Modèles de processus de collecte de données et d'évaluation de performance de disponibilité pour l'aide à la décision en maintenance

This thesis proposes a modeling approach of an adaptive and iterative data collection process, and a validation tool via operational effectiveness features for equipment. An approach, named "Tropos", established based on the information theory, is developed to modeling and evaluating data collection processes. This is an original approach, which allows synthesizing three features that characterize the effectiveness of a data collection process: 1) data usefulness, 2) process complexity, 3) gain of information by a basic process activity. An original model, based on colored stochastic Petri nets coupled to the Monte Carlo simulation, has also been developed to validate the effectiveness of the data collection process. This model uses as input, stochastic process models of degradation, of failure and of maintenance of equipment components. The input parameters of the models are assumed to be known and obtained from the collected data. The properties of colored stochastic Petri net model are also used to derive the minimum cuts required to assess the equipment condition and operational effectiveness. These properties also allow to treating systems of k/n structures. The effectiveness of the proposed approach is finally illustrated on a multi-source renewable energy production system, by implementing the algorithms of the model under the Silab software environmentCette thèse propose une approche de modélisation d'un processus adaptatif et itératif de collecte des données, ainsi qu'un outil de validation via des indicateurs d'efficacité opérationnelle de l'équipement. Une approche nommée "Tropos", établie grâce à la théorie de l'information, est donc développée pour modéliser et évaluer le processus de collecte de données. L'approche, originale, permet de synthétiser trois indicateurs qui caractérisent l'efficacité du processus de collecte : 1) utilité des données, 2) complexité du processus, 3) gain d'information par une activité élémentaire du processus. Un modèle original, basé sur les réseaux de Pétri stochastiques colorés couplé à la simulation Monte Carlo, est également proposé pour valider l'efficacité du processus de collecte de données. Ce modèle utilise comme données d'entrée les modèles des processus stochastiques de dégradation, de défaillance et de maintenance des composants de l'équipement. Les paramètres des modèles d'entrée sont supposés connus et extraits des données collectées. Les propriétés du modèle réseau de Pétri stochastique coloré permettent d'extraire les coupes minimales indispensables à l'évaluation de l'état et de l'efficacité opérationnelle de l'équipement. Elles permettent également de traiter les systèmes de structure k/n. L'effectivité de l'approche proposée est enfin illustrée sur un système de production d'énergie multi-source renouvelable, grâce à l'implémentation des algorithmes du modèle sous le logiciel Sila

Models of data collection process and evaluation of availability performance for maintenance decision support

Cette thèse propose une approche de modélisation d'un processus adaptatif et itératif de collecte des données, ainsi qu'un outil de validation via des indicateurs d'efficacité opérationnelle de l'équipement. Une approche nommée "Tropos", établie grâce à la théorie de l'information, est donc développée pour modéliser et évaluer le processus de collecte de données. L'approche, originale, permet de synthétiser trois indicateurs qui caractérisent l'efficacité du processus de collecte : 1) utilité des données, 2) complexité du processus, 3) gain d'information par une activité élémentaire du processus. Un modèle original, basé sur les réseaux de Pétri stochastiques colorés couplé à la simulation Monte Carlo, est également proposé pour valider l'efficacité du processus de collecte de données. Ce modèle utilise comme données d'entrée les modèles des processus stochastiques de dégradation, de défaillance et de maintenance des composants de l'équipement. Les paramètres des modèles d'entrée sont supposés connus et extraits des données collectées. Les propriétés du modèle réseau de Pétri stochastique coloré permettent d'extraire les coupes minimales indispensables à l'évaluation de l'état et de l'efficacité opérationnelle de l'équipement. Elles permettent également de traiter les systèmes de structure k/n. L'effectivité de l'approche proposée est enfin illustrée sur un système de production d'énergie multi-source renouvelable, grâce à l'implémentation des algorithmes du modèle sous le logiciel SilabThis thesis proposes a modeling approach of an adaptive and iterative data collection process, and a validation tool via operational effectiveness features for equipment. An approach, named "Tropos", established based on the information theory, is developed to modeling and evaluating data collection processes. This is an original approach, which allows synthesizing three features that characterize the effectiveness of a data collection process: 1) data usefulness, 2) process complexity, 3) gain of information by a basic process activity. An original model, based on colored stochastic Petri nets coupled to the Monte Carlo simulation, has also been developed to validate the effectiveness of the data collection process. This model uses as input, stochastic process models of degradation, of failure and of maintenance of equipment components. The input parameters of the models are assumed to be known and obtained from the collected data. The properties of colored stochastic Petri net model are also used to derive the minimum cuts required to assess the equipment condition and operational effectiveness. These properties also allow to treating systems of k/n structures. The effectiveness of the proposed approach is finally illustrated on a multi-source renewable energy production system, by implementing the algorithms of the model under the Silab software environmen

