Relevant bibliographies by topics / Stochastic deterioration

Academic literature on the topic 'Stochastic deterioration'

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Published: 25 May 2024

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Journal articles on the topic "Stochastic deterioration"

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Naserabadi, Bahar, Abolfazl Mirzazadeh, and Sara Nodoust. "A New Mathematical Inventory Model with Stochastic and Fuzzy Deterioration Rate under Inflation." Chinese Journal of Engineering 2014 (August 14, 2014): 1–10. http://dx.doi.org/10.1155/2014/347857.

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This paper develops an inventory model for items with uncertain deterioration rate, time-dependent demand rate with nonincreasing function, and allowable shortage under fuzzy inflationary situation. The goods are not deteriorating upon reception, but the deteriorating starts after elapsing a specified time. The lead time and inflation rate are both uncertain in the model. The resultant effect of inflation and time value of money is assumed to be fuzzy in nature and also we consider lead time as a fuzzy function of order quantity. Furthermore the following different deterioration rates have been considered: for the first case we consider fuzzy deterioration rate and for the second case we assume that the deterioration rate is time dependent and follows Weibull distribution with three known parameters. Since the inflation rate, deterioration rate, and the lead time are fuzzy numbers, the objective function becomes fuzzy. Therefore the estimate of total costs for each case is derived using signed distance technique for defuzzification. The optimal replenishment policy for the model is to minimize the total present value of inventory system costs, derived for both the above mentioned policies. Numerical examples are then presented to illustrate how the proposed model is applied.
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Zhou, Binghai, Faqun Qi, and Hongyu Tao. "Condition-based maintenance modeling for a two-stage deteriorating system with random changes based on stochastic process." Journal of Quality in Maintenance Engineering 23, no. 4 (October 9, 2017): 383–99. http://dx.doi.org/10.1108/jqme-11-2015-0061.

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Purpose The purpose of this paper is to develop a condition-based maintenance (CBM) model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process. Design/methodology/approach Regarding environmental changes as random shocks, the effect of environmental changes on the deterioration process is considered. Then, non-homogeneous Poison process and non-stationary gamma process are introduced to model the deterioration pitting initiation process and the deterioration pitting growth process, respectively. Finally, based on the deterioration model, a CBM policy is put forward to obtain the optimal inspection interval by minimizing the expected maintenance cost rate. Numerical simulations are given to optimize the performance of the deteriorating system. Meanwhile, comparisons between a single-stage deterioration model and a two-stage deterioration model are conducted to demonstrate the application of the proposed approach. Findings The result of simulation verifies that the deterioration rate is not constant in the life cycle and is affected by the environment. Furthermore, the result shows that the two-stage deterioration model proposed makes up for the shortage of single-stage deterioration models and can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models. Practical implications In practical situations, except for normal deterioration caused by internal factors, many systems are also greatly influenced by the random shocks during operation, which are probably caused by the environmental changes. What is more, most systems have self-protection ability in some extent that protects them to keep running as new ones for some time. Under such circumstances, the two-stage deterioration model proposed can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models. In the combination with the bootstrap estimation, the paper obtains the life distributions with approximate 95 percent confidence intervals which can provide valuable information for practical system maintenance scheduling. Originality/value This paper presents a new CBM model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process. Considering the effect of the environmental change on the system deterioration process, a two-stage deterioration model with environmental change factors is proposed to describe the system deterioration.
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Vale, Cecília, and Isabel M. Ribeiro. "RAILWAY CONDITION-BASED MAINTENANCE MODEL WITH STOCHASTIC DETERIORATION." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 20, no. 5 (October 10, 2014): 686–92. http://dx.doi.org/10.3846/13923730.2013.802711.

