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Journal articles on the topic 'Stochastic deterioration'

Author: Grafiati

Published: 25 May 2024

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1

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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2

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ahmed, Reem, Tarek Zayed, and Fuzhan Nasiri. "A Hybrid Genetic Algorithm-Based Fuzzy Markovian Model for the Deterioration Modeling of Healthcare Facilities." Algorithms 13, no. 9 (August 29, 2020): 210. http://dx.doi.org/10.3390/a13090210.

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Healthcare facilities are constantly deteriorating due to tight budgets allocated to the upkeep of building assets. This entails the need for improved deterioration modeling of such buildings in order to enforce a predictive maintenance approach that decreases the unexpected occurrence of failures and the corresponding downtime elapsed to repair or replace the faulty asset components. Currently, hospitals utilize subjective deterioration prediction methodologies that mostly rely on age as the sole indicator of degradation to forecast the useful lives of the building components. Thus, this paper aims at formulating a more efficient stochastic deterioration prediction model that integrates the latest observed condition into the forecasting procedure to overcome the subjectivity and uncertainties associated with the currently employed methods. This is achieved by means of developing a hybrid genetic algorithm-based fuzzy Markovian model that simulates the deterioration process given the scarcity of available data demonstrating the condition assessment and evaluation for such critical facilities. A nonhomogeneous transition probability matrix (TPM) based on fuzzy membership functions representing the condition, age and relative deterioration rate of the hospital systems is utilized to address the inherited uncertainties. The TPM is further calibrated by means of a genetic algorithm to circumvent the drawbacks of the expert-based models. A sensitivity analysis was carried out to analyze the possible changes in the output resulting from predefined modifications to the input parameters in order to ensure the robustness of the model. The performance of the deterioration prediction model developed is then validated through a comparison with a state-of-art stochastic model in contrast to real hospital datasets, and the results obtained from the developed model significantly outperformed the long-established Weibull distribution-based deterioration prediction methodology with mean absolute errors of 1.405 and 9.852, respectively. Therefore, the developed model is expected to assist decision-makers in creating more efficient maintenance programs as well as more data-driven capital renewal plans.

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12

Kumar, Ramesh, Daren B. H. Cline, and Paolo Gardoni. "A stochastic framework to model deterioration in engineering systems." Structural Safety 53 (March 2015): 36–43. http://dx.doi.org/10.1016/j.strusafe.2014.12.001.

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13

Ohlmann, Jeffrey W., and James C. Bean. "Resource-constrained management of heterogeneous assets with stochastic deterioration." European Journal of Operational Research 199, no. 1 (November 2009): 198–208. http://dx.doi.org/10.1016/j.ejor.2008.11.005.

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14

Prakash, Om, A. R. Roy, and A. Goswami. "Stochastic manufacturing system with process deterioration and machine breakdown." International Journal of Systems Science 45, no. 12 (March 4, 2013): 2539–51. http://dx.doi.org/10.1080/00207721.2013.773469.

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15

Straub, Daniel. "Stochastic Modeling of Deterioration Processes through Dynamic Bayesian Networks." Journal of Engineering Mechanics 135, no. 10 (October 2009): 1089–99. http://dx.doi.org/10.1061/(asce)em.1943-7889.0000024.

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16

Levitin, Gregory, and Maxim Finkelstein. "A new stress–strength model for systems subject to stochastic shocks." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 2 (January 31, 2017): 172–79. http://dx.doi.org/10.1177/1748006x16689543.

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A new general approach for obtaining system survival probability under the Poisson shock process is suggested. It takes into account the explicitly defined distribution of shock magnitude (stress) and system strength deterioration caused by the previous shocks. The approach can be used for any form of stress distribution, strength deterioration function and stress–strength interplay model. Specifically, we use the contest success function to model the stress–strength interplay and a cumulative stress ratio form function to represent strength deterioration. Numerical examples illustrating the obtained results are presented.

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17

Morcous, George, and Zoubir Lounis. "Integration of stochastic deterioration models with multicriteria decision theory for optimizing maintenance of bridge decks." Canadian Journal of Civil Engineering 33, no. 6 (June 1, 2006): 756–65. http://dx.doi.org/10.1139/l06-011.

