Bio: Mark Seaver is an academic researcher from United States Naval Research Laboratory. The author has contributed to research in topics: Structural health monitoring & Attractor. The author has an hindex of 23, co-authored 71 publications receiving 1480 citations.
Papers published on a yearly basis
TL;DR: This work explores the utility of attractor-based approaches in the field of vibration-based structural health monitoring by driving the structure directly with the output of a chaotic oscillator using the Kaplan-Yorke conjecture.
Abstract: This work explores the utility of attractor-based approaches in the field of vibration-based structural health monitoring The technique utilizes the unique properties of chaotic signals by driving the structure directly with the output of a chaotic oscillator Using the Kaplan-Yorke conjecture, the Lyapunov exponents of the driving signal may be tuned to the dominant eigenvalues of the structure, thus controlling the dimension of the structural response Data are collected at various stages of structural degradation and a simple nonlinear model, constructed from the undamaged data, is used to make predictions for the damaged response data Prediction error is then introduced as a "feature" for classifying the magnitude of the damage Results are presented for an experimental cantilevered beam instrumented with fiber-optic strain sensors
TL;DR: In this paper, an instrument was developed to monitor the biological fraction of an aerosol, which simultaneously sizes individual particles in a flowing air stream and measures their total fluorescence following excitation at 266 nm.
Abstract: This paper describes an instrument developed to monitor the biological fraction of an aerosol. The instrument simultaneously sizes individual particles in a flowing air stream and measures their total fluorescence following excitation at 266 nm. Laboratory data show that these two parameters enable discrimination between individuals of certain bacterial species. Field measurements are presented in which bacterial samples were aerosolized and subsequently detected 800 m downwind.
TL;DR: In this paper, a structural model of a rectangular steel plate is considered, where damage is represented as a cut in the plate, starting at one edge and extending from 0% to 25% of the plate width in 5% increments.
Abstract: Recurrence-quantification analysis (RQA) has emerged as a useful tool for detecting subtle non-stationarities and/or changes in time-series data. Here, we extend the RQA analysis methods to multivariate observations and present a method by which the “length scale” parameter e (the only parameter required for RQA) may be selected. We then apply the technique to the difficult engineering problem of damage detection. The structure considered is a finite element model of a rectangular steel plate where damage is represented as a cut in the plate, starting at one edge and extending from 0% to 25% of the plate width in 5% increments. Time series, recorded at nine separate locations on the structure, are used to reconstruct the phase space of the system's dynamics and subsequently generate the multivariate recurrence (and cross-recurrence) plots. Multivariate RQA is then used to detect damage-induced changes to the structural dynamics. These results are then compared with shifts in the plate's natural frequencies. Two of the RQA-based features are found to be more sensitive to damage than are the plate's frequencies.
TL;DR: In this article, a multivariate time delay embedding is used to reconstruct the dynamical attractor of a composite beam, bolted at either end to steel plates to detect both the presence and magnitude of damage to the connection.
Abstract: In this work, recent advances in the use of nonlinear time-series analysis for structural health monitoring are extended to incorporate multivariate data. Structural response data recorded at multiple locations are combined using a multivariate time delay embedding in order to reconstruct the structure's dynamical attractor. Using this approach, a global phase-space representation of the dynamics may be realized for spatially extended systems. A new attractor-based metric, chaotic amplification of attractor distortion (CAAD), is then introduced as a damage sensitive feature. The approach is implemented using data acquired from a composite beam, bolted at either end to steel plates. Degradation to the system is introduced as a loosening of the bolts at one end of the structure. Results based on multivariate attractor reconstruction show a clear ability to detect both the presence and magnitude of damage to the connection. Comparisons are then drawn between this approach and one where the same feature is extracted from attractors reconstructed using data acquired from the individual sensor locations. These features are combined “post-extraction” using a linear discriminant coordinant analysis. Performing the analysis separately at the individual sensor locations results in a significant reduction in discriminating power.
TL;DR: Two applications using dominant current methods for fibre Bragg grating wavelength interrogation are described: hull loads monitoring on an all-composite fast patrol boat and bolt pre-load loss monitoring in a composite beam in conjunction with a state-space modelling data analysis technique.