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The application of mathematical programming for scheduling preventive maintenance in railways is relatively new. This paper presents a stochastic mathematical model designed to optimize and to predict tamping operations in ballasted tracks as preventive condition-based maintenance. The model is formulated as a mixed 0–1 nonlinear program that considers real technical aspects as constraints: the reduction of the geometrical track quality over time is characterized by the deterioration rate of the standard deviation of the longitudinal level; the track layout; the dependency of the track recovery on its quality at the moment of the maintenance operation; the limits for preventive maintenance that depend on the maximum permissible train speed. In the model application, a railway stretch with 51.2 km of length is analysed for a time period of five years. The deterioration model is stochastic and represents the reduction of the standard deviation of the longitudinal level over time. The deterioration rate of the standard deviation of the longitudinal level is simulated by Monte Carlo techniques, considering the three parameters Dagum probabilistic distribution fitted with real data (Vale, Simões 2012). Two simulations are performed and compared: stochastic simulation in space; stochastic simulation in space and time. The proposed condition-based maintenance model is able to produce optimal schedules within appropriate computational times.
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Li, Junxiang, Jianqiao Chen, and Zhiqiang Chen. "A new cumulative damage model for time-dependent reliability analysis of deteriorating structures." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 2 (November 20, 2019): 290–302. http://dx.doi.org/10.1177/1748006x19886157.

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Performance and reliability of structures will deteriorate with the effects of loads, environment, and interior factors of materials. In this article, a novel cumulative damage model is developed for time-dependent reliability analysis of deteriorating structures. The deterioration is a combination of three stochastic processes: the gradual deterioration posed by aging effects, the sudden deterioration caused by transient loads, and the additional deterioration introduced by sustained loads. The aging effect is modeled as a gamma process, while the transient load is described by a Poisson process. The sustained load is modeled by a stationary binomial process and a Poisson square wave process, respectively. The load threshold for three different scenarios are all considered and applied to not only the transient loads but also the sustained loads. The time-dependent reliability of deteriorating structures is then evaluated based on this model via semi-analytical methods or numerical simulation methods. Three numerical examples and an example involving a natural gas pipeline are used to validate the effectiveness of the proposed model for computing the time-dependent reliability.
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Langeron, Yves, Antoine Grall, and Anne Barros. "Joint maintenance and controller reconfiguration policy for a gradually deteriorating control system." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 4 (March 31, 2017): 339–49. http://dx.doi.org/10.1177/1748006x17692155.

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This paper tackles the maintenance decision-making of gradually deteriorating control systems. The main feature of these systems is their ability within a closed-loop to drive actuators in order to set the controlled process in a given state. Usually, the existing literature deals with the diagnosis of faults of a control system and the means to recover system performances after their appearance. The controller reconfiguration is one of these means. The root cause of a fault is rarely argued nor its occurrence time. Before designing maintenance policies, this paper proposes a stochastic modeling framework of a degrading control system focusing on the actuator deterioration. It is based on the assumption of a close relationship between the controller setting, the actuator deterioration and finally its faulty situations. These latter are related to given degradation thresholds. Due to the stochastic nature of the deterioration, the thresholds hitting times are random. The obtained model allows the conditional reliability of the actuator to be assessed and then the prognosis of its residual useful life (RUL). This RUL is finally used to achieve two maintenance policies based upon the reconfiguration of the controller which is therefore considered as a new maintenance action.
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Maes, Marc A. "Updating Performance and Reliability of Concrete Structures Using Discrete Empirical Bayes Methods." Journal of Offshore Mechanics and Arctic Engineering 124, no. 4 (October 22, 2002): 239–44. http://dx.doi.org/10.1115/1.1491973.

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When performing a probabilistic assessment of the reliability of deteriorating structures, we often need to integrate the results of different inspections in time, within the models used to analyze the progress of deterioration. A new framework is described in this paper. It rests on a special case of the empirical Bayes method where the non-observable parameter is a discrete random variable with a relatively small number of outcomes. Various likelihood functions are derived. They are based on mixtures of deterioration scenarios. It is shown how the method can be used to calibrate the response of a stochastic deterioration model and to merge with a time-dependent reliability analysis. Examples relating to the long-term chloride corrosion in a reinforced concrete slab are presented in the paper.
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Filina-Dawidowicz, Ludmiła, and Mykhaylo Postan. "Stochastic model of deteriorating cargo transshipment at port’s terminal under irregular arrival of ships." SHS Web of Conferences 58 (2018): 01010. http://dx.doi.org/10.1051/shsconf/20185801010.

