Statistically Planned and Individually Improved Predictive Maintenance Management for Continuously Monitored Degrading Systems

You, Ming‐Yi; Liu, Fang; Wang, Wen; Meng, Guang

doi:10.1109/tr.2010.2085572

Cited by 76 publications

(39 citation statements)

References 21 publications

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“…This result is desired since the expectation of the RUL is required in some maintenance strategies [45,46]. In the following, we develop a parameters estimation algorithm to estimate and update the parameters in Equations (13) and (14).…”

Section: Real Time Rul Prediction With Mementioning

confidence: 99%

“…A classic work about the updating of the random parameters is proposed by Gebraeel et al [43], whose model established a linkage between the past and current degradation data of the congeneric items by a Bayesian mechanism. Following Gebraeel et al [43], some related issues and many variants and applications have been studied and reported [39,[44][45][46]. The work about this Bayesian updating mechanism for the WPME is also been studied in [41].…”

Section: Introductionmentioning

confidence: 99%

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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

et al. 2014

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Abstract:Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.

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Section: Real Time Rul Prediction With Mementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

et al. 2014

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“…PrM can be regarded as an improvement of CBM: the knowledge of the current degradation state of the component (diagnostics) is complemented with a prediction of its future behavior and thus of its Remaining Useful Life (RUL) (e.g., Fan et al 2011, Grall et al 2002, Lu et al 2007, You et al 2010. The accurate estimation of the RUL provides time to opportunely plan and prepare the repair or the replacement of the component, e.g., by delaying the maintenance to the next planned plant outage, by provisioning with spare parts only at time of necessity, by optimizing staff utilization, while remaining acceptably confident that the system will not fail before maintenance.…”

Section: Predictive Maintenancementioning

confidence: 99%

Evaluating maintenance policies by quantitative modeling and analysis

Zio

Compare

2013

Reliability Engineering & System Safety

110

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To cite this version:Enrico Zio, Michele Compare.Evaluating maintenance policies by quantitative modeling and analysis.Reliability Abstract: The growing importance of maintenance in the evolving industrial scenario and the technological advancements of the recent years have yielded the development of modern maintenance strategies such as the Condition-Based Maintenance (CBM) and the Predictive Maintenance (PrM). In practice, assessing whether these strategies really improve the maintenance performance becomes a fundamental issue. In the present work, this is addressed with reference to an example concerning the stochastic crack growth of a generic mechanical component subject to fatigue degradation. It is shown that modeling and analysis provide information useful for setting a maintenance policy.

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“…This predicted residual life was used to make maintenance decisions. Similarly, in [64], [65] and [66], the maintenance decisions were based on the predicted residual life distributions (RLD) which can be updated using in situ sensor data. The updated distributions are then used to revise the schedule of maintenance events based on the most recently observed degradation information.…”

Section: Condition Based Maintenancementioning

confidence: 99%

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Celen

Djurdjanović

2012

ASME 2012 International Manufacturing Science and Engineering Conference

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In highly flexible and integrated manufacturing systems such as those in semiconductor manufacturing, there exist strong dynamic interactions between the equipment condition, operations executed on the equipment and the resulting product quality. These interactions necessitate a methodology that integrates the decisions of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper we aim to devise an integrated decision making policy for maintenance scheduling and production sequencing with the objective of maximizing an adaptive profit function, while taking into account operation-dependent degradation models and a production target. In order to obtain the optimal decision policy, a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system is used. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

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Statistically Planned and Individually Improved Predictive Maintenance Management for Continuously Monitored Degrading Systems

Cited by 76 publications

References 21 publications

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

Evaluating maintenance policies by quantitative modeling and analysis

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

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