Monte Carlo based Petri net simulation for maintenance strategies assessment in series-parallel-series multi-physic systems

International audienceThe authors propose a methodology to assess the effectiveness of a maintenance strategy on the availability of a serial-parallel multi-physic system, using Monte Carlo simulation embedded in a Petri net model. The systems are composed of heterogenous components that are characterized by specific degradations and failure mechanisms. Building an effective maintenance strategy to improve the availability of such a system requires to monitoring the degradation of each component. We assume that each component is subject to stochastic degradations. Also, we consider that each component might have three health status, according to degradation thresholds, function of the component reliability: “healthy”, “degraded” and “failed”. The health condition of the overall system relies on the health status of each component. A model for tracking the status of each component has been worked out using a colored stochastic Petri net (CSPN). Each health status is modeled by a place within the CSPN model, where each component is modeled by a colored token. The degradation of each component of the system is evaluated based on the Monte Carlo simulation technique. Transition firing regarding a given color model the evolution of the associated component from a health condition to another due to the degradation mechanism or to a maintenance action aimed to restore partially or totally its performance. However, the degradation of each component does not have the same influence on the performance of the overall system. Operational performance indicators are introduced to quantify the influence of each component on the performance of the entire system. Furthermore, maintenance actions are defined taking into account the degradation level of each component, the influence that each component has on the performance of the system and the available repairman. The effectiveness of the maintenance strategy on the system availability is evaluated through simulation

Coloured stochastic Petri nets modelling for the reliability and maintenance analysis of multi-state multi-unit systems

International audienceThe purpose of this paper is to introduce a method for modelling the multi-state repairable systems subject to stochastic degradation processes by using the coloured stochastic Petri nets (CSPN). The method is a compact and flexible Petri nets model for multi-state repairable systems and offers an alternative to the combinatory of Markov graphs

Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms

Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors

Two Lysin-Motif Receptor Kinases, Gh-LYK1 and Gh-LYK2, Contribute to Resistance against Verticillium wilt in Upland Cotton

Lysin-motif (LysM) receptor kinases (LYKs) play essential roles in recognition of chitin and activation of defense responses against pathogenic fungi in the model plants Arabidopsis and rice. The function of LYKs in non-model plants, however, remains elusive. In the present work, we found that the transcription of two LYK-encoding genes from cotton, Gh-LYK1 and Gh-LYK2, was induced after Verticillium dahliae infection. Virus-induced gene silencing (VIGS) of Gh-LYK1 and Gh-LYK2 in cotton plants compromises resistance to V. dahliae. As putative pattern recognition receptors (PRRs), both Gh-LYK1 and Gh-LYK2 are membrane-localized, and all three LysM domains of Gh-LYK1 and Gh-LYK2 are required for their chitin-binding ability. However, since Gh-LYK2, but not Gh-LYK1, is a pseudo-kinase and, on the other hand, the ectodomain (ED) of Gh-LYK2 can induce reactive oxygen species (ROS) burst in planta, Gh-LYK2 and Gh-LYK1 may contribute differently to cotton defense. Taken together, our results establish that both Gh-LYK1 and Gh-LYK12 are required for defense against V. dahliae in cotton, possibly through different mechanisms

A new family of Mn-based perovskite (La1-xYxMnO3) with improved oxygen electrocatalytic activity for metal-air batteries

The sluggish reaction kinetics occurring at the cathodes limits the performances of the metal-air batteries. Therefore, developing the oxygen electrocatalysts which can accelerate oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is a critical issue. Mn-based perovskite has drawn extensive interests though their ORR and OER catalytic activity still needs to be improved. In this work, a new family of Mn-based perovskite (La1-xYxMnO3, LYM) is developed which demonstrates an improved ORR and OER catalytic activity compared with the representative strontium-doped Mn-based perovskite (LSM). For La0.9Y0.1MnO3 (LYM-10), the onset potential and half-wave potential during ORR can reach 0.909 V and 0.750 V (vs. RHE), respectively, which are more positive than those of LSM reported in most of the recent reports. Furthermore, LYM-10 also shows a better OER catalytic activity than La0.7Sr0.3MnO3 (LSM-30). In addition to its good bifunctional property, LYM-10 also achieves the superior durability compared with Pt/C during ORR, and the current retention of LYM-10 is as high as 97.3% after 43000 s. Using LYM-10 as the cathode catalyst, the alunimum-air battery can reach the maximum power density of 266 mW/cm(2), and zinc-air battery can obtain low charge-discharge overpotential and the good cycling stability. (C) 2018 Elsevier Ltd. All rights reserved