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In the article the stochastic model is analyzed for deteriorating cargo transshipment taking into account the irregularity of arrived ships movement and cargo removing from warehouse with the regular surface transport. It is assumed that unloading rate of ships is given and fixed, but hold capacities of ships are randomly varying. During storage the cargo is subjected to deterioration with the given intensity. The problem of stationary probabilistic joint distribution of number of ships in terminal and quantity of cargo in warehouse is investigated. For this purpose the theory of Markov drift processes and methods of queueing theory are used. The problem of optimal value of deterioration rate is formulated and solved.
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Jia, Gaofeng, and Paolo Gardoni. "State-dependent stochastic models: A general stochastic framework for modeling deteriorating engineering systems considering multiple deterioration processes and their interactions." Structural Safety 72 (May 2018): 99–110. http://dx.doi.org/10.1016/j.strusafe.2018.01.001.

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Ebrahimi, Nader. "A stochastic covariate failure model for assessing system reliability." Journal of Applied Probability 38, no. 3 (September 2001): 761–67. http://dx.doi.org/10.1239/jap/1005091039.

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Many failure mechanisms can be traced to an underlying deterioration process, and stochastically changing covariates may influence this process. In this paper we propose an alternative model for assessing a system's reliability. The proposed model expresses the failure time of a system in terms of a deterioration process and covariates. When it is possible to measure deterioration as well as covariates, our model provides more information than failure time for the purpose of assessing and improving system reliability. We give several properties of our proposed model and also provide an example.
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Ebrahimi, Nader. "A stochastic covariate failure model for assessing system reliability." Journal of Applied Probability 38, no. 03 (September 2001): 761–67. http://dx.doi.org/10.1017/s002190020001891x.

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Many failure mechanisms can be traced to an underlying deterioration process, and stochastically changing covariates may influence this process. In this paper we propose an alternative model for assessing a system's reliability. The proposed model expresses the failure time of a system in terms of a deterioration process and covariates. When it is possible to measure deterioration as well as covariates, our model provides more information than failure time for the purpose of assessing and improving system reliability. We give several properties of our proposed model and also provide an example.
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Dissertations / Theses on the topic "Stochastic deterioration"

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SILVA, Rodrigo Bernardo da. "A Bayesian approach for modeling stochastic deterioration." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/5610.

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Made available in DSpace on 2014-06-12T17:40:37Z (GMT). No. of bitstreams: 2 arquivo720_1.pdf: 2087569 bytes, checksum: 4e440439e51674690e086dbc501c7a58 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010
Conselho Nacional de Desenvolvimento Científico e Tecnológico
A modelagem de deterioracão tem estado na vanguarda das analises Bayesianas de confiabilidade. As abordagens mais conhecidas encontradas na literatura para este proposito avaliam o comportamento da medida de confiabilidade ao longo do tempo a luz dos dados empiricos, apenas. No contexto de engenharia de confiabilidade, essas abordagens têm aplicabilidade limitada uma vez que frequentemente lida-se com situacões caracterizadas pela escassez de dados empiricos. Inspirado em estrategias Bayesianas que agregam dados empiricos e opiniões de especialistas na modelagem de medidas de confiabilidade não-dependentes do tempo, este trabalho propõe uma metodologia para lidar com confiabilidade dependente do tempo. A metodologia proposta encapsula conhecidas abordagens Bayesianas, como metodos Bayesianos para combinar dados empiricos e opiniões de especialistas e modelos Bayesianos indexados no tempo, promovendo melhorias sobre eles a fim de encontrar um modelo mais realista para descrever o processo de deterioracão de um determinado componente ou sistema. Os casos a serem discutidos são os tipicamente encontrados na pratica de confiabilidade (por meio de simulacão): avaliacão dos dados sobre tempo de execucão para taxas de falha e a quantidade de deterioracão, dados com base na demanda para probabilidade de falha; e opiniões de especialistas para analise da taxa de falha, quantidade de deterioracão e probabilidade de falha. Estes estudos de caso mostram que o uso de informacões especializadas pode levar a uma reducão da incerteza sobre distribuicões de medidas de confiabilidade, especialmente em situacões em que poucas ou nenhuma falha e observada.
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Ahmadi, Reza. "Stochastic modelling and maintenance optimization of systems subject to deterioration." Thesis, City University London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540627.

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Hackl, Jürgen. "Generic Framework for Stochastic Modeling of Reinforced Concrete Deterioration Caused by Corrosion." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for konstruksjonsteknikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23861.

