Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation

This paper is part of the ENHAnCE ITN project (https://www.h2020-enhanceitn.eu/) funded by the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No. 859957. The authors would like to thank the Lloyd's Register Foundation (LRF), a charitable foundation in the U.K. helping to protect life and property by supporting engineeringrelated education, public engagement, and the application of research. The authors gratefully acknowledge the support of these organizations which have enabled the research reported in this paper.The accurate modeling of engineering systems and processes using Petri nets often results in complex graph representations that are computationally intensive, limiting the potential of this modeling tool in real life applications. This paper presents a methodology to properly define the optimal structure and properties of a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative example and a system reliability engineering case study, showing satisfactory results. The results also show that the method allows flexible reduction of the structure of the complex Petri net model taken as reference, and provides numerical justification for the choice of the reduced model structure.European Commission 859957Lloyd's Register Foundation (LRF), a charitable foundation in the U.K

Integration of prognostics at a system level: a Petri net approach

This paper presents a mathematical framework for modeling prognostics at a system level, by combining the prognostics principles with the Plausible Petri nets (PPNs) formalism, first developed in M. Chiach´ıo et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172]. The main feature of the resulting framework resides in its efficiency to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about the system state like the probability distribution of end-of-life, information from sensors, and information coming from expert knowledge. In addition, the proposed methodology allows us to rigorously model the flow of information through logic operations, thus making it useful for nonlinear control, Bayesian updating, and decision making. A degradation process of an engineering sub-system is analyzed as an example of application using condition-based monitoring from sensors, predicted states from prognostics algorithms, along with information coming from expert knowledge. The numerical results reveal how the information from sensors and prognostics algorithms can be processed, transferred, stored, and integrated with discrete-event maintenance activities for nonlinear control operations at system level

A reliability-based prognostics framework for railway track management

Railway track geometry deterioration due to traffic loading is a complex problem with important implications in cost and safety. Without appropriate maintenance, track deterioration can lead to severe speed restrictions or disruptions, and in extreme cases, to train derailment. This paper proposes a physics-based reliability-based prognostics framework as a paradigm shift to approach the problem of railway track management. As key contribution, a geo-mechanical elastoplastic model for cyclic ballast settlement is adopted and embedded into a particle filtering algorithm for sequential state estimation and RUL prediction. The suitability of the pro- posed methodology is investigated and discussed through a case study using published data taken from a laboratory simulation of train loading and tamping on ballast carried out at the University of Nottingham (UK)

An information theoretic approach for knowledge representation using Petri nets

A new hybrid approach for Petri nets (PNs) is proposed in this paper by combining the PNs principles with the foundations of information theory for knowledge representation. The resulting PNs have been named Plausible Petri nets (PPNs) mainly because they can handle the evolution of a discrete event system together with uncertain (plausible) information about the system using states of information. This paper overviews the main concepts of classical PNs and presents a method to allow uncertain information exchange about a state variable within the system dynamics. The resulting methodology is exemplified using an idealized expert system, which illustrates some of the challenges faced in real-world applications of PPNs

Robust optimal sensor configuration using the value of information

This paper is part of the SAFE-FLY project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie (Grant Agreement No. 721455). The authors acknowledge the support acquired by the Brazilian National Council of Research CNPq (Grant Agreement ID: 314168/2020-6).Sensing is the cornerstone of any functional structural health monitoring technology, with sensor number and placement being a key aspect for reliable monitoring. We introduce for the first time a robust methodology for optimal sensor configuration based on the value of information that accounts for (1) uncertainties from updatable and nonupdatable parameters, (2) variability of the objective function with respect to nonupdatable parameters, and (3) the spatial correlation between sensors. The optimal sensor configuration is obtained by maximizing the expected value of information, which leads to a cost-benefit analysis that entails model parameter uncertainties. The proposed methodology is demonstrated on an application of structural health monitoring in plate-like structures using ultrasonic guided waves. We show that accounting for uncertainties is critical for an accurate diagnosis of damage. Furthermore, we provide critical assessment of the role of both the effect of modeling and measurement uncertainties and the optimization algorithm on the resulting sensor placement. The results on the health monitoring of an aluminum plate indicate the effectiveness and efficiency of the proposed methodology in discovering optimal sensor configurations.European Union's Horizon 2020 Research and Innovation Programme 721455Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 314168/2020-

