Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

Throughout the history, the evolutions of the requirements for manufacturing equipments have depended on the changes in the customers' demands. Among the present trends in the requirements for new manufacturing equipments, there are more flexible and more reactive machines. In order to satisfy those requirements, this paper proposes a control and monitoring framework for machine tools based on smart sensor, on smart actuator and on agent concepts. The proposed control and monitoring framework achieves machine monitoring, process monitoring and adapting functions that are not usually provided by machine tool control systems. The proposed control and monitoring framework has been evaluated by the means of a simulated operative part of a machine tool. The communication between the agents is achieved thanks to an Ethernet network and CORBA protocol. The experiments (with and without cooperation between agents for accommodating) give encouraging results for implementing the proposed control framework to operational machines. Also, the cooperation between the agents of control and monitoring framework contributes to the improvement of reactivity by adapting cutting parameters to the machine and process states and to increase productivity

Data validation: a case study for a feed-drive monitoring

The monitoring of machine-tools implicated in the metal cutting process is the subject of increasing developments because of requests on control, reliability, availability of machine-tools and on work-piece quality. The use of computers contributes to a better machine and process monitoring by enabling the implementation of complex algorithms for control, monitoring, … The improvement of monitoring of the main machine-tools devices, the feed-drives and the spindles that drive the cutting process, can be realised by estimating their fault sensitive physical parameters from their continuous-time model. We have chosen to use a continuous-time ARX model. We particularly focus on slow time varying phenomena. This estimation should run while there is no machining process to avoid false detection of faults on the machine due to the cutting process. High speed motions, that occur at least for each tool exchange, are exploited. Some functional constraints require the use of an off-line estimation method, we have chosen an ordinary least squares method. Estimating the physical parameters is insufficient to obtain an efficient monitoring. A measurement analysis and validation are necessary as the validation of the estimated physical parameters. An approach of the measurement and physical parameter estimation validation for a NC machine-tool feed-drive is proposed

Design methodology for smart actuator services for machine tool and machining control and monitoring

This paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and control tasks. Their data processing abilities are also exploited in order to create a new decision level at the machine level. The aim of this decision level is to react to disturbances that the monitoring tasks detect. The cooperation between the computational objects (the smart spindle, the smart feed-drives and the CNC unit) enables to carry out functions for accommodating or adapting to the disturbances. This leads to the extension of the notion of smart actuator with the notion of agent. In order to implement the services of the smart drives, a general design is presented describing the services as well as the behavior of the smart drive according to the object oriented approach. Requirements about the CNC unit are detailed. Eventually, an implementation of the smart drive services that involves a virtual lathe and a virtual turning operation is described. This description is part of the design methodology. Experimental results obtained thanks to the virtual machine are then presented

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

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system

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

Méthodes et outils pour la réactivité et la proactivité des systèmes et des organisations

Le parcours de recherche présenté dans le dossier présente des contributions portant sur la réactivité et la proactivité des systèmes et des organisations. Ces contributions portent sur une méthode pour la surveillance en fabrication mécanique par une mise en œuvre du concept de capteur intelligent. Puis, l’exploitation des données de surveillance est réalisée par une architecture distribuée de surveillance et de conduite des machines et des processus accroissant la réactivité et la productivité de la ressource de production. Un retour d’expérience cognitif permet, par la capitalisation d’expériences passées, à une organisation de réagir plus vite face à une situation courante. La proactivité a été traitée par une méthode de pronostic des systèmes multi-composants fournissant des indicateurs d’aide à la décision ainsi que par la méthode les exploitant pour la planification conjointe de la production et de la maintenance. Le projet recherche porté doit contribuer à deux domaines d’application que sont l’ingénierie des systèmes et « l’industrie 4.0 ». En ingénierie des systèmes, le projet porte notamment sur les phases de conception par de l’aide à la recherche d’architectures répondant aux exigences de nouveaux cahier des charges mais aussi par des contributions à l’évaluation d’architectures éligibles, notamment, par la définition d’une architectures d’objets simulant l’environnement, la physique des composants, les fonctions, les scénarios assurant les échanges des différents types de flux. Dans le domaine de « l’industrie 4.0 », le projet porte sur la définition d’architectures fonctionnelles de ressources techniques pour accroître leur réactivité à leur état de santé et à celui du procédé mais aussi sur la définition de méthodes réactives et des services nécessaires pour (re)planifier la production et la maintenance selon la santé actuelle ou future des procédés et ressources techniques

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

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 multi-component 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

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

Scheduling of production and maintenance activities using multi-agents system

Manufacturing systems are usually confronted to conflicting situations between production and maintenance ser- vices since their activities are considered as source of disturbance to each other. In order to reduce these conflicts, a multi-agents system SCEMP (Supervisor, Customer, Environment, Maintainer and Producer) is proposed in this paper, making sure that these two entities collaborate in order to achieve a common goal. It consists of scheduling the production activities according to the health states of the machines. The main idea is to use the prediction of the durations of use and remaining useful lifetimes of the machines devices, which can be obtained using prognostic techniques. This enables simultaneous scheduling of production and maintenance activities