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Reinforced concrete structures constitute an important fraction of the building infrastructure. This infrastructure is aging and a large number of structures will exceed the prescribed service period in the next decades. The aging of concrete structures is often accompanied by correspondent deterioration mechanisms. One of the major deterioration mechanisms is the corrosion of the reinforcing steel, caused by chloride ions or carbon dioxide exposure.The decisions, made in connection to possible repair or renewals of these structures, have major implications on safety and cost efficiency in a societal dimension. Public authorities, entitled to administrate the infrastructure, are in need of schemes and methodologies that facilitate the optimal management of the already existing stock of structures, especially in regard to repair and maintenance planning.In this thesis a generic framework for a stochastic modeling of reinforced concrete deterioration caused by corrosion is presented. This framework couples existing probabilistic models for chloride and carbonation initiation with models for the propagation and the effects of corrosion. For this purpose, a combination of structural reliability analysis and Bayesian networks is used for the reliability assessment of the reinforced concrete structure.This approach allows to compute probabilities of rare events for complex structures in an efficient way to update the model with new information from measurements, monitoring and inspection results.This framework enables, for the first time, a holistic view of the current service life models, with corresponding sensitivity studies and finding optimal decisions for treating deteriorated reinforced concrete structures. The temporal evolvement of structures can also be represented and analyzed within this framework.
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Hwisu, Shin. "Stochastic Analysis For Water Pipeline System Management." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/202696.

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Chang, Minwoo. "Investigating and Improving Bridge Management System Methodologies Under Uncertainty." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5039.

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This dissertation presents a novel procedure to select explanatory variables, without the influence of human bias, for deterioration model development using National Bridge Inventory (NBI) data. Using NBI information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection is to use expert opinion solicited from experienced engineers. This may introduce human influenced biases into the deterioration modeling process. A framework using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. Additionally, the cross validation analysis and solution path is used as a standard for the selection of minimum number of explanatory variables. The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the L2 relative error norm. The final chapter presents a new method to develop stochastic deterioration models using logistic regression. The relative importance amongst explanatory variables is used to develop a classification tree for Wyoming bridges. The bridges in a subset are commonly associated with several explanatory variables, so that the deterioration models can be more representative and accurate than using a single explanatory variable. The logistic regression is used to introduce the stochastic contribution into the deterioration models. In order to avoid missing data problems, the binary categories condition rating, either remaining the same or decreased, are considered for logistic regression. The probability of changes in bridges’ condition rating is obtained and the averages for same condition ratings are used to create transition probability matrix for each age group. The deterioration model based on Markov chain are developed for Wyoming bridges and compared with the previous model based on percentage prediction and optimization approach. The prediction error is analyzed, which demonstrates the considerable performance of the proposed method and is suitable for relatively small data samples.
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Baingo, Darek. "A Framework for Stochastic Finite Element Analysis of Reinforced Concrete Beams Affected by Reinforcement Corrosion." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23063.

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Corrosion of reinforcing bars is the major cause of deterioration of reinforced concrete (RC) structures in North America, Europe, the Middle East, and many coastal regions around the world. This deterioration leads to a loss of serviceability and functionality and ultimately affects the structural safety. The objective of this research is to formulate and implement a general stochastic finite element analysis (SFEA) framework for the time-dependent reliability analysis of RC beams with corroding flexural reinforcement. The framework is based on the integration of nonlinear finite element and reliability analyses through an iterative response surface methodology (RSM). Corrosion-induced damage is modelled through the combined effects of gradual loss of the cross-sectional area of the steel reinforcement and the reduction bond between steel and concrete for increasing levels of corrosion. Uncertainties in corrosion rate, material properties, and imposed actions are modelled as random variables. Effective implementation of the framework is achieved by the coupling of commercial finite element and reliability software. Application of the software is demonstrated through a case study of a simply-supported RC girder with tension reinforcement subjected to the effects of uniform (general) corrosion, in which two limit states are considered: (i) a deflection serviceability limit state and (ii) flexural strength ultimate limit state. The results of the case study show that general corrosion leads to a very significant decrease in the reliability of the RC beam both in terms of flexural strength and maximum deflections. The loss of strength and serviceability was shown to be predominantly caused by the loss of bond strength, whereas the gradual reduction of the cross-sectional area of tension reinforcement was found to be insignificant. The load-deflection response is also significantly affected by the deterioration of bond strength (flexural strength and stiffness). The probability of failure at the end of service life, due to the effects of uniform corrosion-induced degradation, is observed to be approximately an order of magnitude higher than in the absence of corrosion. Furthermore, the results suggest that flexural resistance of corroded RC beams is controlled by the anchorage (bond) of the bars and not by the yielding of fully bonded tensile reinforcement at failure. This is significant since the end regions can be severely corroded due to chloride, moisture, and oxygen access at connections and expansion joints. The research strongly suggests that bond damage must be considered in the assessment of the time-dependent reliability of RC beams subjected to general corrosion.
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Yang, Jidong. "Road crack condition performance modeling using recurrent Markov chains and artificial neural networks." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000567.

