to a base station.
Abstract: This paper presents the development and application of a miniaturized impedance sensor node for structural health monitoring (SHM). A large amount of research has been focused on utilizing the impedance method for structural health monitoring. The vast majority of this research, however, has required the use of expensive and bulky impedance analyzers that are not suitable for field deployment. In this study, we developed a wireless impedance sensor node equipped with a low-cost integrated circuit chip that can measure and record the electrical impedance of a piezoelectric transducer, a microcontroller that performs local computing and a wireless telemetry module that transmits the structural information to a base station. The performance of this miniaturized and portable device has been compared to results obtained with a conventional impedance analyzer and its effectiveness has been demonstrated in an experiment to detect loss of preload in a bolted joint. Furthermore, for the first time, we also consider the problem of wireless powering of such SHM sensor nodes, where we use radio-frequency wireless energy transmission to deliver electrical energy to power the sensor node. In this way, the sensor node does not have to rely on an on-board power source, and the required energy can be wirelessly delivered as needed by human or a remotely controlled robotic device.
TL;DR: In this article, the authors provide a review of examples from nonlinear dynamical systems theory and nonlinear system identification techniques that are used for the feature extraction portion of the damage detection process.
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 a review of examples from nonlinear dynamical systems theory and from nonlinear system identification techniques that are used for the feature-extraction portion of the damage detection process. This paper is not intended as a comprehensive review of all damage detection methods rooted in nonlinear dynamics, but rather to provide a number of illustrations of complimentary approaches where damage-sensitive data features are based on nonlinear system response. These features, in turn, can either be used as a direct diagnosis of damage or as input to statistical damage classifier. Copyright © 2007 John Wiley & Sons, Ltd.
01 Dec 1989
TL;DR: Technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner and the historical overview and summarizing the SPR paradigm are provided.
Abstract: This introduction begins with a brief history of SHM technology development. Recent research has begun to recognise that a productive approach to the Structural Health Monitoring (SHM) problem is to regard it as one of statistical pattern recognition (SPR); a paradigm addressing the problem in such a way is described in detail herein as it forms the basis for the organisation of this book. In the process of providing the historical overview and summarising the SPR paradigm, the subsequent chapters in this book are cited in an effort to show how they fit into this overview of SHM. In the conclusions are stated a number of technical challenges that the authors believe must be addressed if SHM is to gain wider acceptance.
01 Mar 1995
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
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)
07 Apr 2002
TL;DR: An updated review covering the years 1996 2001 will summarize the outcome of an updated review of the structural health monitoring literature, finding that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition.
Abstract: Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.
TL;DR: This paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development ofDTs, and the major DT applications in industry and outlines the current challenges and some possible directions for future work.
Abstract: Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.