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This paper presents a new approach to optimizing the maintenance of concrete bridge decks. This approach combines a stochastic deterioration model and a multiobjective optimization model. The stochastic deterioration model is based on the first-order Markov chain, which predicts the probabilistic time variation of bridge deck conditions. The multiobjective optimization model takes into account two important and conflicting criteria: the minimization of maintenance costs and the maximization of the network condition. This approach achieves the best compromise between these competing criteria while considering the uncertainty in bridge deck deterioration. The feasibility and capability of the proposed approach are demonstrated with field data for a sample network of bridge decks obtained from the Ministère des Transports du Québec database. This example illustrates the effectiveness of the proposed approach in determining the optimal set of maintenance alternatives for reinforced concrete bridge decks when two or more relevant optimization criteria are taken into consideration.Key words: concrete bridge deck, maintenance management, multicriteria optimization, Markov chain, deterioration model.

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18

Lee, Cheol-Eung. "Simplified Method for Estimation of Mean Residual Life of Rubble-mound Breakwaters." Journal of Korean Society of Coastal and Ocean Engineers 34, no. 2 (April 28, 2022): 37–45. http://dx.doi.org/10.9765/kscoe.2022.34.2.37.

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A simplified model using the lifetime distribution has been presented to estimate the Mean Residual Life (MRL) of rubble-mound breakwaters, which is not like a stochastic process model based on time-dependent history data to the cumulative damage progress of rubble-mound breakwaters. The parameters involved in the lifetime distribution can be easily estimated by using the upper and lower limits of lifetime and their likelihood that made a judgement by several experts taking account of the initial design lifetime, the past sequences of loads, and others. The simplified model presented in this paper has been applied to the rubble-mound breakwater with TTP armor layer. Wiener Process (WP)-based stochastic model also has been applied together with Monte-Carlo Simulation (MCS) technique to the breakwater of the same condition having time-dependent cumulative damage to TTP armor layer. From the comparison of lifetime distribution obtained from each models including Mean Time To Failure (MTTF), it has found that the lifetime distributions of rubble-mound breakwater can be very satisfactorily fitted by log-normal distribution for all types of cumulative damage progresses, such as exponential, linear, and logarithmic deterioration which are feasible in the real situations. Finally, the MRL of rubble-mound breakwaters estimated by the simplified model presented in this paper have been compared with those by WP stochastic process. It can be shown that results of the presented simplified model have been identical with those of WP stochastic process until any ages in the range of MTT F regardless of the deterioration types. However, a little of differences have been seen at the ages in the neighborhood of MTTF, specially, for the linear and logarithmic deterioration of cumulative damages. For the accurate estimation of MRL of harbor structures, it may be desirable that the stochastic processes should be used to consider properly time-dependent uncertainties of damage deterioration. Nevertheless, the simplified model presented in this paper can be useful in the building of the MRL-based preventive maintenance planning for several kinds of harbor structures, because of which is not needed timedependent history data about the damage deterioration of structures as mentioned above.

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19

Jia, Gaofeng, and Paolo Gardoni. "Stochastic life-cycle analysis: renewal-theory life-cycle analysis with state-dependent deterioration stochastic models." Structure and Infrastructure Engineering 15, no. 8 (March 27, 2019): 1001–14. http://dx.doi.org/10.1080/15732479.2019.1590424.

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20

CASTANIER, BRUNO, ANTOINE GRALL, and CHRISTOPHE BÉRENGUER. "A STOCHASTIC MODEL FOR HYBRID MAINTENANCE POLICIES EVALUATION AND OPTIMIZATION." International Journal of Reliability, Quality and Safety Engineering 08, no. 03 (September 2001): 233–48. http://dx.doi.org/10.1142/s0218539301000499.

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We propose a hybrid maintenance policy which combines periodic (age-based or time-based) regulation-based inspections with aperiodic condition-based inspection/replacements for a stochastically and gradually deteriorating system. The stationary laws of the deterioration state of the maintained system are derived in order to evaluate the long-run average running cost on an infinite span generated by the proposed combined policy. A computable expression of the average cost is established using the regenerative or semi-regenerative properties of the stochastic process describing the maintained system state. The behavior of the proposed maintenance is illustrated through numerical experiments and it is shown that the cost incurred by the optimized combined policy is lower than the cost generated by the regulation-based maintenance alone.