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Zhu, Wenjin. "Maintenance of monitored systems with multiple deterioration mechanisms in dynamic environments : application to wind turbines." Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0005/document.

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Les travaux présentés contribuent à la modélisation stochastique de la maintenance de systèmes mono- ou multi-composants à détériorations et à modes de défaillances multiples en environnement dynamique. Dans ce cadre, les contributions portent d'une part sur la modélisation des processus de défaillance, et d'autre part sur la proposition de structures de décision de maintenance intégrant les différents types d'information de surveillance en ligne disponible sur le système (état de détérioration mesuré ou reconstruit, état de l'environnement, ...) et le développement des modèles mathématiques d'évaluation associés. Les modèles de détérioration et de défaillances proposés pour les systèmes mono-composants permettent de rendre compte de sources de détérioration multiples (chocs et détérioration graduelle) et d'intégrer les effets de l'environnement sur la dégradation. Pour les systèmes multi-composants, on insiste sur les risques concurrents, indépendants ou dépendants et sur l'intégration de l'environnement. Les modèles de maintenance développés sont adaptés aux modèles de détérioration proposés et permettent de prendre en compte la contribution de chaque source de détérioration dans la décision de maintenance, ou d'intégrer de l'information de surveillance indirecte dans la décision, ou encore de combiner plusieurs types d'actions de maintenance. Dans chaque cas, on montre comment les modèles développés répondent aux problématiques de la maintenance de turbines et de parcs éoliens
The thesis contributes to stochastic maintenance modeling of single or multi-components deteriorating systems with several failure modes evolving in a dynamic environment. In one hand, the failure process modeling is addressed and in the other hand, the thesis proposes maintenance decision rules taking into account available on-line monitoring information (system state, deterioration level, environmental conditions …) and develops mathematical models to measure the performances of the latter decision rules.In the framework of single component systems, the proposed deterioration and failure models take into account several deterioration causes (chocks and wear) and also the impact of environmental conditions on the deterioration. For multi-components systems, the competing risk models are considered and the dependencies and the impact of the environmental conditions are also studied. The proposed maintenance models are suitable for deterioration models and permit to consider different deterioration causes and to analyze the impact of the monitoring on the performances of the maintenance policies. For each case, the interest and applicability of models are analyzed through the example of wind turbine and wind turbine farm maintenance
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Kosgodagan, Alex. "High-dimensional dependence modelling using Bayesian networks for the degradation of civil infrastructures and other applications." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0020/document.