Abstract: This work first considers a review of the dominant current methods for fibre Bragg grating wavelength interrogation. These methods include WDM interferometry, tunable filter (both Fabry–Perot and acousto-optic) demultiplexing, CCD/prism technique and a newer hybrid method utilizing Fabry–Perot and interferometric techniques. Two applications using these techniques are described: hull loads monitoring on an allcomposite fast patrol boat and bolt pre-load loss monitoring in a composite beam in conjunction with a state-space modelling data analysis technique.
TL;DR: The aim of this work is to provide the readers with the know how for the application of recurrence plot based methods in their own field of research, and detail the analysis of data and indicate possible difficulties and pitfalls.
Abstract: Recurrence is a fundamental property of dynamical systems, which can be exploited to characterise the system's behaviour in phase space. A powerful tool for their visualisation and analysis called recurrence plot was introduced in the late 1980's. This report is a comprehensive overview covering recurrence based methods and their applications with an emphasis on recent developments. After a brief outline of the theory of recurrences, the basic idea of the recurrence plot with its variations is presented. This includes the quantification of recurrence plots, like the recurrence quantification analysis, which is highly effective to detect, e. g., transitions in the dynamics of systems from time series. A main point is how to link recurrences to dynamical invariants and unstable periodic orbits. This and further evidence suggest that recurrences contain all relevant information about a system's behaviour. As the respective phase spaces of two systems change due to coupling, recurrence plots allow studying and quantifying their interaction. This fact also provides us with a sensitive tool for the study of synchronisation of complex systems. In the last part of the report several applications of recurrence plots in economy, physiology, neuroscience, earth sciences, astrophysics and engineering are shown. The aim of this work is to provide the readers with the know how for the application of recurrence plot based methods in their own field of research. We therefore detail the analysis of data and indicate possible difficulties and pitfalls.
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.
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: A review of the current knowledge on major categories of primary biological aerosol particles (PBAP): bacteria and archaea, fungal spores and fragments, pollen, viruses, algae and cyanobacteria, biological crusts and lichens and others like plant or animal fragments and detritus is presented in this article.
Abstract: Atmospheric aerosol particles of biological origin are a very diverse group of biological materials and structures, including microorganisms, dispersal units, fragments and excretions of biological organisms. In recent years, the impact of biological aerosol particles on atmospheric processes has been studied with increasing intensity, and a wealth of new information and insights has been gained. This review outlines the current knowledge on major categories of primary biological aerosol particles (PBAP): bacteria and archaea, fungal spores and fragments, pollen, viruses, algae and cyanobacteria, biological crusts and lichens and others like plant or animal fragments and detritus. We give an overview of sampling methods and physical, chemical and biological techniques for PBAP analysis (cultivation, microscopy, DNA/RNA analysis, chemical tracers, optical and mass spectrometry, etc.). Moreover, we address and summarise the current understanding and open questions concerning the influence of PBAP on the atmosphere and climate, i.e. their optical properties and their ability to act as ice nuclei (IN) or cloud condensation nuclei (CCN). We suggest that the following research activities should be pursued in future studies of atmospheric biological aerosol particles: (1) develop efficient and reliable analytical techniques for the identification and quantification of PBAP; (2) apply advanced and standardised techniques to determine the abundance and diversity of PBAP and their seasonal variation at regional and global scales (atmospheric biogeography); (3) determine the emission rates, optical properties, IN and CCN activity of PBAP in field measurements and laboratory experiments; (4) use field and laboratory data to constrain numerical models of atmospheric transport, transformation and climate effects of PBAP. Keywords: primary biological atmospheric aerosol; climate; cloud condensation nuclei; biology; atmospheric ice nuclei (Published: 22 February 2012) Citation: Tellus B 2012, 64 , 15598, DOI: 10.3402/tellusb.v64i0.15598
TL;DR: In this article, a review of the past and recent developments in system identification of nonlinear dynamical structures is presented, highlighting their assets and limitations and identifying future directions in this research area.
Abstract: This survey paper contains a review of the past and recent developments in system identification of nonlinear dynamical structures. The objective is to present some of the popular approaches that have been proposed in the technical literature, to illustrate them using numerical and experimental applications, to highlight their assets and limitations and to identify future directions in this research area. The fundamental differences between linear and nonlinear oscillations are also detailed in a tutorial.