DIAMOND: distributed multi-agent architecture for monitoring and diagnosis

Wörn, Heinz; Längle, Thomas; Albert, Martin; Kazi, Arif; Brighenti, Attilio; Seijo, Santiago Revuelta; Senior, Christopher; Sanz-Bobi, Miguel Á.; Villar, José

doi:10.1080/09537280410001662475

Cited by 23 publications

(11 citation statements)

References 5 publications

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“…The functional knowledge can be extracted from the hierarchical view which breaks down a system into subsystems, then into functions, then into multiple levels of sub-functions till components implementing one or more sub-functions [49]. The structural knowledge is obtained from the direct interactions between entities (components or functions) and their failure modes mainly in order to propagate their effects [51]. For this purpose, MBSE diagrams can be used like, with SysML, the internal blocks diagrams, activity diagrams that represent material, energy and data flows that are used, produced, transformed and exchanged by functions and components.…”

Section: Multicomponent System Modeling and Inferencesmentioning

confidence: 99%

Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses

González

Desforges

Archimède

2018

Reliability Engineering & System Safety

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Conditioned-based maintenance and prognostics and health management enable to optimize maintenance by scheduling the necessary repairs and replacements of technical system components according to their present and future health states. The assessment of future health states is the prognostics and health management keystone. Many technical production systems are made of numerous components implementing their functions. A method to assess the ability of multicomponent systems to carry out future production tasks is proposed to provide decision supports for production and maintenance planning for a better compromise between their objectives. It is based on components prognoses. To handle inherent uncertainties of these prognoses, the method is based on the Dempster Shafer theory and Bayesian networks inferences. Local prognoses are categorized and transformed to be compliant to Dempster Shafer theory. Patterns of systems are identified for which inferences are defined. The patterns are then used to model systems and to assess their abilities to achieve future tasks. An identification of components that should first undergo maintenance is proposed. An example implementing a fictitious complex systems is presented to show how the provided decision supports can be used for production and maintenance planning purposes.

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Section: Multicomponent System Modeling and Inferencesmentioning

confidence: 99%

Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses

González

Desforges

Archimède

2018

Reliability Engineering & System Safety

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“…The structural modeling aims at representing the direct interactions between entities (components or functions) and their failure modes mainly in order to propagate their effects [47]. Failure Modes and Effects Analysis (FMEA), fault trees or HAZard and OPerability (HAZOP) studies enable to collect the necessary knowledge for structural modeling.…”

Section: Structural Knowledge Modelingmentioning

confidence: 99%

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

Desforges

Divart

Archimde

2017

Computers in Industry

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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.

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“…Structural modeling aims at identifying the direct interactions between entities (components or functions) that lead to the propagations of the effects of their failures [16].…”

Section: B Structural Knowledge Modelingmentioning

confidence: 99%

Towards a generic prognostic function of technical multi-component systems taking into account the uncertainties of the predictions of their components

Gonzalez¹,

Desforges²,

Archimède³

2017

2017 4th International Conference on Control, Decision and Information Technologies (CoDIT)

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This article presents the first elements of a generic function that assesses the capacity of technical multi-component systems to accomplish the assigned productive tasks from production planning. This assessment is based on the prognostics of their components. It must so be able to process inaccuracies and uncertainties of these prognostics. For its implementation the aimed function combines the Dempster-Shafer theory combined and Bayesian inferences. The paper presents the multicomponent system modeling and the inferences for the different identified structures as well as a general algorithm. The final aim of the proposed generic function is to compute decision supports for cooperative maintenance and production management.

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DIAMOND: distributed multi-agent architecture for monitoring and diagnosis

Cited by 23 publications

References 5 publications

Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses

Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses

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

Towards a generic prognostic function of technical multi-component systems taking into account the uncertainties of the predictions of their components

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