Structural health monitoring by combining machine learning and dimensionality reduction techniques

Article number 20International audienceStructural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights

Design and validation of a methodology for wind energy structures health monitoring

L’objectiu de la Monitorització de la salut estructural (SHM) és la verificació de l’estat o la salut de les estructures per tal de garantir el seu correcte funcionament i estalviar en el cost de manteniment. El sistema SHM combina una xarxa de sensors connectada a l’estructura amb monitoratge continu i algoritmes específics. Es deriven diferents beneficis de l’aplicació de SHM, on trobem: coneixement sobre el comportament de l’estructura sota diferents operacions i diferents càrregues ambientals , el coneixement de l’estat actual per tal de verificar la integritat de l’estructura i determinar si una estructura pot funcionar correctament o si necessita manteniment o substitució i, per tant, reduint els costos de manteniment. El paradigma de la detecció de danys es pot abordar com un problema de reconeixement de patrons (comparació entre les dades recollides de l’estructura sense danys i l’estructura actual, per tal de determinar si hi ha algun canvi) . Hi ha moltes tècniques que poden gestionar el problema. En aquest treball s’utilitzen les dades dels acceleròmetres per desenvolupar aproximacions estadístiques utilitzant dades en temps per a la detecció dels danys en les estructures. La metodologia s’ha dissenyat per a una turbina eòlica off - shore i només s’utilitzen les dades de sortida per detectar els danys. L’excitació de la turbina de vent és induïda pel vent o per les ones del mar. La detecció de danys no és només la comparació de les dades. S’ha dissenyat una metodologia completa per a la detecció de danys en aquest treball. Gestiona dades estructurals, selecciona les dades adequades per detectar danys, i després de tenir en compte les condicions ambientals i operacionals (EOC) en el qual l’estructura està treballant, es detecta el dany mitjançant el reconeixement de patrons. Quan es parla del paradigma de la detecció de danys sempre s’ha de tenir en compte si els sensors estan funcionant correctament. Per això és molt important comptar amb una metodologia que comprova si els sensors estan sans. En aquest treball s’ha aplicat un mètode per detectar els sensors danyats i s’ha insertat en la metodologia de detecció de danys.The objective of Structural Health Monitoring (SHM) is the verification of the state or the health of the structures in order to ensure their proper performance and save on maintenance costs. The SHM system combines a sensor network attached to the structure with continuous monitoring and specific, proprietary algorithms. Different benefits are derived from the implementation of SHM, some of them are: knowledge about the behavior of the structure under different loads and different environmental changes, knowledge of the current state in order to verify the integrity of the structure and determine whether a structure can work properly or whether it needs to be maintained or replaced and, therefore, reduce maintenance costs. The paradigm of damage detection can be tackled as a pattern recognition problem (comparison between the data collected from the structure without damages and the current structure in order to determine if there are any changes). There are lots of techniques that can handle the problem. In this work, accelerometer data is used to develop statistical data driven approaches for the detection of damages in structures. As the methodology is designed for wind turbines, only the output data is used to detect damage; the excitation of the wind turbine is provided by the wind itself or by the sea waves, being those unknown and unpredictable. The damage detection strategy is not only based on the comparison of many data. A complete methodology for damage detection based on pattern recognition has been designed for this work. It handles structural data, selects the proper data for detecting damage and besides, considers the Environmental and Operational Conditions (EOC) in which the structure is operating. The damage detection methodology should always be accessed only if there is a way to probe that the sensors are correctly working. For this reason, it is very important to have a methodology that checks whether the sensors are healthy. In this work a method to detect the damaged sensors has been also implemented and embedded into the damage detection methodology.El objetivo de la Monitorización de la salud estructural (SHM) es la verificación del estado o la salud de las estructuras con el fin de garantizar su correcto funcionamiento y ahorrar en el costo de mantenimiento. El sistema SHM combina una red de sensores conectada a la estructura con monitorización continua y algoritmos específicos. Se derivan diferentes beneficios de la aplicación de SHM, donde encontramos: conocimiento sobre el comportamiento de la estructura bajo diferentes operaciones y diferentes cargas ambientales, el conocimiento del estado actual con el fin de verificar la integridad de la estructura y determinar si una estructura puede funcionar correctamente o si necesita mantenimiento o sustitución y, por lo tanto, reduciendo los costes de mantenimiento. El paradigma de la detección de daños se puede abordar como un problema de reconocimiento de patrones (comparación entre los datos recogidos de la estructura sin daños y la estructura actual, con el fin de determinar si hay algún cambio). Hay muchas técnicas que pueden manejar el problema. En este trabajo se utilizan los datos de los acelerómetros para desarrollar aproximaciones estadísticas utilizando datos en tiempo para la detección de los daños en las estructuras. La metodología se ha diseñado para una turbina eólica off-shore y sólo se utilizan los datos de salida para detectar los daños. La excitación de la turbina de viento es inducida por el viento o por las olas del mar. La detección de daños no es sólo la comparación de los datos. Se ha diseñado una metodología completa para la detección de daños en este trabajo. Gestiona datos estructurales, selecciona los datos adecuados para detectar daños, y después de tener en cuenta las condiciones ambientales y operacionales (EOC) en el que la estructura está trabajando, se detecta el daño mediante el reconocimiento de patrones. Cuando se habla del paradigma de la detección de daños siempre se debe tener en cuenta si los sensores están funcionando correctamente. Por eso es muy importante contar con una metodología que comprueba si los sensores están sanos. En este trabajo se ha aplicado un método para detectar los sensores dañados y se ha metido en la metodología de detección de dañosPostprint (published version

