A distributed architecture to implement a prognostic function for complex systems

The proactivity in maintenance management is improved by the implementation of CBM (Condition-Based Maintenance) principles and of PHM (Prognostic and Health Management). These implementations use data about the health status of the systems. Among them, prognostic data make it possible to evaluate the future health of the systems. The Remaining Useful Lifetimes (RULs) of the components is frequently required to prognose systems. However, the availability of complex systems for productive tasks is often expressed in terms of RULs of functions and/or subsystems; those RULs have to bring information about the components. Indeed, the maintenance operators must know what components need maintenance actions in order to increase the RULs of the functions or subsystems, and consequently the availability of the complex systems for longer tasks or more productive tasks. This paper aims at defining a generic prognostic function of complex systems aiming at prognosing its functions and at enabling the isolation of components that needs maintenance actions. The proposed function requires knowledge about the system to be prognosed. The corresponding models are detailed. The proposed prognostic function contains graph traversal so its distribution is proposed to speed it up. It is carried out by generic agents

Architectures de diagnostic et de pronostic distribuées de systèmes techniques complexes de grande dimension

Dans ce mémoire, différentes architectures pour le contrôle et la surveillance des systèmes techniques complexes de grande dimension (STCGD) sont discutées. Les problématiques de maintenance conditionnelle et d'évaluation de l'état de santé sont définies. Les types de diagnostic et de pronostic sont présentés afin d'aboutir à une évaluation de l'état de santé des STCGD. Les études relatives au diagnostic décentralisé sont discutées puis les apports des NTIC et des technologies distribuées au diagnostic sont présentés. Par la suite, le diagnostic distribué et les travaux relatifs à ce mode de déploiement sont introduits. Les limites des approches centralisées et décentralisées du diagnostic sont présentées et confrontées à l'apport des approches distribuées. Les informations et/ou les connaissances supports aux diagnostic et au pronostic ainsi que leur modélisation afin de les exploiter sont décrites et formalisées. Une caractérisation des statuts que peut prendre un composant est proposée. Il est décrit les pré-requis nécessaires pour la couche de surveillance des STCGD et les principes du diagnostic et du pronostic sont ensuite présentés sous la forme de différents algorithmes. Enfin, une méthode d'évaluation de l'état de santé des STCGD est proposée. Plusieurs déploiements peuvent être envisagés pour l'évaluation de la santé des STCGD. Une plateforme de simulation a été développée pour évaluer les performances des déploiements centralisés et distribués. La plateforme a eu pour but de se comporter comme la couche de surveillance d'un STCGD. Un cas d'étude paramétrable est proposé pour chacun des deux déploiements et leurs performances sont comparées. ABSTRACT : In this dissertation, various architectures for the control and the monitoring of Large Scale Complex Technical Systems (LSCTS) are discussed. The problematic of condition-based maintenance and health status assessment is defined. A diagnostic and prognostic typology is presented leading to the assessment of the health status of LSCTSs. Decentralized diagnosis studies are discussed then the contributions of the ICT and of the distributed technologies for the diagnosis are presented. Thereafter, the distributed diagnosis and works relative to this kind of deployments are introduced. The limits of the centralized and decentralized diagnosis approaches are presented. Then the centralized approaches are compared to the distributed ones. Information and/or knowledge that support the diagnosis and the prognosis as well as their modeling in order to exploit them are described and formalized. A characterization is proposed for the different status of a component can be in. Requirements are described for the monitoring layer of the LSCTSs are described in order to implement the proposed diagnosis and prognosis principles that are then specified by the means of algorithms. Eventually, a health assessment method of the LSCTSs is also proposed. Several deployments can be considered to implement the health assessment of the LSCTSs. A simulation platform, which was developed to evaluate the performances of the centralized and the distributed deployments, is presented. Among the purposes of the platform, one is to behave as the monitoring layer of a LSCTS. A use case is proposed for two deployments and their performances are compared

Applicative architecture for embedded distributed technical diagnosis

This article presents an applicative architecture based on a solving method for embedded technical diagnosis of complex systems. This architecture is defined in order to provide services enabling the evaluation of the health status of complex systems. Diagnostic services provide information to the maintenance decision support system that leads to reduce the periods of unavailability and determine if their future mission can be carried out. The architecture presented in this paper implements a distributed diagnostic function using multi-agent techniques. A consistency model-based diagnosis is proposed that leads to the identification of the faulty LRUs and the failed functions of complex systems

A prognostic function for complex systems to support production and maintenance co-operative planning based on an extension of object oriented Bayesian networks

The high costs of complex systems lead companies to improve their efficiency. This improvement can particularly be achieved by reducing their downtimes because of failures or for maintenance purposes. This reduction is the main goal of Condition-Based Maintenance and of Prognostics and Health Management. Both those maintenance policies need to install appropriate sensors and data processes not only to assess the current health of their critical components but also their future health. These future health assessments, also called prognostics, produce the Remaining Useful Life of the components associated to imprecision quantifications. In the case of complex systems where components are numerous, the matter is to assess the health of whole systems from the prognostics of their components (the local prognostics). In this paper, we propose a generic function that assesses the future availability of complex systems from their local prognostics (the prognostics of their components) by using inferences rules. The results of this function can then be used as decision support indicators for planning productive and maintenance tasks. This function exploits a proposed extension for Object Oriented Bayesian Networks (OOBN) used to model the complex system in order to assess the probabilities of failure of components, functions and subsystems. The modeling of the complex system is required and it is presented as well as modeling transformations to tackle some OOBN limitations. Then, the computing inference rules used to define the future availability of complex systems are presented. The extension added to OOBN consists in indicating the components that should first be maintained to improve the availabilities of the functions and subsystems in order to provide a second kind of decision support indicators for maintenance. A fictitious multi-component system bringing together most of the structures encountered in complex systems is modeled and the results obtained from the application of the proposed generic function are presented as well as ways that production and maintenance planning can used the computed indicators. Then we show how the proposed generic prognostic function can be used to predict propagations of failures and their effects on the functioning of functions and subsystems

Possible Spillover of Pathogens between Bee Communities Foraging on the Same Floral Resource

Viruses are known to contribute to bee population decline. Possible spillover is suspected from the co-occurrence of viruses in wild bees and honey bees. In order to study the risk of virus transmission between wild and managed bee species sharing the same floral resource, we tried to maximize the possible cross-infections using Phacelia tanacetifolia, which is highly attractive to honey bees and a broad range of wild bee species. Virus prevalence was compared over two years in Southern France. A total of 1137 wild bees from 29 wild bee species (based on COI barcoding) and 920 honey bees (Apis mellifera) were checked for the seven most common honey bee RNA viruses. Halictid bees were the most abundant. Co-infections were frequent, and Sacbrood virus (SBV), Black queen cell virus (BQCV), Acute bee paralysis virus (ABPV) and Israeli acute paralysis virus (IAPV) were widespread in the hymenopteran pollinator community. Conversely, Deformed wing virus (DWV) was detected at low levels in wild bees, whereas it was highly prevalent in honey bees (78.3% of the samples). Both wild bee and honey bee virus isolates were sequenced to look for possible host-specificity or geographical structuring. ABPV phylogeny suggested a specific cluster for Eucera bees, while isolates of DWV from bumble bees (Bombus spp.) clustered together with honey bee isolates, suggesting a possible spillover