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Cette thèse explore l’utilisation des réseaux Bayésiens (RB) afin de répondre à des problématiques de dégradation en grandes dimensions concernant des infrastructures du génie civil. Alors que les approches traditionnelles basées l’évolution physique déterministe de détérioration sont déficientes pour des problèmes à grande échelle, les gestionnaires d’ouvrages ont développé une connaissance de modèles nécessitant la gestion de l’incertain. L’utilisation de la dépendance probabiliste se révèle être une approche adéquate dans ce contexte tandis que la possibilité de modéliser l’incertain est une composante attrayante. Le concept de dépendance au sein des RB s’exprime principalement de deux façons. D’une part, les probabilités conditionnelles classiques s’appuyant le théorème de Bayes et d’autre part, une classe de RB faisant l’usage de copules et corrélation de rang comme mesures de dépendance. Nous présentons à la fois des contributions théoriques et pratiques dans le cadre de ces deux classes de RB ; les RB dynamiques discrets et les RB non paramétriques, respectivement. Des problématiques concernant la paramétrisation de chacune des classes sont également abordées. Dans un contexte théorique, nous montrons que les RBNP permet de caractériser n’importe quel processus de Markov
This thesis explores high-dimensional deterioration-related problems using Bayesian networks (BN). Asset managers become more and more familiar on how to reason with uncertainty as traditional physics-based models fail to fully encompass the dynamics of large-scale degradation issues. Probabilistic dependence is able to achieve this while the ability to incorporate randomness is enticing.In fact, dependence in BN is mainly expressed in two ways. On the one hand, classic conditional probabilities that lean on thewell-known Bayes rule and, on the other hand, a more recent classof BN featuring copulae and rank correlation as dependence metrics. Both theoretical and practical contributions are presented for the two classes of BN referred to as discrete dynamic andnon-parametric BN, respectively. Issues related to the parametrization for each class of BN are addressed. For the discrete dynamic class, we extend the current framework by incorporating an additional dimension. We observed that this dimension allows to have more control on the deterioration mechanism through the main endogenous governing variables impacting it. For the non-parametric class, we demonstrate its remarkable capacity to handle a high-dimension crack growth issue for a steel bridge. We further show that this type of BN can characterize any Markov process
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Langeron, Yves. "Modélisation stochastique pour la sûreté de fonctionnement des systèmes commandés." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0001/document.

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Dans le contexte des systèmes commandés, l’effort de recherche est principalement porté sur la reconfiguration d’une loi de commande à l’apparition d’une situation de défaut. La reconfiguration a pour objectif de pallier au défaut et donc de maintenir les performances du système. La problématique principale de nos travaux est d’étudier ces systèmes du point de vue de leur sûreté de fonctionnement en s’interrogeant sur les causes qui engendrent une situation de défaut. Pour cela, il est supposé l’existence d’une relation étroite entre la commande d’un système, sa dégradation et ses défauts. Un cadre de modélisation stochastique de la dégradation est proposé intégrant l’usage du système ainsi que les différents modes de détérioration. Le pronostic de la durée de vie résiduelle RUL de l’actionneur -élément critique de ces systèmes- est dérivé de l’ensemble des modèles. La RUL est alors utilisée comme un outil de reconfiguration de la loi LQR (Linear Quadratic Regulator) d’un système mono-actionné dans le cadre d’une maintenance prédictive. L’impact de cette nouvelle politique de maintenance sur les performances statiques et dynamiques du système est évalué. Enfin, le comportement stochastique d’un système tolérant aux fautes basé sur une redondance d’actionneurs est étudié au travers des modèles développés
In the context of control systems, the research effort is focused on how to reconfigure the control law upon the occurrence of a faulty situation. The reconfiguration procedure aims to overcome the fault and thus to maintain system performances. The main issue of this thesis is to study these systems in terms of their dependability by questioning the causes that generate a fault. Then it is assumed a close relationship between the control of a system, its degradation and its faults. A stochastic modelling framework is proposed combining the use of the system and the various modes of deterioration. The actuator is assumed to be the most critical part of a system. The prognosis of its remaining life RUL is derived from these models. This RUL is then used as a tool for reconfiguring the LQR law (Linear Quadratic Regulator) of a system with a single actuator in the context of a predictive maintenance. The impact of this new maintenance policy on static and dynamic performances is assessed. Finally the stochastic behavior of a fault tolerant control system is studied by means of the achieved models
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Book chapters on the topic "Stochastic deterioration"

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Tran, H., and S. Setunge. "Deterioration Modeling of Concrete Bridges and Potential Nanotechnology Application." In Lecture Notes in Civil Engineering, 399–408. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_41.

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AbstractManagement of aging concrete bridges with limited resources can be a challenge for state authorities. Deterioration modeling of concrete bridges at the component level is essential to optimize maintenance actions and ensure the safety and serviceability of the bridge network. In this study we examined the Level 2 visual inspection data of a concrete bridge’s components collected over 4–5 inspection cycles with the objective of predicting deterioration of components and the bridge’s life cycle. With the increasing application of nanotechnology to increase the mechanical properties and durability of concrete material for bridge structures, the deterioration of nano-based concrete could be significantly different from conventional concrete. A range of deterioration prediction methods, including deterministic models and stochastic models, were examined to understand the validity of the different methods in predicting the deterioration of bridge components made of conventional and nano-based materials. A case study with a demonstration on a concrete open girder was investigated with regard to linear regression models and the stochastic Markov deterioration model. The outcomes can be used to support future study on the performance of conventional and nano-based concrete materials and their lifecycles in the asset management of bridges.
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Jia, Gaofeng, and Paolo Gardoni. "Stochastic life-cycle analysis and performance optimization of deteriorating engineering systems using state-dependent deterioration stochastic models." In Routledge Handbook of Sustainable and Resilient Infrastructure, 580–602. Abingdon, Oxon ; New York, NY : Routledge, 2019. |: Routledge, 2018. http://dx.doi.org/10.4324/9781315142074-30.

