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ORION-AE: Multisensor acoustic emission datasets reflecting supervised untightening of bolts in a jointed vibrating structure
No full textExperiments were designed to reproduce the loosening phenomenon observed in aeronautics, automotive or civil engineering structures where parts are assembled together by means of bolted joints. The bolts can indeed be subject to self-loosening under vibrations. Therefore, it is of paramount importance to develop sensing strategies and algorithms for early loosening estimation. The test rig was specifically designed to make the vibration tests as repeatable as possible.
The dataset ORION-AE is made of a set of time-series measurements obtained by untightening a bolt with seven different levels. The data have been sampled at 5 MHz on four different sensors, including three permanently attached acoustic emission sensors in contact with the structure, and one laser (contactless) measurement apparatus. This dataset can thus be used for performance benchmarking of supervised, semi-supervised or unsupervised learning algorithms, including deep and transfer learning for time-series data, with possibly seven classes. This dataset may also be useful to challenge denoising methods or wave-picking algorithms, for which the vibrometer measurements can be used for validation.
ORION is a jointed structure made of two plates manufactured in a 2024 aluminium alloy, linked together by three bolts. The contact between the plates is done through machined overlays. The contact patches has an area of 12x12 mm^2 and is 1 mm thick. The structure was submitted to a 100 Hz harmonic excitation force during about 10 seconds. The load was applied using a Tyra electromagnetic shaker, which can deliver a 200 N force. The force was measured using a PCB piezoelectric load cell and the vibration level was determined next to the end of the specimen using a Polytec laser vibrometer.
The ORION-AE dataset is composed of five directories collected in five campaigns denoted as B, C, D, E and F in the sequel. Seven tightening levels were applied on the upper bolt. The tightening was first set to 60 cNm with a torque screwdriver. After a 10 seconds vibration test, the shaker was stopped and this vibration test was repeated after a torque modification at 50 cNm. Then torque modifications at 40, 30, 20, 10 and 5 cNm were applied. Note that, for campaign C, the level 40 cNm is missing.
During each cycle of the vibration test for a given tightening level, different AE sources can generate signals and those sources may be activated or not, depending on the tribological conditions within the contact between the beams which are not controlled. The tightening levels can be used to represent a reference against which clustering or classification results can be compared with. In that case, the main assumption is that the torque remained close to the level which was set at the beginning of every period of 10 s. This assumption can not be checked in the current configuration of the tests.
For each campaign, four sensors were used: a laser vibrometer and three different AE sensors (micro-200-HF, micro-80 and the F50A from Euro-Physical Acoustics) with various frequency bands were attached onto the lower plate (5 cm above the end of the plate). All data were sampled at 5 MHz using a Picoscope 4824 and a preamplifier (from Euro-Physical Acoustics) set to 60 dB. The velocimeter is used for different purposes, in particular to control the amplitude of the displacement of the top of the upper beam so that it remains constant whatever the tightening level.
The sensors are expected to detect the stick-slip transitions or shocks in the interface that are known to generate small AE events during vibrations. The acoustic waves generated by these events are highly dependent on bolt tightening. These sources of AE signals have to be detected and identified from the data stream which constitute the challenge.
Details of the folders and files
There is 1 folder per campaign, each composed of 7 subfolders corresponding to 7 tightening levels: 5 cNm, 10 cNm, 20 cNm, 30 cNm, 40 cNm, 50 cNm, 60 cNm. So, 7 levels are available per campaign, except for campaign C for which 40 cNm is missing.
There is about 10 seconds of continuous recording of data per level (the exact value can be found according to the number of files in each subfolder). The sampling frequency was set to 5 MHZ on all channels of a picoscope 4824 and a preamplifer of 60 dB (model 2/4/6 preamplifier made by Europhysical acoustics). The characteristics of both the picoscope and preamplifier are provided in the enclosed documentation.
Each subfolder is made of .mat files. There is about 1 file per second (depending on the buffering, it can vary a little). The files in a subfolder are named according to the timestamps (time of recording). Each file is composed of vectors of data named:
A = micro80 sensor.
B = F50A sensor.
C = micro200HF sensor.
D = velocimeter.
Note that the measurements are stored in mV.
Sample Matlab codes are provided to read the files provided.
The characteristics of the sensors are provided in the enclosed documentation. <br
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Méthodologies d’analyse de séries temporelles sous incertitudes aléatoires et épistémiques pour le suivi et le pronostic de l’état de systèmes et structures - De l’estimation d’une cinétique d’endommagement à son contrôle
Get PDFIn this habilitation, I propose a synthesis of my research findings on time series analysis methodologies for monitoring equipments, especially rotating machines and composite structures. The illustrations relate to structures at various scales (laboratory, semi-structural), to benchmarks and to industrial applications. After a synthesis of research and teaching activities, three chapters present the state of the art, the methods and the main results obtained. The first chapter describes a state of the art in PHM (Prognostics and Health Management) and SHM (Structural Health Monitoring). Special attention is given to the evolution of PHM / SHM with the advent of Industry 4.0 accompanied by recent developments in both information gathering technologies and data analysis methods. The second chapter is dedicated to the presentation of contributions to PHM. They concern pattern recognition methods for PHM validated by benchmarking or tested on real data from industrial applications. The specificity of the proposed models particularly concerns the use of formalisms for the representation of uncertainties based on the theory of belief functions or computational geometry. The third chapter depicts the contributions for monitoring composites under fatigue loading using acoustic emission. A methodology is proposed taking as starting point the raw acoustic emission streaming coming from multiple sensors arranged on structures. It includes a real-time processing of streaming by wavelets and classification (unsupervised or partially supervised) into acoustic emissions sources. The issue of robust unsupervised classification from massive data is addressed to help materials scientists to select the correct parameterisation of a pattern recognition chain. The post-HDR project is on integrated approaches for SHM using micromachined ultrasonics transducers developed at FEMTO-ST.Dans ce manuscrit d’habilitation à diriger des recherches, je propose une synthèse de mes travaux de recherche de 2008 à 2016, portant sur des méthodologies d’analyse de séries temporelles pour la surveillance d’équipements, en particulier les machines tournantes et les structures composites. En fonction des travaux concernés, les illustrations portent sur des éprouvettes de laboratoire, des pièces semi-structurales, des benchmarks ou encore des applications industrielles dans le cadre de partenariats. Après une synthèse des activités de recherche, d’enseignement et des tâches d’intérêt collectif, trois chapitres permettent de présenter l’état de l’art, la démarche et les principaux résultats obtenus. Le premier chapitre desse un état de l’art des approches de suivi de santé et de pronostic dans les disciplines du PHM (Prognostics and Health Management) et du SHM (Structural Health Monitoring). Une attention particulière est portée à l’évolution du PHM/SHM avec l’avènement de l’industrie 4.0 accompagné par l’évolution récente à la fois des technologies de collecte de l’information et des méthodes d’analyse de données. Le second chapitre est dédié à la présentation des contributions au PHM. Elles reposent sur des modèles de reconnaissance de formes adaptés aux problématiques du PHM validés par benchmarking ou testés sur des données réelles issues d’applications industrielles. La spécificité de ces modèles concerne notamment l’emploi de formalismes pour la représentation des incertitudes basés sur la théorie des fonctions de croyance ou sur la géométrie computationnelle. Le troisième chapitre dépeint les contributions en caractérisation et suivi de santé en fatigue de structures composites basés sur la technique de l’émission acoustique. Une méthodologie est proposée prenant comme point de départ les flux bruts issus de multiples capteurs disposés sur des structures tubulaires. Des traitements temps réel de ces flux permettent l’extraction des formes d’ondes et leur classification (non supervisée ou partiellement supervisée) en sources d’émissions acoustiques. La problématique de la classification non-supervisée robuste est abordée pour aider à la caractérisation des matériaux à partir des données massives issues de cette technique. Un lien avec la mécanique de la rupture et la physique des sources est enfin initié permettant d’alimenter le projet post-HDR sur la nécessité d’approches intégrées pour le SHM. Un ensemble de publications complètent le manuscrit sur lesquelles s’appuient les chapitres précédents et permettant de donner au lecteur une vision détaillée des activités menées pendant la période post-doctorale
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Inference and learning in evidential discrete latent markov models
No full textInternational audienceWe present the Evidential Hidden Markov Model (EvHMM), an extension of standard HMM for time-series modelling whereconditional belief functions are used in place of probabilities to manage uncertainty on discrete latent variables. Inference andlearning mechanisms are described and allow to solve the three problems initially defined for HMM, namely: the classificationproblem (find the most plausible model), the decoding problem (finding the best sequence of hidden states) and the learning problembased on incomplete and uncertain data (estimate the parameters). Exact inference mechanisms based on the Generalized BayesianTheorem are proposed which allows one to recover standard HMM when probabilities are considered. An EM-like procedure isdeveloped for parameter learning, relying on some approximations suggested to make the solutions tractable. Relationships arediscussed with both the learning criterion conjectured by Vannoorenberghe and Smets and the formulation of Evidential MarkovChains by Pieczynski et al. A comparison with standard HMM on simulated data confirms the interest of considering randomdisjuctive sets to represent data incompleteness in evidential temporal graphical models
A solution for the learning problem in Evidential (Partially) Hidden Markov Models based on Conditional Belief Functions and EM
No full textInternational audienceEvidential Hidden Markov Models (EvHMM) is a particularEvidential Temporal Graphical Model that aims at statistically repre-senting the kynetics of a system by means of an Evidential Markov Chainand an observation model. Observation models are made of mixture ofdensities to represent the inherent variability of sensor measurements,whereas uncertainty on the latent structure, that is generally only par-tially known due to lack of knowledge, is managed by Dempster-Shafer'stheory of belief functions. This paper is dedicated to the presentation ofan Expectation-Maximization procedure to learn parameters in EvHMM.Results demonstrate the high potential of this method illustrated oncomplex datasets originating from turbofan engines where the aim is toprovide early warnings of disfunction
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