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21

KOBAYASHI, Kiyoshi, Kiyoyuki KAITO, Kengo OBAMA, Rentaro HAYASHI, and Wataru FUKATANI. "STOCHASTIC DETERIORATION FRONTIER ANALYSIS FOR EFFICIENCY EVALUATION OF MANAGEMENT BODIES." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 72, no. 2 (2016): 173–90. http://dx.doi.org/10.2208/jscejipm.72.173.

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22

Chang, Minwoo, Marc Maguire, and Yan Sun. "Stochastic Modeling of Bridge Deterioration Using Classification Tree and Logistic Regression." Journal of Infrastructure Systems 25, no. 1 (March 2019): 04018041. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000466.

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23

Manafpour, Amir, Ilgin Guler, Aleksandra Radlińska, Farshad Rajabipour, and Gordon Warn. "Stochastic Analysis and Time-Based Modeling of Concrete Bridge Deck Deterioration." Journal of Bridge Engineering 23, no. 9 (September 2018): 04018066. http://dx.doi.org/10.1061/(asce)be.1943-5592.0001285.

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24

Han, Daeseok. "Stochastic Disaggregation and Aggregation of Localized Uncertainty in Pavement Deterioration Process." Journal of The Korean Society of Civil Engineers 33, no. 4 (July 30, 2013): 1651–64. http://dx.doi.org/10.12652/ksce.2013.33.4.1651.

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25

Bu, G. P., J. B. Son, J. H. Lee, H. Guan, M. Blumenstein, and Y. C. Loo. "Typical deterministic and stochastic bridge deterioration modelling incorporating backward prediction model." Journal of Civil Structural Health Monitoring 3, no. 2 (April 6, 2013): 141–52. http://dx.doi.org/10.1007/s13349-013-0044-5.

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26

Eryilmaz, Serkan. "Parallel and consecutive-k-out-of-n:F systems under stochastic deterioration." Applied Mathematics and Computation 227 (January 2014): 19–26. http://dx.doi.org/10.1016/j.amc.2013.10.081.

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27

Zhou, Yanxun, Yimin Zhang, and Guo Yao. "Stochastic forced vibration analysis of a tapered beam with performance deterioration." Acta Mechanica 228, no. 4 (December 27, 2016): 1393–406. http://dx.doi.org/10.1007/s00707-016-1764-5.

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28

Glazebrook, K. D. "Single-machine scheduling of stochastic jobs subject to deterioration or delay." Naval Research Logistics 39, no. 5 (August 1992): 613–33. http://dx.doi.org/10.1002/1520-6750(199208)39:5<613::aid-nav3220390503>3.0.co;2-p.

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29

Allaix, D. L., V. I. Carbone, and Giuseppe Mancini. "Stochastic Perturbation Analysis of Deteriorated Structures." Advanced Materials Research 905 (April 2014): 296–99. http://dx.doi.org/10.4028/www.scientific.net/amr.905.296.

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The paper addresses a generalized perturbation method for the uncertainty analysis of concrete structures subject to reinforcement corrosion. The initiation phase is modelled by the well known Ficks 2ndlaw of diffusion. Deterioration models derived from experimental studies and a non-linear finite element (FE) model are used to simulate the structural behaviour of beams from the serviceability to the ultimate conditions. The time of corrosion initiation and the failure load are expressed by a Taylor series expansion about the origin of the space of independent standard normal random variables.

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30

CHUN, Pang-jo, Daisuke SETO, and Mitao OHGA. "Derivation of deterioration prediction interval with stochastic Markov Chain model and Bayes estimation of individual deterioration characteristics of bridge." Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)) 68, no. 2 (2012): I_771—I_781. http://dx.doi.org/10.2208/jscejam.68.i_771.

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31

Benkherouf, Lakdere, and Lakhdar Aggoun. "ON A STOCHASTIC INVENTORY MODEL WITH DETERIORATION AND STOCK-DEPENDENT DEMAND ITEMS." Probability in the Engineering and Informational Sciences 16, no. 2 (April 2002): 151–65. http://dx.doi.org/10.1017/s0269964802162024.