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Jha, Manoj K. "Optimal Highway Infrastructure Maintenance Scheduling Considering Deterministic and Stochastic Aspects of Deterioration." In Sustainable and Resilient Critical Infrastructure Systems, 231–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11405-2_9.

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Savage, Gordon J., and Young Kap Son. "Efficient Use of Meta-Models for Reliability-Based Design Optimization of Systems Under Stochastic Excitations and Stochastic Deterioration." In Handbook of Advanced Performability Engineering, 383–401. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55732-4_17.

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Fujimoto, Y., M. Mizutani, S. Swilem, and M. Asaka. "Inspection Strategy for Deteriorating Structures Based on Cost Minimization Approach." In Computational Stochastic Mechanics, 421–32. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_36.

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Kawai, Hajime, Junji Koyanagi, and Masamitsu Ohnishi. "Optimal Maintenance Problems for Markovian Deteriorating Systems." In Stochastic Models in Reliability and Maintenance, 193–218. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-540-24808-8_8.

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Lee, Chiang-Sheng, Chien-Hung Cho, Cheng-Thai Tsai, and Hsine-Jen Tsai. "Considering a Deteriorating EOQ Model Under Stochastic Demand and Shortage Allowed." In Lecture Notes in Production Engineering, 235–46. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18641-7_23.

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Filina-Dawidowicz, Ludmila, and Mykhaylo Postan. "Models of Stochastic Optimization for Deteriorating Cargo Inventory Control at Port’s Terminal." In Dynamics in Logistics, 40–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74225-0_6.

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Zhu, Yukui, Xiang Cao, Jian Dong, and Mengke Fu. "Optimization of Condition-Based Maintenance Strategy for Stochastic and Continuous Deteriorating Systems." In Lecture Notes in Electrical Engineering, 986–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7423-5_98.

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Feng, Ying, and Yanzhi Zhang. "Joint Pricing and Replenishment Policies for a Deteriorating Inventory Model with Stochastic Demand." In Communications in Computer and Information Science, 663–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23235-0_84.

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Conference papers on the topic "Stochastic deterioration"

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Castro, Inma T., and Lucía Bautista. "Multiple Deterioration Processes with Stochastic Arrival Intensity." In Proceedings of the 31st European Safety and Reliability Conference. Singapore: Research Publishing Services, 2021. http://dx.doi.org/10.3850/978-981-18-2016-8_707-cd.

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Aboura, Khalid, Bijan Samali, Keith Crews, and Jianchun Li. "Stochastic Deterioration Processes for Bridge Lifetime Assessment." In 2008 Third International Conference on Broadband Communications, Information Technology & Biomedical Applications. IEEE, 2008. http://dx.doi.org/10.1109/broadcom.2008.6.

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Xiang, Yisha, and C. Casady. "Time to Failure Behavior under a Stochastic Deterioration Model." In 2007 Proceedings Annual Reliability and Maintainability Sympsoium. IEEE, 2007. http://dx.doi.org/10.1109/rams.2007.328108.

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Mehrabani, Mehrdad Bahari, and Hua-Peng Chen. "PERFORMANCE PREDICTIONS OF COASTAL DEFENCES USING STOCHASTIC DETERIORATION MODELLING." In 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2017. http://dx.doi.org/10.7712/120217.5357.16869.

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Ahmed, Reem, Ahmed Assad, Eslam Mohammed Abdelkader, Tarek Zayed, and Fuzhan Nasiri. "Stochastic-based Deterioration Modeling of Elevators in Healthcare Facilities." In 2020 International Conference on Decision Aid Sciences and Application (DASA). IEEE, 2020. http://dx.doi.org/10.1109/dasa51403.2020.9317250.

