Overview of microgrippers and design of a micro-manipulation station based on a MMOC microgripper

International audienceThis paper deals with an overview of recent microgrippers. As the end-effectors of micromanipulation systems, microgrippers are crucial point of such systems for their efficiency and their reliability. The performances of current microgrippers are presented and offer a stroke extending from 50 m to approximately 2mm and a maximum forces varying from 0,1mN to 600 mN. Then, micromanipulation system based on a piezoelectric microgripper and a SCARA robot is presented

A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling

International audiencePerformances of data-driven approaches are closely related to the form and trend of extracted features (that can be seen as time series health indicators). 1) Even if much of datadriven approaches are suitable to catch non-linearity in signals, features with monotonic trends (which is not always the case!) are likely to lead to better estimates. 2) Also, some classical extracted features do not show variation until a few time before failure occurs, which prevents performing RUL predictions in a timely manner to plan maintenance task. The aim of this paper is to present a novel feature extraction procedure to face with these two problems. Two aspects are considered. Firstly, the paper focuses on feature extraction in a new manner by utilizing trigonometric functions to extract features (health indicators) rather than typical statistic measures like RMS, etc. The proposed approach is applied on time-frequency analysis with Discrete Wavelet Transform (DWT). Secondly, a simple way of building new features based on cumulative functions is also proposed in order to transform time series into descriptors that depict accumulated wear. This approach can be extended to other types of features. The main idea of both developments is to map raw data with monotonic features with early trends, i.e., with descriptors that can be easily predicted. This methodology can enhance prognostics modeling and RUL prediction. The whole proposition is illustrated and discussed thanks to tests performed on vibration datasets from PRONOSTIA, an experimental platform that enables accelerated degradation of bearings

E2GKpro: An evidential evolving multi-modeling approach for system behavior prediction with applications.

International audienceNonlinear dynamical systems identification and behavior prediction are di cult problems encountered in many areas of industrial applications such as fault diagnosis and prognosis. In practice, the analytical description of a nonlinear system directly from observed data is a very challenging task because of the the too large number of the related parameters to be estimated. As a solution, multi-modeling approaches have lately been applied and consist in dividing the operating range of the system under study into di erent operating regions easier to describe by simpler functions to be combined. In order to take into consideration the uncertainty related to the available data as well as the uncertainty resulting from the nonlinearity of the system, evidence theory is of particular interest, because it permits the explicit modeling of doubt and ignorance. In the context of multi-modeling, information of doubt may be exploited to properly segment the data and take into account the uncertainty in the transitions between the operating regions. Recently, the Evidential Evolving Gustafson-Kessel algorithm (E2GK) has been proposed to ensure an online partitioning of the data into clusters that correspond to operating regions. Based on E2GK, a multi-modeling approach called E2GKpro is introduced in this paper, which dynamically performs the estimation of the local models by upgrading and modifying their parameters while data arrive. The proposed algorithm is tested on several datasets and compared to existing approaches. The results show that the use of virtual centroids in E2GKpro account for its robustness to noise and generating less operating regions while ensuring precise predictions

Evidential Evolving Gustafson-Kessel Algortithm (E2GK) and its application to PRONOSTIA's Data Streams Partitioning.

International audienceCondition-based maintenance (CBM) appears to be a key element in modern maintenance practice. Research in diagnosis and prognosis, two important aspects of a CBM program, is growing rapidly and many studies are conducted in research laboratories to develop models, algorithms and technologies for data processing. In this context, we present a new evolving clustering algorithm developed for prognostics perspectives. E2GK (Evidential Evolving Gustafson-Kessel) is an online clustering method in the theoretical framework of belief functions. The algorithm enables an online partitioning of data streams based on two existing and efficient algorithms: Evidantial c-Means (ECM) and Evolving Gustafson-Kessel (EGK). To validate and illustrate the results of E2GK, we use a dataset provided by an original platform called PRONOSTIA dedicated to prognostics applications

Evidential Evolving Gustafson-Kessel Algorithm (E2GK) and its application to PRONOSTIA's Data Streams Partitioning.

