Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

A cost-benefit approach for the evaluation of prognostics-updated maintenance strategies in complex dynamic systems

The implementation of maintenance strategies which integrate online condition data has the potential to increase availability and reduce maintenance costs. Prognostics techniques enable the implementation of these strategies through up-to-date remaining useful life estimations. However, a cost-benefit assessment is necessary to verify the scale of potential benefits of condition-based maintenance strategies and prognostics for a given application. The majority of prognostics applications focus on the evaluation of a specific failure mode of an asset. However, industrial systems are comprised of different assets with multiple failure modes, which in turn, work in cooperation to perform a system level function. Besides, these systems include time-dependent events and conditional triggering events which cause further effects on the system. In this context not only are the system-level prognostics predictions challenging, but also the cost-benefit analysis of condition-based maintenance policies. In this work we combine asset prognostics predictions with temporal logic so as to obtain an up-to-date system level health estimation. We use asset level and system level prognostics estimations to evaluate the cost-effectiveness of alternative maintenance policies. The application of the proposed approach enables the adoption of conscious trade-off decisions between alternative maintenance strategies for complex systems. The benefits of the proposed approach are discussed with a case study from the power industry

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Modelo multiobjetivo para la selección de estrategias óptimas de mantenimiento en sistemas multicomponentes: una aplicación en líneas de transmisión de energía eléctrica

A multi-objective model is proposed for defining optimal maintenance strategies, in systems composed of several interconnected elements. The optimal maintenance strategies derived correspond to a set of efficient actions, focused on maximizing the reliability of the system, and minimizing the associated costs. Optimization is carried out by using evolutionary algorithms type NSGA-II. For the evaluation of the system reliability, a procedure based on Monte Carlo simulation is used, which allows analyzing systems with different performance functions and for component configurations different from the classical ones (series, parallel, k-out-of-N). The proposal is applied to assess electrical power system components, specifically the insulator chains of the transmission lines. Several scenarios illustrate the proposed model. The strategies selected by the model prioritize the most important elements based on costs and/or maintenance. These strategies make up an approximate Pareto front, in which the decision-maker can choose the most suitable strategy according to their interests.En este artículo se formula un modelo multiobjetivo para seleccionar estrategias de mantenimiento óptimas en sistemas formados por varios elementos interconectados. Las aquí planteadas corresponden al conjunto de acciones eficientes, centradas en maximizar la confiabilidad del sistema y, a su vez, minimizar los costos asociados. La optimización se realiza mediante el uso de algoritmos evolutivos tipo NSGA-II. Para evaluar la confiabilidad del sistema se utiliza un procedimiento basado en simulación de Monte Carlo, que permite analizar sistemas con distintas funciones de desempeño y para configuraciones de componentes diferentes a las clásicas (serie, paralelo, k-out-of-N). La propuesta se analiza para los componentes de un sistema eléctrico de potencia, específicamente las cadenas de aisladores de las líneas de transmisión, y varios escenarios de cálculo. Las estrategias seleccionadas por el modelo priorizan los elementos más importantes, según costo o mantenimiento, y conforman un frente de Pareto aproximado donde el decisor puede seleccionar la más adecuada, de acuerdo con sus intereses

Propuesta de herramientas basadas en fiabilidad para el modelado de sistemas productivos complejos.