Information Bottleneck

The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence

Experimenting with Constraint Programming Techniques in Artificial Intelligence: Automated System Design and Verification of Neural Networks

This thesis focuses on the application of Constraint Satisfaction and Optimization techniques in two Artificial Intelligence (AI) domains: automated design of elevator systems and verification of Neural Networks (NNs). The three main areas of interest for my work are (i) the languages for defining the constraints for the systems, (ii) the algorithms and encodings that enable solving the problems considered and (iii) the tools that implement such algorithms. Given the expressivity of the domain description languages and the availability of effective tools, several problems in diverse application fields have been solved successfully using constraint satisfaction techniques. The two case studies herewith presented are no exception, even if they entail different challenges in the adoption of such techniques. Automated design of elevator systems not only requires encoding of feasibility (hard) constraints, but should also take into account design preferences, which can be expressed in terms of cost functions whose optimal or near-optimal value characterizes “good” design choices versus “poor” ones. Verification of NNs (and other machine-learned implements) requires solving large-scale constraint problems which may become the main bottlenecks in the overall verification procedure. This thesis proposes some ideas for tackling such challenges, including encoding techniques for automated design problems and new algorithms for handling the optimization problems arising from verification of NNs. The proposed algorithms and techniques are evaluated experimentally by developing tools that are made available to the research community for further evaluation and improvement

Identification and Characterization of electrical patterns underlying stereotyped behaviours in the semi-intact leech

Neuroscience aims at understanding the mechanisms underlying perception, learning, memory, consciousness and acts. The present Ph.D. thesis aims to elucidate some principles controlling actions, which in a more scientific and technical language is referred to as motor control. This concept has been studied in a variety of preparations in vertebrate and invertebrate species. In this PhD thesis, the leech has been the subject of choice, because it is a well known preparation, highly suitable for relating functional and behavioural properties to the underlying neuronal networks. The semi-intact leech preparation (Kristan et al., 1974) has been the main methodological strategy performed in the experiments. Its importance lies in the fact that it gives the possibility to access the information from the leech\u2019s central nervous system (CNS) and compare simultaneously some stereotyped behaviours. Thus, entering in this work it is necessary to make a brief summary of the steps followed before arriving to the conclusions written ahead. The main objective followed in this work has been the analysis, identification and characterization of electrical patterns underlying different behaviours in Hirudo medicinalis. This main objective has been reached focusing the project on three particular objectives, which have been pursued during the author\u2019s Philosophical Doctorate course

MAPPING LOW-FREQUENCY FIELD POTENTIALS IN BRAIN CIRCUITS WITH HIGH-RESOLUTION CMOS ELECTRODE ARRAY RECORDINGS

Neurotechnologies based on microelectronic active electrode array devices are on the way to provide the capability to record electrophysiological neural activity from a thousands of closely spaced microelectrodes. This generates increasing volumes of experimental data to be analyzed, but also offers the unprecedented opportunity to observe bioelectrical signals at high spatial and temporal resolutions in large portions of brain circuits. The overall aim of this PhD was to study the application of high-resolution CMOS-based electrode arrays (CMOS-MEAs) for electrophysiological experiments and to investigate computational methods adapted to the analysis of the electrophysiological data generated by these devices. A large part of my work was carried out on cortico-hippocampal brain slices by focusing on the hippocampal circuit. In the history of neuroscience, a major technological advance for hippocampal research, and also for the field of neurobiology, was the development of the in vitro hippocampal slice preparation. Neurobiological principles that have been discovered from work on in vitro hippocampal preparations include, for instance, the identification of excitatory and inhibitory synapses and their localization, the characterization of transmitters and receptors, the discovery of long-term potentiation (LTP) and long-term depression (LTD) and the study of oscillations in neuronal networks. In this context, an initial aim of my work was to optimize the preparation and maintenance of acute cortico-hippocampal brain slices on planar CMOS-MEAs. At first, I focused on experimental methods and computational data analysis tools for drug-screening applications based on LTP quantifications. Although the majority of standard protocols still use two electrodes platforms for quantifying LTP, in my PhD I investigate the potential advantages of recording the electrical activity from many electrodes to spatiotemporally characterized electrically induced responses. This work also involved the collaboration with 3Brain AG and a CRO involved in drug-testing, and led to a software tool that was licensed for developing its exploitation. In a second part of my work I focused on exploiting the recording resolution of planar CMOS-MEAs to study the generation of sharp wave ripples (SPW-Rs) in the hippocampal circuit. This research activity was carried out also by visiting the laboratory of Prof. A. Sirota (Ludwig Maximilians University, Munich). In addition to set-up the experimental conditions to record SPW-Rs from planar CMOS-MEAs integrating 4096 microelectrodes, I also explored the implementation of a data analysis pipeline to identify spatiotemporal features that might characterize different type of in-vitro generated SPW-R events. Finally, I also contributed to the initial implementation of high-density implantable CMOS-probes for in-vivo electrophysiology with the aim of evaluating in vivo the algorithms that I developed and investigated on brain slices. With this aim, in the last period of my PhD I worked on the development of a Graphical User Interface for controlling active dense CMOS probes (or SiNAPS probes) under development in our laboratory. I participated to preliminary experimental recordings using 4-shank CMOS-probes featuring 1024 simultaneously recording electrodes and I contributed to the development of a software interface for executing these experiments