A new algorithm for prognostics using subset simulation

This work presents an efficient computational framework for prognostics by combining the particle filter-based prognostics principles with the technique of Subset Simulation, first developed in S.K. Au and J.L. Beck [Probabilistic Engrg. Mech., 16 (2001), pp. 263-277], which has been named PFP-SubSim. The idea behind PFP-SubSim algorithm is to split the multi-step-ahead predicted trajectories into multiple branches of selected samples at various stages of the process, which correspond to increasingly closer approximations of the critical threshold. Following theoretical development, discussion and an illustrative example to demonstrate its efficacy, we report on experience using the algorithm for making predictions for the end-of-life and remaining useful life in the challenging application of fatigue damage propagation of carbon-fibre composite coupons using structural health monitoring data. Results show that PFP-SubSim algorithm outperforms the traditional particle filter-based prognostics approach in terms of computational efficiency, while achieving the same, or better, measure of accuracy in the prognostics estimates. It is also shown that PFP-SubSim algorithm gets its highest efficiency when dealing with rare-event simulation

Notas introductorias sobre fiabilidad estructural

Libro de apuntes de apoyo a la docencia para el curso "Fiabilidad y Daño Continuo" del Máster de Estructuras (código M63/56/1​)​.El material que se recoge en este documento está especialmente concebido para sentar las bases teóricas así como para reiterar acerca de los fundamentos matemáticos de la fiabilidad. Al mismo tiempo, los autores pretenden presentar un material que, en un futuro, puede llegar a ser un libro de texto sobre ingeniería de fiabilidad, en el cual se aborde la fiabilidad desde una perspectiva más amplia, con especial atención a la fiabilidad de sistemas. Finalmente, conviene recordar al alumno que debe ampliar y contrastar el contenido a través del material de referencia recomendado en el apartado de bibliografía

Notas sobre mécanica de materiales compuestos

Estas notas han sido preparadas como apoyo a los estudiantes del Máster en Ingeniería de Estructuras de la Universidad de Granada, con el objetivo de que encuentren en ellas un manual básico para adentrarse en el cálculo de materiales compuestos. También van dirigidos a aquellos estudiantes de ingeniería que deciden basar su proyecto fin de carrera en estos materiales

Curso de introducción a estructuras de fibra de carbono

Material de apoyo a la docencia.Actualmente la utilización de materiales avanzados de alta eficacia, como los composites de fibra de carbono o vidrio originarios de la industria aeronáutica y aeroespacial, se presenta como alternativa viable en el diseño de estructuras de ingeniería civil en las que las exigencias de ligereza, durabilidad y tiempo de construcción se convierten en aspectos críticos del diseño. Desde los últimos 10 años se está asistiendo a un aumento importante a nivel mundial de las aplicaciones de materiales avanzados en construcción, y en particular en estructuras de ingeniería y arquitectura civil: puentes, estructuras de arquitectura singular, estructuras offshore, sistemas de alma- cenamiento energético, etc. Estados Unidos, Japón, Suiza, Reino Unido y Dinamarca ente otros países tecnológicamente avanzados, cuentan ya con numerosos puentes y estructuras de ingeniería realizadas íntegramente con estos materiales. Así mismo, en estos países se ha creado una red empresarial en torno a los nuevos materiales que está suponiendo en ciertos casos un importante impulso económico y una revolución tecnológica en el sector de la construcción.Este curso es una introducción a la tecnología y diseño, y se plantea desde un punto de vista divulgativo y práctico de forma que el alumno no solo conozca una nueva tecnología sino además un novedoso sector de la industria con nuevas oportunidades.

Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation

The accurate modeling of engineering systems and processes using Petri nets often results in complex graph representations that are computationally intensive, limiting the potential of this modeling tool in real life applications. This paper presents a methodology to properly define the optimal structure and properties of a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative example and a system reliability engineering case study, showing satisfactory results. The results also show that the method allows flexible reduction of the structure of the complex Petri net model taken as reference, and provides numerical justification for the choice of the reduced model structure