International audienceCondition-based maintenance (CBM) appears to be a key element in modern maintenance practice. Research in diagnosis and prognosis, two important aspects of a CBM program, is growing rapidly and many studies are conducted in research laboratories to develop models, algorithms and technologies for data processing. In this context, we present a new evolving clustering algorithm developed for prognostics perspectives. E2GK (Evidential Evolving Gustafson-Kessel) is an online clustering method in the theoretical framework of belief functions. The algorithm enables an online partitioning of data streams based on two existing and efficient algorithms: Evidantial c-Means (ECM) and Evolving Gustafson-Kessel (EGK). To validate and illustrate the results of E2GK, we use a dataset provided by an original platform called PRONOSTIA dedicated to prognostics applications

PRONOSTIA : An experimental platform for bearings accelerated degradation tests.

International audienceThis paper deals with the presentation of an experimental platform called PRONOSTIA, which enables testing, verifying and validating methods related to bearing health assessment, diagnostic and prognostic. The choice of bearings is justified by the fact that most of failures of rotating machines are related to these components. Therefore, bearings can be considered as critical as their failure significantly decreases availability and security of machines. The main objective of PRONOSTIA is to provide real data related to accelerated degradation of bearings performed under constant and/or variable operating conditions, which are online controlled. The operating conditions are characterized by two sensors: a rotating speed sensor and a force sensor. In PRONOSTIA platform, the bearing's health monitoring is ensured by gathering online two types of signals: temperature and vibration (horizontal and vertical accelerometers). Furthermore, the data are recorded with a specific sampling frequency which allows catching all the frequency spectrum of the bearing during its whole degradation. Finally, the monitoring data provided by the sensors can be used for further processing in order to extract relevant features and continuously assess the health condition of the bearing. During the PHM conference, a "IEEE PHM 2012 Prognostic Challenge" is organized. For this purpose, a web link to the degradation data is provided to the competitors to allow them testing and verifying their prognostic methods. The results of each method can then be evaluated regarding its capability to accurately estimate the remaining useful life of the tested bearings

Capteurs mobiles en réseau ZigBee dans le contexte de la e-maintenance. Plateforme communicante sans fil par réseau de capteurs embarqués : application de supervision dédiée à la e-maintenance.

L'atelier de Productique et la plateforme de e-maintenance du département AS2M de l'institut FEMTO-ST à Besançon, constituent une vitrine technologique propice au développement et à la mise en place d'un réseau de capteurs mobiles et d'actionneurs sans fil, dans un contexte d'enseignement, de recherche et de transfert de technologies. L'état de l'art réalisé, allié aux impératifs dictés par la volonté de mettre en place une solution industrielle, a conduit à retenir le récent standard ZigBee pour fixer le coeur de notre système. La conception, le développement et la réalisation de modules électroniques fonctionnels ont permis la mise en place d'un réseau de capteurs maillé, dont la compacité, la mobilité et l'autonomie énergétique sont en corrélation avec les flux des chaînes de productions industrielles. L'étude, l'adaptation aux besoins et la mise en oeuvre de la pile logicielle ZigBee Microchip constituent l'ultime phase de la réalisation de ce projet, dont les contours ne sont pas simplement restreints à une problématique purement électronique, mais plus généralement au projet de rénovation de cette plateforme de Productique

Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics.

International audience—Performances of data-driven prognostics approaches are closely dependent on form, and trend of extracted features. Indeed, features that clearly reflect the machine degradation, should lead to accurate prognostics, which is the global objective of the paper. This paper contributes a new approach for features extraction / selection: the extraction is based on trigonomet-ric functions and cumulative transformation, and the selection is performed by evaluating feature fitness using monotonicity and trendability characteristics. The proposition is applied to time-frequency analysis of non-stationary signals using Discrete Wavelet Transform. The main idea is to map raw vibration data into monotonic features with early trends, which can be easily predicted. To show that, selected features are used to build a model with a data-driven approach namely, the Summation Wavelet-Extreme Learning Machine, that enables a good balance between model accuracy and complexity. For validation and generalization purpose, vibration data from two real applications of Prognostics and Health Management challenges are used: 1) cutting tools from Computer Numerical Control (CNC) machine (2010), and 2) bearings from platform PRONOSTIA (2012). Performances of the proposed approach are thoroughly compared with the classical approach by performing: feature fitness analysis, cutting tool wear " estimation " and bearings " long-term predictions " tasks, which validates the proposition