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Srikanth, Ishwarya, and M. Arockiasamy. "Stochastic Time-Dependent Deterioration Models for Estimating Residual Service Life of Offshore Jacket-Type Platforms." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-62919.

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Abstract This paper presents deterioration models for maintenance planning of offshore jacket platform based on two methods: i) stochastic Markov-chain based model and ii) stochastic-mechanistic deterioration models based on steel corrosion rates. Markov-chain models require the estimation of transition probability matrix (TPM), which is typically derived from the inspection data. The global structural health condition of the jacket is computed based on the condition of individual elements and their criticality in terms of failure consequence. The criticality factors are established based on nonlinear static redundancy analyses. This method can model deterioration when routine inspection records of jacket members are available. When there is scarcity of inspection records, stochastic-mechanistic deterioration modeling approach can be used. Monte-Carlo simulations with established corrosion wastage models are utilized to estimate the time-dependent deterioration of jacket legs, horizontal and diagonal bracings in splash and immersion zones. This method is proposed when there is scarcity of inspection records. The deterioration models are further utilized to predict the timing for Maintenance, Repair and Rehabilitation (MRR) actions, and estimate the residual service life of the jacket platform. This study demonstrates the application of the proposed deterioration modeling approaches with a case study of a typical 4-legged offshore jacket platform.
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Hachem, Hassan, Hai Canh Vu, and Mitra Fouladirad. "Remaining Useful Lifetime Prediction and Noisy Stochastic Deterioration Process Considering Sensor Degradation." In Proceedings of the 31st European Safety and Reliability Conference. Singapore: Research Publishing Services, 2021. http://dx.doi.org/10.3850/978-981-18-2016-8_209-cd.

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Saad, Dina, Mohamed Bahaa, and Hesham Osman. "Optimization of Infrastructure Rehabilitation Funding Decisions Considering Vulnerability-Based Stochastic Deterioration Modelling." In Construction Research Congress 2020. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482858.034.

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He, David, Ruoyu Li, and Eric Bechhoefer. "Stochastic Modeling of Damage Physics for Mechanical Component Prognostics Using Condition Indicators." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86483.

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The health of a mechanical component deteriorates over time and its service life is randomly distributed and can be modeled by a stochastic deterioration process. For most of the mechanical components, the deterioration process follows a certain physical laws and their mean life to failure can be determined approximately by these laws. However, it is not easy to apply these laws for mechanical component prognostics in current health monitoring applications. In this paper, a stochastic modeling methodology for mechanical component prognostics with condition indicators used in current health monitoring applications is presented. The effectiveness of the methodology is demonstrated with a real shaft fatigue life prediction case study.
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Zuo, J., C. Cadet, Z. Li, C. Berenguer, and R. Outbib. "Fuel Cell Stochastic Deterioration Modeling for Energy Management in a Multi-stack System." In 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS). IEEE, 2022. http://dx.doi.org/10.1109/icrms55680.2022.9944566.

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Reports on the topic "Stochastic deterioration"

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Méndez-Vizcaíno, Juan C., and Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, October 2021. http://dx.doi.org/10.32468/be.1175.

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Fiscal sustainability in five of the largest Latin American economies is examined before and after the COVID-19 pandemic. For this purpose, the DSGE model in Bi(2012) and Hürtgen (2020) is used to estimate the Fiscal Limits and Fiscal Spaces for Peru, Chile, Mexico, Colombia, and Brazil. These estimates advance the empirical literature for Latin America on fiscal sustainability by offering new calculations stemming from a structural framework with alluring novel features: government default on the intensive margin; dynamic Laffer curves; utility-based stochastic discount factor; and a Markov-Switching process for public transfers with an explosive regime. The most notable additions to the existing literature for Latin America are the estimations of entire distributions of public debt limits for various default probabilities and that said limits critically hinge on both current and future states. Results obtained indicate notorious contractions of Fiscal Spaces among all countries during the pandemic, but the sizes of these were very heterogeneous. Countries that in 2019 had positive spaces and got closer to negative spaces in 2020, have since seen deterioration of their sovereign debt ratings or outlooks. Colombia was the only country to lose its positive Fiscal Space and investment grade, thereby joining Brazil, the previously sole member of both groups
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