Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction

Sankararaman, Shankar

doi:10.1016/j.ymssp.2014.05.029

Cited by 138 publications

(92 citation statements)

References 69 publications

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“…Therefore, uncertainty has an important role when estimating the system RUL. Accordingly, the PDF of the RUL is preferable to the fixed constant RUL estimation because the PDF includes information about the possible variability of the system and the deterministic RUL specifies a single point estimation [28]. Parametrized distributions are necessary to model, integrate, and propagate prognostics results into dynamic dependability models.…”

Section: A Prognostics Techniques and Adaptation Of Prognostics Resultsmentioning

confidence: 99%

Improved Dynamic Dependability Assessment Through Integration With Prognostics

et al. 2017

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This version is available at https://strathprints.strath.ac.uk/60422/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognosticsupdated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

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Section: A Prognostics Techniques and Adaptation Of Prognostics Resultsmentioning

confidence: 99%

Improved Dynamic Dependability Assessment Through Integration With Prognostics

et al. 2017

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“…The modelling of dynamic processes with imperfect or unknown information gives rise to numerous sources of uncertainty and variability that have to be accounted for [13][14][15][16]. These sources contribute to the system's state uncertainty, the modelling uncertainty and -in terms of prognosis -the future uncertainty.…”

Section: Bayesian Approachmentioning

confidence: 99%

A Bayesian Approach to Model Calibration and Parameter Estimation in Potential Drop Measuring

Berg¹,

Ende²,

Lammering³

2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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“…Some newer references about predictive maintenance can be found in Refs. [10][11][12]. No matter what is the goal of predictive maintenance, three key steps must be followed for its implementation: (1) data acquisition, (2) data processing and (3) maintenance decision-making.…”

Section: Predictive Maintenancementioning

confidence: 99%

Predictive Maintenance Based on Control Charts Applied at Thermoelectric Power Plant

Kisić¹,

Đurović²,

Petrović³

2017

Recent Improvements of Power Plants Management and Technology

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In this chapter, innovative predictive maintenance technique is described with the aim of highlighting the benefits of predictive maintenance compared to time-based maintenance. The proposed technique is applied to a specific problem that occurs when time-based maintenance is applied on grinding tables of the coal mill, in coal-grinding subsystem at the thermoelectric power plant 'TEKO', Kostolac, Serbia. Time-based maintenance provides replacement of grinding tables after certain number of working hours, but depending on the quality of the coal and grinding table itself, this replacement sometimes needs to be made before or after planned replacement. The consequences of such maintenance are great material losses incurred because of frequent shutdowns of the entire coal-grinding subsystem, as well as the possibility that the failure occurs before replacement. Innovative predictive maintenance technique described in the chapter is used for solution of this problem.

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Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction

Cited by 138 publications

References 69 publications

Improved Dynamic Dependability Assessment Through Integration With Prognostics

Improved Dynamic Dependability Assessment Through Integration With Prognostics

A Bayesian Approach to Model Calibration and Parameter Estimation in Potential Drop Measuring

Predictive Maintenance Based on Control Charts Applied at Thermoelectric Power Plant

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