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In this article, we propose a new continuous-time stochastic inventory model with deterioration and stock-dependent demand items. We then formulate the problem of finding the optimal impulse control schedule that minimizes the total expected return over an infinite horizon, as a quasivariational inequality (QVI) problem. The QVI is shown to lead to an (s, S) policy, where s and S are determined uniquely as a solution of some algebraic equations.

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Zhang, Bangcheng, Yubo Shao, Zhenchen Chang, Zhongbo Sun, and Yuankun Sui. "A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation." Applied Sciences 9, no. 3 (February 12, 2019): 613. http://dx.doi.org/10.3390/app9030613.

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Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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33

Semaan, Nabil, and Youssef Dib. "Comparison between the predicted performance curve and the Markov Chain models for structural performance of infrastructure components." MATEC Web of Conferences 289 (2019): 08006. http://dx.doi.org/10.1051/matecconf/201928908006.

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This paper compares the PPC model to a Markov Chain (MC) stochastic deterioration model. First, inspection data from the Société de Transport de Montréal (STM) is gathered and analyzed. Then Transition Probability Matrices (TPM) are developed, and, using Matlab, MC deterioration curves are developed. Comparison between MC and the PPC deterioration curves is performed for subway station walls and slabs. The comparison has shown that the useful service life can be as low as 2 years for components having many inspection history records, and very high as 30 years for components having very few inspection history records. The PPC model has always a higher useful service life estimate. Also, the MC has a ten times higher deterioration rate (0.2 per year) compared to the PPC model (0.02 per year). It can be concluded that the MC deterioration model requires a high amount of inspection data, and it is mathematically difficult to generate since most practicing managers and engineers have no background in Markov Chain modeling.

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34

Nanda, Asok K., and Amarjit Kundu. "On Improvement and Deterioration of A Repairable System Under Generalized Stochastic Orders." Calcutta Statistical Association Bulletin 63, no. 1-4 (March 2011): 259–72. http://dx.doi.org/10.1177/0008068320110114.

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35

Tan, Yang, and Michael X. Weng. "Optimal stochastic inventory control with deterioration and partial backlogging/service-level constraints." International Journal of Operational Research 16, no. 2 (2013): 241. http://dx.doi.org/10.1504/ijor.2013.051785.

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36

Hasan, Sahar, and Emad Elwakil. "Stochastic regression deterioration models for superstructure of prestressed concrete bridges in California." Journal of Structural Integrity and Maintenance 4, no. 2 (April 3, 2019): 97–108. http://dx.doi.org/10.1080/24705314.2019.1603194.

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37

Le Son, Khanh, Mitra Fouladirad, Anne Barros, Eric Levrat, and Benoît Iung. "Remaining useful life estimation based on stochastic deterioration models: A comparative study." Reliability Engineering & System Safety 112 (April 2013): 165–75. http://dx.doi.org/10.1016/j.ress.2012.11.022.

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38

Shahraki, Mohammad Reza. "Developing an Inventory Mathematical Model with Deterioration Variables for Discounted Stochastic Goods." Indian Journal of Science and Technology 7, no. 11 (November 20, 2014): 1750–55. http://dx.doi.org/10.17485/ijst/2014/v7i11.13.

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39

Sazvar, Zeinab, Mohammad Reza Akbari Jokar, and Armand Baboli. "A new order splitting model with stochastic lead times for deterioration items." International Journal of Systems Science 45, no. 9 (February 4, 2013): 1936–54. http://dx.doi.org/10.1080/00207721.2012.759301.

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40

Cao, Xiangang, Pengfei Li, and Song Ming. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven." Sustainability 13, no. 15 (July 31, 2021): 8548. http://dx.doi.org/10.3390/su13158548.

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Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability.

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Pellerey, Franco, and Patrizia Semeraro. "Ageing and stochastic comparisons for a covariate failure model." Journal of Applied Probability 39, no. 2 (June 2002): 421–25. http://dx.doi.org/10.1239/jap/1025131439.

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Ebrahimi (2001) proposes an interesting model for assessing a system's reliability, expressing the failure time of the system in terms of a deterioration process and covariates. He also provides illustrative examples and gives some properties of the model. In this note, we give conditions for negative ageing of the system's lifetime, and we correct one of his statements. Moreover, for the lifetimes of two systems of the same kind, some stochastic comparisons are presented.

