A tutorial review on time-frequency analysis of non-stationary vibration signals with nonlinear dynamics applications

Time-frequency analysis (TFA) for mechanical vibrations in non-stationary operations is the main subject of this article, concisely written to be an introducing tutorial comparing different time-frequency techniques for non-stationary signals. The theory was carefully exposed and complemented with sample applications on mechanical vibrations and nonlinear dynamics. A particular phenomenon that is also observed in non-stationary systems is the Sommerfeld effect, which occurs due to the interaction between a non-ideal energy source and a mechanical system. An application through TFA for the characterization of the Sommerfeld effect is presented. The techniques presented in this article are applied in synthetic and experimental signals of mechanical systems, but the techniques presented can also be used in the most diverse applications and also in the numerical solution of differential equation

Characterization of damage evolution on metallic components using ultrasonic non-destructive methods

When fatigue is considered, it is expected that structures and machinery eventually fail. Still, when this damage is unexpected, besides of the negative economic impact that it produces, life of people could be potentially at risk. Thus, nowadays it is imperative that the infrastructure managers, ought to program regular inspection and maintenance for their assets; in addition, designers and materials manufacturers, can access to appropriate diagnostic tools in order to build superior and more reliable materials. In this regard, and for a number of applications, non-destructive evaluation techniques have proven to be an efficient and helpful alternative to traditional destructive assays of materials. Particularly, for the design area of materials, in recent times researchers have exploited the Acoustic Emission (AE) phenomenon as an additional assessing tool with which characterize the mechanical properties of specimens. Nevertheless, several challenges arise when treat said phenomenon, since its intensity, duration and arrival behavior is essentially stochastic for traditional signal processing means, leading to inaccuracies for the outcome assessment. In this dissertation, efforts are focused on assisting in the characterization of the mechanical properties of advanced high strength steels during under uniaxial tensile tests. Particularly of interest, is being able to detect the nucleation and growth of a crack throughout said test. Therefore, the resulting AE waves generated by the specimen during the test are assessed with the aim of characterize their evolution. For this, on the introduction, a brief review about non-destructive methods emphasizing the AE phenomenon is introduced. Next is presented, an exhaustive analysis with regard to the challenge and deficiencies of detecting and segmenting each AE event over a continuous data-stream with the traditional threshold detection method, and additionally, with current state of the art methods. Following, a novel AE event detection method is proposed, with the aim of overcome the aforementioned limitations. Evidence showed that the proposed method (which is based on the short-time features of the waveform of the AE signal), excels the detection capabilities of current state of the art methods, when onset and endtime precision, as well as when quality of detection and computational speed are also considered. Finally, a methodology aimed to analyze the frequency spectrum evolution of the AE phenomenon during the tensile test, is proposed. Results indicate that it is feasible to correlate nucleation and growth of a crack with the frequency content evolution of AE events.Cuando se considera la fatiga de los materiales, se espera que eventualmente las estructuras y las maquinarias fallen. Sin embargo, cuando este daño es inesperado, además del impacto económico que este produce, la vida de las personas podría estar potencialmente en riesgo. Por lo que hoy en día, es imperativo que los administradores de las infraestructuras deban programar evaluaciones y mantenimientos de manera regular para sus activos. De igual manera, los diseñadores y fabricantes de materiales deberían de poseer herramientas de diagnóstico apropiadas con el propósito de obtener mejores y más confiables materiales. En este sentido, y para un amplio número de aplicaciones, las técnicas de evaluación no destructivas han demostrado ser una útil y eficiente alternativa a los ensayos destructivos tradicionales de materiales. De manera particular, en el área de diseño de materiales, recientemente los investigadores han aprovechado el fenómeno de Emisión Acústica (EA) como una herramienta complementaria de evaluación, con la cual poder caracterizar las propiedades mecánicas de los especímenes. No obstante, una multitud de desafíos emergen al tratar dicho fenómeno, ya que el comportamiento de su intensidad, duración y aparición es esencialmente estocástico desde el punto de vista del procesado de señales tradicional, conllevando a resultados imprecisos de las evaluaciones. Esta disertación se enfoca en colaborar en la caracterización de las propiedades mecánicas de Aceros Avanzados de Alta Resistencia (AAAR), para ensayos de tracción de tensión uniaxiales, con énfasis particular en la detección de fatiga, esto es la nucleación y generación de grietas en dichos componentes metálicos. Para ello, las ondas mecánicas de EA que estos especímenes generan durante los ensayos, son estudiadas con el objetivo de caracterizar su evolución. En la introducción de este documento, se presenta una breve revisión acerca de los métodos existentes no destructivos con énfasis particular al fenómeno de EA. A continuación, se muestra un análisis exhaustivo respecto a los desafíos para la detección de eventos de EA y las y deficiencias del método tradicional de detección; de manera adicional se evalúa el desempeño de los métodos actuales de detección de EA pertenecientes al estado del arte. Después, con el objetivo de superar las limitaciones presentadas por el método tradicional, se propone un nuevo método de detección de actividad de EA; la evidencia demuestra que el método propuesto (basado en el análisis en tiempo corto de la forma de onda), supera las capacidades de detección de los métodos pertenecientes al estado del arte, cuando se evalúa la precisión de la detección de la llegada y conclusión de las ondas de EA; además de, cuando también se consideran la calidad de detección de eventos y la velocidad de cálculo. Finalmente, se propone una metodología con el propósito de evaluar la evolución de la energía del espectro frecuencial del fenómeno de EA durante un ensayo de tracción; los resultados demuestran que es posible correlacionar el contenido de dicha evolución frecuencial con respecto a la nucleación y crecimiento de grietas en AAAR's.Postprint (published version

Modeling Spectral–Temporal Data From Point Source Events

In recent years, a great deal of effort has been invested in developing sensors to detect, locate, and identify “energetic” electromagnetic events. When observed through one type of imaging spectrometer, these events produce a data record that contains complete spectral and temporal information over the event’s evolution. This article describes the development of a statistical model for the data produced by a particular spectral–temporal sensor. While the application is unique in some ways, this approach to model building may be useful in other related contexts. Several plots, estimated parameters, and some additional details for an equation are provided in the Appendix which is available as supplementary material online

Data-driven modal decomposition methods as feature detection techniques for flow problems: a critical assessment

Modal decomposition techniques are showing a fast growth in popularity for their good properties as data-driven tools. There are several modal decomposition techniques, yet Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) are considered the most demanded methods, especially in the field of fluid dynamics. Following their magnificent performance on various applications in several fields, numerous extensions of these techniques have been developed. In this work we present an ambitious review comparing eight different modal decomposition techniques, including most established methods: POD, DMD and Fast Fourier Trasform (FFT), extensions of these classical methods: based on time embedding systems, Spectral POD (SPOD) and Higher Order DMD (HODMD), based on scales separation, multi-scale POD (mPOD), multi-resolution DMD (mrDMD), and based on the properties of the resolvent operator, the data-driven Resolvent Analysis (RA). The performance of all these techniques will be evaluated on three different testcases: the laminar wake around cylinder, a turbulent jet flow, and the three dimensional wake around cylinder in transient regime. First, we show a comparison between the performance of the eight modal decomposition techniques when the datasets are shortened. Next, all the results obtained will be explained in details, showing both the conveniences and inconveniences of all the methods under investigation depending on the type of application and the final goal (reconstruction or identification of the flow physics). In this contribution we aim on giving a -- as fair as possible -- comparison of all the techniques investigated. To the authors knowledge, this is the first time a review paper gathering all this techniques have been produced, clarifying to the community what is the best technique to use for each application

Energetics and activation of the central nervous system by in vivo nuclear magnetic resonance

Stefano Pascarella, Mario Compiani, Staderin

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Towards Laser Driven Hadron Cancer Radiotherapy: A Review of Progress

It has been known for about sixty years that proton and heavy ion therapy is a very powerful radiation procedure for treating tumours. It has an innate ability to irradiate tumours with greater doses and spatial selectivity compared with electron and photon therapy and hence is a tissue sparing procedure. For more than twenty years powerful lasers have generated high energy beams of protons and heavy ions and hence it has been frequently speculated that lasers could be used as an alternative to RF accelerators to produce the particle beams necessary for cancer therapy. The present paper reviews the progress made towards laser driven hadron cancer therapy and what has still to be accomplished to realise its inherent enormous potential.Comment: 40 pages, 24 figure