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Publications de l'équipe DISCO

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18578
20/10/2018

Set membership estimation applied to the localization of small UAS in tight flight formations

J.BOTLING, S.FERGANI

ISAE, DISCO

Manifestation avec acte : International Conference on Control, Automation and Systems ( ICCAS ) 2018 du 17 octobre au 20 octobre 2018, Yong Pyong (Corée), Octobre 2018 , N° 18578

Lien : https://hal.laas.fr/hal-01884592

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Abstract

This paper proposes a set membership approach for UAS (unmanned aerial system)localization in a tight flight formation. A novel set membership estimation strategy based on the typical hardware available for localization (due to the cost constraints put on small UAS for civil applications) is developed. The main idea is as follows: using time-differenced differential GNSS carrier phase observations, the relative position between UAS can be tracked with centimeter-level precision, but affected by an unknown constant meter-level bias due to the initial coarse position standalone position estimate. Using pseudorange observations, as well as UWB range observations, the guaranteed space containing this position bias is determined using dense box particle sampling and sequential purging. The carrier phase trajectory fully captures the dynamics of the UAS motion and enables precise relative position holding from t = 0 on. The proposed set membership filter scheme is fully complementary to and independent of any other algorithm employed to estimate the relative position. simulation results of the problem of cooperative relative lo-calization between UAS on a formation flight benchmark compared to a standard Extended Kalman Filter illustrate the benefits arising from the deterministic nature of set membership filtering. .

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18429
19/09/2018

Diagnostic : étude d’un raisonnement complexe et multi-dimensionnel

Y.PENCOLE

DISCO

Habilitation à diriger des recherches : 19 Septembre 2018, 229p., Président: P.DAGUE, Rapporteurs: A.GRALL, S.HAAR, P.MARQUIS, Examinateurs: M.COMBACAU, Directrice: L.TRAVE-MASSUYES , N° 18429

Lien : https://hal.laas.fr/tel-01984666

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Abstract

Diagnosing is a general reasoning task that aims at identifying the nature of a situation (misbehaviour, illness, problem,..) by interpreting output signs (observations, measurements, alarms). This type of reasoning can be applied in many social and economical domains. This defense will present a synthesis of the contributions for the development of diagnostic agents on a various type of systems (from statical logical-based systems till timed discrete event systems). The way such agents are involved in decision processes such as corrective and predictive maintenance will be also discussed.

Résumé

Le diagnostic est un raisonnement général qui consiste à identifier la nature d'une situation (dysfonctionnement, maladie, problème...) par l'interprétation de signes extérieurs (observations, mesures, alarmes). Par sa nature, ce type de raisonnement peut s’appliquer à de nombreux domaines socio-économiques. Cette soutenance a pour objectif de présenter une synthèse des contributions à la mise en œuvre effective d’agents diagnostiqueurs sur des types de systèmes variés (allant des systèmes logiques statiques à des systèmes à événements discrets temporels) ainsi que de leur intégration dans des systèmes d’aide à la décision, notamment pour la maintenance corrective et prévisionnelle de systèmes.

Mots-Clés / Keywords
Diagnostic; Raisonnement; Systèmes à événements discrets; Systèmes temporels; Maintenance;

145753
18442
14/09/2018

An Ordered Chronicle Discovery Algorithm

A.SAHUGUEDE, E.LE CORRONC, M.V.LE LANN

DISCO

Manifestation avec acte : ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data ( AALDT ) 2018 du 14 septembre au 14 septembre 2018, Dublin (Irelande), Septembre 2018, 8p. , N° 18442

Lien : https://hal.laas.fr/hal-01947338

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Abstract

Chronicles are temporal patterns well suited to capture dynamic process thanks to an event abstraction of the information of interest. Designing chronicles from a journal log is not a trivial task considering the huge amount of data generated by highly-advanced systems. Chronicle discovery is a mean to help expert design chronicles that are representative of a system behavior from direct observations. In this paper, a clustering approach to the chronicle discovery problem is considered. To improve the discovered chronicle quality, an order in the design of interesting pattern is introduced. This allows a better robustness to small perturbations in the input journal log. The efficiency of the ordered chronicle discovery algorithm is evaluated on a real dataset.

145853
18403
31/08/2018

Decentralized Diagnosis via Structural Analysis and Integer Programming

C.PEREZ, E.CHANTHERY, L.TRAVE-MASSUYES, J.SOTOMAYOR, C.ARTIGUES

DISCO, PUCP, ROC

Manifestation avec acte : IFAC International Symposium on Fault Detection Supervision and Safety of Technical Processes ( SAFEPROCESS ) 2018 du 29 août au 31 août 2018, Varsovie (Pologne), Août 2018, 7p. , N° 18403

Lien : https://hal.archives-ouvertes.fr/hal-01882324

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Abstract

Centralized fault diagnosis architectures are sometimes prohibitive for large-scale interconnected systems such as distribution systems, telecommunication networks, water distribution networks, fluid power systems. This paper presents a decentralized fault diagnosis method for continuous systems that only requires the knowledge of local models and limited knowledge of their neighboring subsystems. The notion of Fault-Driven Minimal Structurally Overdetermined (FMSO) set is used as the corner stone of the design of residual generators for the design of decentralized fault diagnosis for systems that have constraints of confidentiality, distance or limited access to some information. Binary integer linear programming (BILP) is used to optimize the choice of FMSO sets in each local subsystem.

145458
18579
31/08/2018

Fusion of Model-based and Data-based Fault Diagnosis Approaches

A.SLIMANI, P.RIBOT, E.CHANTHERY, N.RACHEDI

DISCO, ALTRAN

Manifestation avec acte : IFAC International Symposium on Fault Detection Supervision and Safety of Technical Processes ( SAFEPROCESS ) 2018 du 29 août au 31 août 2018, Varsovie (Pologne), Août 2018, 7p. , N° 18579

Lien : https://hal.archives-ouvertes.fr/hal-01882327

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Abstract

This paper presents a generic approach that combines model-based and data-based methods for fault detection and diagnosis. A proposed generic representation framework is used to express the different diagnosis results and to merge them without taking into account neither their internal characteristics, nor the nature of their outputs. Within this framework, the generic approach is performed in two steps. The first step consists in operating several diagnosis methods using the system measurements. The second step deals with the fusion of various methods results. This diagnosis approach is evaluated and tested on an anti-lock braking system. Simulations show that methods combination and results fusion make our diagnosis approach more efficient.

146725
18164
31/08/2018

Chronicle Discovery for Diagnosis from Raw Data: A Clustering Approach

A.SAHUGUEDE, E.LE CORRONC, M.V.LE LANN

DISCO

Manifestation avec acte : IFAC International Symposium on Fault Detection Supervision and Safety of Technical Processes ( SAFEPROCESS ) 2018 du 29 août au 31 août 2018, Varsovie (Pologne), Août 2018, 8p. , N° 18164

Lien : https://hal.laas.fr/hal-01817529

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Abstract

Chronicles are temporal patterns well suited for an abstract representation of the behavior of dynamic systems. For fault diagnosis, chronicles describe the nominal and faulty behaviors of the process. Powerful algorithms allow the recognition of chronicles in the flow of observations of the system and appropriate actions can be taken when a faulty situation is recognized. However, designing chronicles is not a trivial thing to do. The increasing complexity and capacity of data generation of highly-advanced processes cause the acquisition of a complete model difficult. This paper focuses on the problem of discovering chronicles that are representative of a system behavior from direct observations. A clustering approach to this problem is considered. The chronicle discovery algorithm proposed here designs chronicles with minimal knowledge of the system to diagnose. Furthermore, unprocessed data obtained directly from the system can be used in this clustering algorithm. Finally, the chronicle discovery algorithm proposed in this paper is illustrated on a sport performance monitoring device for a diagnosis of movement deviations in the temporal domain, in the event domain, or both, considered as faults for the athlete.

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18243
30/08/2018

Computer-aided Diagnosis via Hierarchical Density Based Clustering

T;OBRY, L.TRAVE-MASSUYES, A.SUBIAS

DISCO

Manifestation avec acte : International Workshop on Principles of Diagnosis ( DX ) 2018 du 27 août au 30 août 2018, Varsovie (Pologne), Août 2018, 8p. , N° 18243

Lien : https://hal.laas.fr/hal-01847563

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Abstract

When applying non-supervised clustering, the concepts discovered by the clustering algorithm hardly match business concepts. Hierarchical clustering then proves to be a useful tool to exhibit sets of clusters according to a hierarchy. Data can be analyzed in layers and the user has a full spectrum of clusterings to which he can give meaning. This paper presents a new hierarchical density-based algorithm that advantageously works from compacted data. The algorithm is applied to the monitoring of a process benchmark, illustrating its value in identifying different types of situations , from normal to highly critical.

