A Petri net model for optimization of inspection and preventive maintenance rates

Degradation of power system components can be reduced through preventative maintenance. In addition, optimizing inspection and preventive maintenance rates is of great importance since too little or an excessive amount of maintenance can have undesirable consequences. Conventional approaches are not applicable to practical and large-scale systems due to their inherent restrictions, such as complexity and computational burden. In this paper, a Petri net (PN) maintenance model is proposed to consider degradation, inspection, and repair processes as well as random and aging-related failures. It has great flexibility since some constraints can be imposed on the maximum number of maintenance actions, or the maintenance can be inhibited at any deterioration state without the need to change the model structure. Another advantage of this model is that it can handle the dependent deterioration among components. All the mentioned aspects are illustrated by applying the model to some circuit breakers (CBs) of the Roy Billinton test system (RBTS). The simulation results reveal that the obtained inspection rates could differ from the conventional methods resulting in lower total costs. It is also demonstrated that the proposed model can be linked with maintenance decision-making and asset management tools.© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

AADLib, A Library of Reusable AADL Models

The SAE Architecture Analysis and Design Language is now a well-established language for the description of critical embedded systems, but also cyber-physical ones. A wide range of analysis tools is already available, either as part of the OSATE tool chain, or separate ones. A key missing elements of AADL is a set of reusable building blocks to help learning AADL concepts, but also experiment already existing tool chains on validated real-life examples. In this paper, we present AADLib, a library of reusable model elements. AADLib is build on two pillars: 1/ a set of ready-to- use examples so that practitioners can learn more about the AADL language itself, but also experiment with existing tools. Each example comes with a full description of available analysis and expected results. This helps reducing the learning curve of the language. 2/ a set of reusable model elements that cover typical building blocks of critical systems: processors, networks, devices with a high level of fidelity so that the cost to start a new project is reduced. AADLib is distributed under a Free/Open Source License to further disseminate the AADL language. As such, AADLib provides a convenient way to discover AADL concepts and tool chains, and learn about its features

Maintenance models applied to wind turbines. A comprehensive overview

Producción CientíficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models

Transformation by example

La transformation de modèles consiste à transformer un modèle source en un modèle cible conformément à des méta-modèles source et cible. Nous distinguons deux types de transformations. La première est exogène où les méta-modèles source et cible représentent des formalismes différents et où tous les éléments du modèle source sont transformés. Quand elle concerne un même formalisme, la transformation est endogène. Ce type de transformation nécessite généralement deux étapes : l’identification des éléments du modèle source à transformer, puis la transformation de ces éléments. Dans le cadre de cette thèse, nous proposons trois principales contributions liées à ces problèmes de transformation. La première contribution est l’automatisation des transformations des modèles. Nous proposons de considérer le problème de transformation comme un problème d'optimisation combinatoire où un modèle cible peut être automatiquement généré à partir d'un nombre réduit d'exemples de transformations. Cette première contribution peut être appliquée aux transformations exogènes ou endogènes (après la détection des éléments à transformer). La deuxième contribution est liée à la transformation endogène où les éléments à transformer du modèle source doivent être détectés. Nous proposons une approche pour la détection des défauts de conception comme étape préalable au refactoring. Cette approche est inspirée du principe de la détection des virus par le système immunitaire humain, appelée sélection négative. L’idée consiste à utiliser de bonnes pratiques d’implémentation pour détecter les parties du code à risque. La troisième contribution vise à tester un mécanisme de transformation en utilisant une fonction oracle pour détecter les erreurs. Nous avons adapté le mécanisme de sélection négative qui consiste à considérer comme une erreur toute déviation entre les traces de transformation à évaluer et une base d’exemples contenant des traces de transformation de bonne qualité. La fonction oracle calcule cette dissimilarité et les erreurs sont ordonnées selon ce score. Les différentes contributions ont été évaluées sur d’importants projets et les résultats obtenus montrent leurs efficacités.Model transformations take as input a source model and generate as output a target model. The source and target models conform to given meta-models. We distinguish between two transformation categories. Exogenous transformations are transformations between models expressed using different languages, and the whole source model is transformed. Endogenous transformations are transformations between models expressed in the same language. For endogenous transformations, two steps are needed: identifying the source model elements to transform and then applying the transformation on them. In this thesis, we propose three principal contributions. The first contribution aims to automate model transformations. The process is seen as an optimization problem where different transformation possibilities are evaluated and, for each possibility, a quality is associated depending on its conformity with a reference set of examples. This first contribution can be applied to exogenous as well as endogenous transformation (after determining the source model elements to transform). The second contribution is related precisely to the detection of elements concerned with endogenous transformations. In this context, we present a new technique for design defect detection. The detection is based on the notion that the more a code deviates from good practice, the more likely it is bad. Taking inspiration from artificial immune systems, we generate a set of detectors that characterize the ways in which a code can diverge from good practices. We then use these detectors to determine how far the code in the assessed systems deviates from normality. The third contribution concerns transformation mechanism testing. The proposed oracle function compares target test cases with a base of examples containing good quality transformation traces, and assigns a risk level based on the dissimilarity between the two. The traces help the tester understand the origin of an error. The three contributions are evaluated with real software projects and the obtained results confirm their efficiencies

Intelligent Business Process Optimization for the Service Industry

The company\u27s sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization

Intelligent Business Process Optimization for the Service Industry

The company's sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization

ARCHITECTURE-BASED RELIABILITY ANALYSIS OF WEB SERVICES

In a Service Oriented Architecture (SOA), the hierarchical complexity of Web Services (WS) and their interactions with the underlying Application Server (AS) create new challenges in providing a realistic estimate of WS performance and reliability. The current approaches often treat the entire WS environment as a black-box. Thus, the sensitivity of the overall reliability and performance to the behavior of the underlying WS architectures and AS components are not well-understood. In other words, the current research on the architecture-based analysis of WSs is limited. This dissertation presents a novel methodology for modeling the reliability and performance of web services. WSs are treated as atomic entities but the AS is broken down into layers. More specifically, interactions of WSs with the underlying layers of an AS are investigated. One important feature of the research is investigating the impact of dynamic parameters that exist at the layers, such as configuration parameters. These parameters may have negative impact on WSs performance if they are not configured properly. WSs are developed in house and the AS considered is JBoss AS. An experimental environment is setup so that controlled service requests can be generated and important performance metrics can be recorded under various configurations of the AS. On the other hand, a simulation model is developed from the source code and run-time behavior of the existing WS and AS implementations. The model mimics the logical behavior of the WSs based on their communication with the AS layers. The simulation results are compared to the experimental results to ensure the correctness of the model. The architecture of the simulation model, which is based on Stochastic Petri Nets (SPN), is modularized in accordance to the layers and their interactions. As the web services are often executed in a complex and distributed environment, the modularized approach enables a user or a designer to observe and investigate the performance of the entire system under various conditions. In contrast, most approaches to WSs analyses are monolithic in that the entire system is treated as a closed box. The results show that 1) the simulation model can be a viable tool for measuring the performance and reliability of WSs under different loads and conditions that may be of great interest to WS designers and the professionals involved; 2) Configuration parameters have big impacts on the overall performance; 3) The simulation model can be tuned to account for various speeds in terms of communication, hardware, and software; 4) As the simulation model is modularized, it may be used as a foundation for aggregating the modules (layers), nullifying modules, or the model can be enhanced to include other aspects of the WS architecture such as network characteristics and the hardware/operating system on which the AS and WSs execute; and 5) The simulation model is beneficial to predict the performance of web services for those cases that are difficult to replicate in a field study

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding