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Dragan Banjevic

Bio: Dragan Banjevic is an academic researcher from University of Toronto. The author has contributed to research in topics: Condition-based maintenance & Condition monitoring. The author has an hindex of 29, co-authored 73 publications receiving 6172 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.

3,848 citations

Journal ArticleDOI
01 Feb 2001-Infor
TL;DR: The analysis of a preventive replacement policy of the control-limit type for a deteriorating system subject to inspections at discrete points of time is presented, using Cox’s PHM with a Weibull baseline hazard function and time dependent stochastic covariates.
Abstract: The focus of the paper is the optimization of condition-based maintenance decisions within the contexts of physical asset management. In particular, the analysis of a preventive replacement policy of the control-limit type for a deteriorating system subject to inspections at discrete points of time is presented. Cox’s PHM with a Weibull baseline hazard function and time dependent stochastic covariates is used to describe the failure rate of the system. The methods of estimating model parameters and the calculation of the optimal policy are given. The structure of the decision-making software EXAKT is presented. Experience with collecting, preprocessing and using real oil and vibration data is reported.

195 citations

Journal ArticleDOI
TL;DR: The Weibull proportional-hazards model is used to determine the optimal replacement policy for a critical item which is subject to vibration monitoring, and the policy is validated using data that arose from subsequent operation of the plant.
Abstract: This paper describes a case study in which the Weibull proportional-hazards model is used to determine the optimal replacement policy for a critical item which is subject to vibration monitoring. Such an approach has been used to date in the context of monitoring through oil debris analysis, and this approach is extended in this paper to the vibration monitoring context. The Weibull proportional-hazards model is reviewed along with the software EXAKT used for optimization. In particular the case considers condition-based maintenance for circulating pumps in a coal wash plant that is part of the SASOL petrochemical company. The condition-based maintenance policy recommended in this study is based on histories collected over a period of 2 years, and is compared with current practice. The policy is validated using data that arose from subsequent operation of the plant.

157 citations

Journal ArticleDOI
TL;DR: In this paper, a model consisting of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density function (PDF) estimator is presented.

149 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.

3,848 citations

Journal ArticleDOI
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.

1,667 citations

Journal ArticleDOI
TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.

1,569 citations