144398
18404
30/08/2018

Optimal test / sensor selection problems formalized as integer programs

C.ARTIGUES, BBASSENE, E.CHANTHERY, A.GASMI, L.TRAVE-MASSUYES

ROC, DISCO

Manifestation avec acte : International Workshop on Principles of Diagnosis ( DX ) 2018 du 27 août au 30 août 2018, Varsovie (Pologne), Août 2018, 8p. , N° 18404

Lien : https://hal.archives-ouvertes.fr/hal-01882322

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Abstract

Diagnosis is the reasoning leading to the identification of the cause of a problem. Given a system instrumented with a set of sensors, diagnosis can be performed thanks to diagnosis tests that are designed from the system model. The tests only involve measured variables and can be checked with the measured values. The configuration of tests that pass and tests that do not pass provides a way to isolate the faults. However the fact that all faults are discriminable, i.e. the system is fully diagnosable, depends on the set of tests and hence of the sensors that are placed on the system. The number of tests that can be designed is generally huge and more than sufficient to achieve full or maximal diagnosability. This paper addresses several variants of the problem of selecting the set of tests/sensors so that diagnosability is maximal and a cost criterion is minimized. The variant problems are formalized in the integer programming framework.

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18153
27/08/2018

A Study of Evacuation Planning for Wildfires

C.ARTIGUES, E.HEBRARD, Y.PENCOLE, A.SCHUTT, P.STUCKEY

ROC, DISCO, CSIRO, Univ of Melbourne

Manifestation avec acte : International Workshop on Constraint Modelling and Reformulation ( ModRef ) 2018 du 27 août au 27 août 2018, Lille (France), Août 2018, 17p. , N° 18153

Lien : https://hal.archives-ouvertes.fr/hal-01814083

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Abstract

The GEO-SAFE project gathers researchers and fire emergency practitioners from EU and Australia with the aim to design innovative models and efficient response tools based on optimization methods for fighting wildfires. In this paper, we consider an evacuation planning problem issued from discussions with practitioners, where a wildfire is threatening a region with intermediate populated centres. As in earlier approaches in case of flood, we use a constraint optimization model involving malleable tasks to represent the evacuation of a population and a cumulative constraint per route segments. Indeed, in order to mitigate congestion risks, the authorities may delay the start of the evacuation but they may also affect the rate of evacuation by modulating the method used to raise the alarm. However, we consider a different objective: we maximize the minimum " safety margin " , weighted by the population, over every road segment. We introduce a new heuristic and a global flow constraint propa-gator. Moreover, we also propose an instance generator based on a random generation of road networks and basic fire propagation models. This generator produces challenging benchmarks even with very few evacuation tasks. Finally, we report the results of extensive computational experiments done using CP Optimizer.

147036
18191
24/08/2018

Mapping Chronicles to a k-dimensional Euclidean Space via Random Projections

A.SAHUGUEDE, S.FERGANI, E.LE CORRONC, M.V.LE LANN

DISCO

Manifestation avec acte : IEEE International Conference on Automation Science and Engineering ( CASE ) 2018 du 20 août au 24 août 2018, Munich (Allemagne), Août 2018, 6p. , N° 18191

Lien : https://hal.laas.fr/hal-01817539

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Abstract

This paper is concerned with an innovative strategy that maps chronicles, that are timed discrete event models, to a k-dimensional Euclidean space via random projections. The proposed approach is a projection that takes into account both characteristics of events, namely event types, and temporal constraints of chronicles. This will lead to an unbounded convex polytope in the Euclidean space that contains all the possible instances of the corresponding chronicle. It allows to easily and efficiently compare chronicles. Such comparisons are useful in a fault diagnosis purpose to discriminate chronicles representing behaviors of dynamic processes. Examples and preliminary results are provided in this paper to introduce the proposed methodology.

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