A hybrid closed queuing network approach to model dataflow in networked distributed processors

International audienceIn this paper, a hybrid closed queuing network model has been proposed to model dataflow in networked distributed processing systems. Multi-threading is useful in reducing the latency by switching among a set of threads in order to improve the processor utilization. Two sets of processors, synchronization and execution processors exist. Synchronization processors handle load/store operations and execution processors handle arithmetic/logic and control operations. A closed queuing network model is suitable for large number of job arrivals. Both single server and multiple server models are discussed. The normalization constant is derived using a recursive algorithm for the given model. Performance measures such as average response times and average system throughput are derived and plotted against the total number of processors in the closed queuing network model. Other important performance measures like processor utilizations, average queue lengths, average waiting times and relative utilizations are also derived

A hybrid closed queuing network approach to model dataflow in networked distributed processors

International audienceIn this paper, a hybrid closed queuing network model has been proposed to model dataflow in networked distributed processing systems. Multi-threading is useful in reducing the latency by switching among a set of threads in order to improve the processor utilization. Two sets of processors, synchronization and execution processors exist. Synchronization processors handle load/store operations and execution processors handle arithmetic/logic and control operations. A closed queuing network model is suitable for large number of job arrivals. Both single server and multiple server models are discussed. The normalization constant is derived using a recursive algorithm for the given model. Performance measures such as average response times and average system throughput are derived and plotted against the total number of processors in the closed queuing network model. Other important performance measures like processor utilizations, average queue lengths, average waiting times and relative utilizations are also derived

Prediction of System Reliability for Single Component Repair

Purpose – To present a new Split System Model (SSM) that predicts the reliability of complex systems with multiple Preventive Maintenance (PM) actions in the long term. Design/methodology/approach - The SSM was developed using probability theory based on the concept of separating repaired and unrepaired components within a system virtually when modelling the reliability of the system after repairs. After theoretical analysis, a case study and Monte Carlo simulation were used to evaluate the effectiveness of the newly developed model. Findings – The model can be used to determine the remaining life of systems, to show the changes in reliability with PM actions, and to quantify PM intervals after imperfect repairs. Practical implications – SSM can be used to predict the reliability of complex systems with multiple PM actions, and hence can be used to support asset PM decision making over the whole life of the asset, such as scheduled PM times and spare parts requirements. An asset often has some vulnerable components, i.e., where the lives of these components are much shorter than the rest of the asset. In this case, PM is often conducted on these vulnerable components for maximising the useful life of the asset. The specific formulae derived in this paper can be used to predict the reliability of the asset for this scenario. Originality/value - The proposed model uses a new concept of split systems to predict the changes of reliability of complex systems with multiple PM actions. Asset managers will find this model to be a useful tool in the optimisation of their asset PM strategies

Approach of Data and Information collection for the Decision Support in Industrial Asset Management: Keynote

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About the extraction of relevant health information of equipment for optimizing their performances: Keynote

International audienceThe development of nanotechnologies, biotechnology, and the technologies of communication and information, pushed back the limits of information of the product, which forces to reformulate the problem of extraction of information on the health condition of the product over the life cycle. Thus, a product's performance should not be limited to the evaluation of macroscopic measurements, as the state of the components can now be evaluated, starting from intrinsic data at the level nanometric. Thus, it will be possible to characterize a product component by the information of DNA type (Deoxyribonucleic Acid) that will enable the following, for example, the degradations of involved materials. This prospect can help to reinforce the quality of the collected data to feed the information extraction models. Decision support requires data and information of high quantity at all levels in the product decomposition. This need led today the human society to becoming increasingly voracious to information on products or systems lifecycle. Data quality has forced improving the reliability of the data collection systems for product life cycle management. Unfortunately, there are not yet well-established models of the data collection process, enabling strengthening their effectiveness. On the other hand, the advances in information technologies and the increasing power of computer systems enable us to consider products and production systems with value-added support services. Thus, today, it is a challenge to design quite intelligent and self-healing products by using nanotechnologies and intelligent embedded information devices. This presentation will describe the product’s health condition information extraction approach for decision support in maintenance and risk assessment. A modeling approach of data and information collection processes, based on the product identification technologies and nanotechnology, will be presented with the associated data processing tools. It will also point out the emerging scientific problems, of which solutions will enable efficient information systems supporting the product lifecycle and health condition monitoring

Requirements and issues of data and Information for the decision support in industrial asset management

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Cumulative diagnosis strategy for predictive maintenance decision support

International audienceWe propose a new diagnosis strategy, here called "cumulative diagnosis", for advanced decision support to predictive maintenance. It is based on the cumulative damage principle and the use the degradation laws of the considered components. The main objectives of this strategy include the reduction of the cost of diagnoses per time unit and the improvement of the systems' availability. The strategy requires establishing and composing three models: resources allocation to the diagnosis tasks under exclusiveness constraint; diagnosis tasks scheduling under precedence constraints; and a dynamic model of tasks' planning in real-time over periodic, a-periodic and stochastic time windows. The obtained models are integrated to support the predictive maintenance decisions. The new diagnosis strategy has several advantages and its performances may be appreciated through the experimental results of evaluation