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Pellerey, Franco, and Patrizia Semeraro. "Ageing and stochastic comparisons for a covariate failure model." Journal of Applied Probability 39, no. 02 (June 2002): 421–25. http://dx.doi.org/10.1017/s0021900200022646.

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Ebrahimi (2001) proposes an interesting model for assessing a system's reliability, expressing the failure time of the system in terms of a deterioration process and covariates. He also provides illustrative examples and gives some properties of the model. In this note, we give conditions for negative ageing of the system's lifetime, and we correct one of his statements. Moreover, for the lifetimes of two systems of the same kind, some stochastic comparisons are presented.

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Nazemi, Ehsan, and Kamran Shahanaghi. "Developing an Inspection Optimization Model Based on the Delay-Time Concept." Journal of Industrial Engineering 2015 (July 2, 2015): 1–7. http://dx.doi.org/10.1155/2015/843137.

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Infrastructures are considered as important facilities required for every country and society to be able to work properly. Aging and deterioration of such structures during their lifetime are a major concern both for maintenance researchers in the academic world and for the practitioners. This concern is mainly because the deterioration increases the maintenance costs dramatically and lowers the reliability, availability, and safety of the structural system. Preventive maintenance and inspection activities are the most usual means for keeping the structure in a good condition. This paper utilizes the concept of delay-time for developing the optimal inspection policy for deteriorating structures. In the proposed stochastic model, discrete times of inspection activities are taken as the decision variables of an optimization problem, in a way that the obtained aperiodic (nonuniform) inspection schedule minimizes the total downtime ratio of the structure. To illustrate the model capabilities, various numerical examples are solved and results are compared with the traditional periodic (uniform) inspection policies. The results indicate the substantial reduction in system downtime due to the wisely planned inspection schedule and the appropriate utilization of delay-time concept, which is indeed a powerful framework for inspection optimization problems.

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Childress, Suzanne, and Pablo Durango-Cohen. "On parallel machine replacement problems with general replacement cost functions and stochastic deterioration." Naval Research Logistics 52, no. 5 (August 2005): 409–19. http://dx.doi.org/10.1002/nav.20088.

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Huang, Dawen, Dengji Zhou, Xunkai Wei, Hao Wang, and Xuehong Zhao. "Gas path deterioration observation based on stochastic dynamics for reliability assessment of aeroengines." Reliability Engineering & System Safety 238 (October 2023): 109458. http://dx.doi.org/10.1016/j.ress.2023.109458.

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Wang, Cao. "Resistance Assessment of Service-Proven Aging Bridges Incorporating Deterioration-Load Dependency." Infrastructures 5, no. 1 (January 20, 2020): 10. http://dx.doi.org/10.3390/infrastructures5010010.

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The historical vehicles passed through an existing bridge can be regarded as proof-loading tests for the bridge, and, as a result, are evident of the bridge’s performance. Such service history information has been utilized to update the estimate of bridge resistance in previous studies with the help of a Bayesian method, where the resistance deterioration process was assumed to be independent of the vehicle load process. This assumption is, however, untenable in many cases where the deterioration stochastic process is statistically correlated with the load process (e.g., a greater load intensity may affect/accelerate the deterioration of structural resistance and the accumulation of structural fragility). With this regard, this paper investigates the effect of correlation between the resistance deterioration and load processes on updating the resistance of aging bridges with prior service load information. The copula function is employed to model the joint distribution of the correlated deterioration and load processes, with which the correlation is measured by the Kendall’s tau. A new method is developed in this paper to assess the updated bridge resistance taking into consideration the deterioration-load dependency in an explicit form. The applicability of the proposed method is illustrated using an existing RC beam bridge. The sensitivity analysis is conducted to examine how the deterioration-load dependency affects the updated resistance of service-proven aging bridges.

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Hwan Cha, Ji, and Sophie Mercier. "Transformed Lévy processes as state-dependent wear models." Advances in Applied Probability 51, no. 2 (June 2019): 468–86. http://dx.doi.org/10.1017/apr.2019.21.

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AbstractMany wear processes used for modeling accumulative deterioration in a reliability context are nonhomogeneous Lévy processes and, hence, have independent increments, which may not be suitable in an application context. In this work we consider Lévy processes transformed by monotonous functions to overcome this restriction, and provide a new state-dependent wear model. These transformed Lévy processes are first observed to remain tractable Markov processes. Some distributional properties are derived. We investigate the impact of the current state on the future increment level and on the overall accumulated level from a stochastic monotonicity point of view. We also study positive dependence properties and stochastic monotonicity of increments.