La importancia de los costes de mantenimiento en procesos intensivos en el uso de activos, puede alcanzar hasta el 40% de los costes de producción, como, por ejemplo, en los procesos de la gran minería del Cobre (Consejo Minero, 2015). Dada su relevancia, resulta indispensable un estudio acabado de cada uno de los procesos, bajo un enfoque de mantenimiento y de coste de ciclo de vida. El estudio y modelado de fiabilidad, es la piedra angular para un análisis de mantenimiento, ya que se relaciona directamente con el comportamiento de fallos de cada uno de los componentes hasta establecer la relación de dependencia dinámica de cada uno de los equipos en estudio, aspectos que son fundamentales para evaluar criticidad y proyectar costes en fases de inversión y operación (CAPEX y OPEX) (Parra et al., 2012). El modelado de fiabilidad, basa su análisis en la ocurrencia de los fallos de un equipo, a través de distribuciones probabilísticas que permiten ajustar los tiempos de buen funcionamiento, las que dan origen a la función de fiabilidad. Dentro de las distribuciones más utilizadas, están la Exponencial y la Weibull, que permiten modelar el comportamiento de un componente durante todo su ciclo de vida; con fases de rodaje, vida útil y degaste, a través de la curva de la bañera (Dhillon, 2006). El modelado de fiabilidad por componentes se hace extensivo a procesos productivos, lo que permite conocer la fiabilidad por componente y sistemas en su conjunto. Sobre este punto, existen diversas metodologías como Reliability Block Diagram (RBD) (Rausand and Hoyland, 2003; Guo and Yang, 2007), Cadenas de Markov (Welte, 2009), Árboles de Fallo (Rauzy et al., 2007), Gráficos de Fiabilidad (Distefano and Puliafito, 2009), Redes de Petri (PNs) (Volovoi, 2014), entre otros. No obstante a lo anterior, existen relaciones de equipos que, dada su configuración, no es posible modelarlas con las técnicas tradicionales. La realidad de los procesos industriales evidencia que una mayor flexibilidad en dichos procesos mejora la productividad, la eficiencia del propio proceso y, en definitiva, los resultados generales de la empresa. En ese contexto, los sistemas dinámicos alcanzan una gran importancia en el modelado de los procesos productivos. Los sistemas dinámicos son aquellos que cambian con el tiempo, es decir, pueden variar sus relaciones de dependencia con el entorno o bien, su habilidad de funcionar en diversos escenarios. El tema de investigación principal de la presente Tesis Doctoral, presentado en el formato por Compendio de Publicaciones, se desarrolla en la revisión y proposición de las técnicas de modelado de fiabilidad, para la evaluación de impacto de fiabilidad y fallos de elementos individuales que se encuentren inmersos en procesos productivos complejos, permitiendo evaluar la criticidad operacional de cada uno de ellos. La determinación del indicador de criticidad operacional es de vital importancia para la identificación de riesgos operacionales en el interior de los procesos productivos de las empresas, permitiendo facilitar el proceso de toma de decisión de manera efectiva. Actualmente , en la literatura existen diversas investigaciones desarrolladas para identificar los factores que afectan directamente la maximización de beneficios. Estos factores se fundamentan en la consideración empírica de los indicadores de fiabilidad, mantenibilidad y disponibilidad (RAM) (Viveros et al., 2012). Como resultado principal del trabajo de doctorado, se obtienen 3 artículos ISI – JCR y la presentación de 4 artículos en congresos internacionales con proceedings. En cada una de estas publicaciones, el candidato a doctor es el primer autor y su tutor, el segundo. El proyecto de Tesis Doctoral que se presenta, se enmarca dentro de la línea de investigación del grupo Sistemas Inteligentes de Mantenimiento - SIM, perteneciente al Departamento de Organización Industrial y Gestión de Empresas de la Universidad de Sevilla

Prognostics and health management for multi-component systems

The ever increasing number of manufacturing requirements is pushing original equipment manufacturers (OEM) to design more complex systems to meet industrial needs. These systems are being fitted with more components which bear stochastic and economic dependencies. Therefore maintaining such systems is becoming more and more of a challenge, especially due to their degradation processes becoming highly stochastic in nature. This thesis is concerned with the prognostics and health management (PHM) of such complex multi-component systems, whereby signal processing and health indicator extraction, diagnostics, prognostics and maintenance decision making in light of present stochastic and economic dependencies are considered. We introduce several novel approaches for dealing with systems that have multiple components. We first introduce a gearbox accelerated life testing platform that was designed with the objective of gathering experimental data for multi-component degradation models, for the reason that multi-component systems with inter-dependencies follow a highly stochastic degradation process which depends to an extent on their complex mechanical design. We then present our methodology for extracting accurate health indicators from multi-component systems by means of a time-frequency domain analysis. This sets the stage for degradation modelling, and so we show the development of a generic degradation model in which the degradation process of a component may be dependent on the operating conditions, the component's own state, and the state of the other components. We then show how to fit the models to data using particle filter. This method is then used for the data generated by the gearbox. Afterwards a diagnostic procedure is presented and uses Gaussian mixture models. This is used to uncover accelerated wear processes that take place when old worn out components are coupled with new healthy components. Finally economic dependency is considered where combining multiple maintenance activities has lower cost than performing maintenance on components separately. To select a component or components to be preventively maintained, adaptive preventive maintenance and opportunistic maintenance rules are proposed. A cost model is developed to find the optimal values of decision variables. In our work, we find that stochastic dependencies between components lead to accelerated degradation which causes unexpected faults and failures, and consequent economic losses. Although this work deals with stochastic dependence between components, it involves some engineering knowledge of the systems under study, and this makes application of the models on a large scale challenging to automate. Therefore, we make recommendation for future research that includes the development of end-to-end learning techniques such as deep learning. In doing so we can potentially use the time wave data and automatically extract the most relevant features for doing accurate prognostics, and therefore health management, of such systems. The research work in this thesis was motivated by the problems faced by industrial partners such as the world leading food system manufacturing company Marel in the Netherlands, which were part of the sustainable manufacturing and advanced robotics training network in Europe (SMART-e)

A review on maintenance optimization

To this day, continuous developments of technical systems and increasing reliance on equipment have resulted in a growing importance of effective maintenance activities. During the last couple of decades, a substantial amount of research has been carried out on this topic. In this study we review more than two hundred papers on maintenance modeling and optimization that have appeared in the period 2001 to 2018. We begin by describing terms commonly used in the modeling process. Then, in our classification, we first distinguish single-unit and multi-unit systems. Further sub-classification follows, based on the state space of the deterioration process modeled. Other features that we discuss in this review are discrete and continuous condition monitoring, inspection, replacement, repair, and the various types of dependencies that may exist between units within systems. We end with the main developments during the review period and with potential future research directions