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Maes, M. A., X. Wei, and W. H. Dilger. "Fatigue reliability of deteriorating prestressed concrete bridges due to stress corrosion cracking." Canadian Journal of Civil Engineering 28, no. 4 (August 1, 2001): 673–83. http://dx.doi.org/10.1139/l01-031.

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In this paper, an analysis framework is presented to develop a relationship between fatigue reliability in a prestressed concrete bridge and the progress of stress corrosion cracking (SCC) in prestressing steel. The fatigue limit state uses a cumulative damage model for prestressing steel wires, which is a function of both stress range and minimum stress. The SCC model takes into account varying degrees of material susceptibility, stress regimes, and environmental conditions and is structured around three stages: initiation of micro-cracking, propagation, and macro-crack instability using linear elastic fracture mechanics. The framework is an overall time-dependent analysis of the safety against fatigue of a prestressed concrete bridge. It involves a stochastic analysis of the evolution of prestressing wire corrosion as a function of time and a time-dependent probabilistic analysis of the fatigue reliability of the prestressed concrete bridge suffering a certain degree of deterioration. The uncertainties involved in the fatigue model, the SCC model, and traffic actions are considered. The updating of uncertainties is simplified by considering a limited number of classes representative of the severity of SCC exposure. The framework is applied to three deteriorating highway bridges.Key words: fatigue analysis, prestressing strands, stress corrosion cracking, reliability assessment, prestressed concrete bridges, deterioration.

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Chang, Minwoo, and Marc Maguire. "Condition Rating Prediction Using an Interactive Deterioration Model Development Package." Applied Sciences 10, no. 24 (December 15, 2020): 8946. http://dx.doi.org/10.3390/app10248946.

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This paper presents an advanced method to determine explanatory variables required for developing deterioration models without the interference of human bias. Although a stationary set of explanatory variables is ideal for long-term monitoring and asset management, the penalty regression results vary annually due to the innate bias in the inspection data. In this study, weighting factors were introduced to consider the inspection data collected for several years, and the most stationary set was identified. To manage the substantial amount of inspection data effectively, we proposed a software package referred to as the Deterioration Model Development Package (DMDP). The objective of the DMDP is to provide a convenient platform for users to process and investigate bridge inspection data. Using the standardized data interpretation, the user can update an initial dataset for the deterioration model development when new inspection data are archived. The deterministic method and several stochastic approaches were included for the development of the deterioration models. The performances of the investigated methods were evaluated by estimating the error between the predicted and inspected condition ratings; further, this error was used for estimating the most effective number of explanatory variables for a given number of bridges.

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Rivera-Gómez, Héctor, Joselito Medina-Marin, Francisca Santana-Robles, Oscar Montaño-Arango, Irving Barragán-Vite, and Gabriel Cisneros-Flores. "Impact of Unreliable Subcontracting on Production and Maintenance Planning Considering Quality Decline." Applied Sciences 12, no. 7 (March 26, 2022): 3379. http://dx.doi.org/10.3390/app12073379.

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Manufacturing systems face several disturbances during production, such as sudden failures, defects, and unreliable subcontractors that reduce their production capacity. Currently, subcontracting represents an efficient alternative to support production decisions. The novelty of the study was the development of a new integrated model that properly coordinates production, subcontracting, and maintenances strategies in the context of stochastic uncertainty, quality deterioration, and random subcontracting availability. Such a set of characteristics has not been addressed before in the literature. A simulation–optimization approach was proposed to address such a stochastic model. A numerical case study was performed as an illustration of the approach and a comprehensive sensitivity analysis was performed to analyze the impact of several costs. Furthermore, the effect of the availability of the subcontractor and the producer was analyzed. The main finding of the study showed that the integrated model led to significant economic cost savings compared to other approaches that address such policies in isolation. The results also indicated that quality deterioration had a strong impact on the subcontracting rate and that the proposed joint control policy adequately coordinated these three key functions. The level of subcontracting participation was directly defined by its availability and the subcontracting cost.

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