Methodologies for uncertainty management in prognostics

Tang, Liang; Kacprzynski, Gregory J.; Goebel, Kai; Vachtsevanos, George

doi:10.1109/aero.2009.4839668

Cited by 55 publications

(35 citation statements)

References 17 publications

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“…Commonly, the uncertainty could be derived from model structure or Monte-Carlo Bootstrap method. The research about the prediction uncertainty have been discussed in some papers [3][4][5]. Fig.…”

Section: Evaluation Methods Based On Predic-tion Accuracymentioning

confidence: 99%

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Review of Common Evaluation Methods for Life Prediction Models

Fangcheng¹

2014

TOCSJ

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As one of the core parts of Prognostics and Health Management (PHM) technologies, residual useful life (RUL) prediction is a very important concept in decision making and contingency mitigation. With life prediction models, researchers could obtain the prediction RUL of different objects. However, since sometimes there will be several available prediction models to be chosen, evaluation methods or selection methods) for life prediction models should be proposed to help choosing models that suit for certain objects. The most important factors that affect the performance of prediction models include prediction accuracy, data fitness, model complexity and parameter sensitivity. This paper presents some common evaluation methods for life prediction models that have already been used in this area.

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Section: Evaluation Methods Based On Predic-tion Accuracymentioning

confidence: 99%

“…Schwarz [10], who gave a Bayesian argument for adopting it. The BIC could be describe as: (3 .3) when is large, the BIC could be approximately equal to:…”

Section: Information Criterionmentioning

confidence: 99%

Review of Common Evaluation Methods for Life Prediction Models

Fangcheng¹

2014

TOCSJ

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“…It may not be possible to identify and accurately characterize all sources of uncertainty and hence use of a sensitivity analysis is recommended to isolate the most important factors. 13,15,20 Through effective uncertainty management practices one can at most strive towards bringing the predicted estimate close to the true spread and not arbitrarily reducing the spread of RUL itself. What can be minimized, is the variability in the estimate of a given parameter of interest, not the variability in the parameter of interest itself.…”

Section: Uncertainty Management In Prognosticsmentioning

confidence: 99%

A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

Celaya

Saxena

Goebel

2012

Infotech@Aerospace 2012

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This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

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“…Uncertainty is caused by model uncertainty (e.g., due to the limited amount of data used to build it), uncertainty on the observations (e.g., due to sensor noise), and process uncertainty (e.g., due to uncertain future loads and operating conditions) [13]. The intrinsic ability of RVM and GPR to fit probability distribution functions (pdfs) to the degradation data is desirable for prognostics where uncertainty management is of paramount importance [23][24]. In practice, the RVM method is actually a special case of a Gaussian Process (GP) [22].…”

Section: Introductionmentioning

confidence: 99%

A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression

Baraldi

Mangili

Zio

2015

Progress in Nuclear Energy

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Abstract-Advanced diagnostics and prognostics tools are expected to play an important role in ensuring safe and long term operation in nuclear power plants. In this context, we use Gaussian Process Regression (GPR) to build a stochastic model of the equipment degradation evolution and apply it for prognostics.GPR is a probabilistic technique for non-linear nonparametric regression that estimates the distribution of the future equipment degradation states by constraining a prior distribution to fit the available training data, based on Bayesian inference. Training data are taken from sequences of degradation measures collected from a set of similar historical equipments which have undergone a similar degradation process. Given new degradation measures from a currently degrading equipment (test trajectory), the distribution of the Remaining Useful Life (RUL) before failure is estimated by comparing with a failure criterion the distribution of the future degradation states predicted by GPR.Applications are shown on simulated data concerning the evolution of creep damage in ferritic steel exposed to high stress and on real data concerning the clogging of sea water filters placed upstream the heat exchangers of a BWR condenser.Index Terms-Remaining Useful Life, Prediction, Prognostics, Bayesian Inference, Gaussian Process Regression, Degradation, Ccreep, Ffilter Cclogging I. INTRODUCTIONUCLEAR INDUSTRY is considering the development and use of advanced diagnostic and prognostic tools to enable longer term safe operation of nuclear structures, systems and components (SSCs) [1][2][3][4][5][6][7]. This interest is mainly originated by the following three reasons:  many nuclear SSCs are becoming old due to the extension of the life of the existing reactors beyond the initial 30 or 40 years. This aging may cause degradation and even failures if SSC maintenance and replacement is not properly planned [8];  many utilities are considering performing power up-rates in their plants. This typically requires increased coolant flow which may cause faster degradation of the SSCs;  there is a strong economic interest towards having longer fuel cycles and decreased outage times. This may pose constraints on the frequency and extent of in-service inspections which may become not able to detect in a timely manner all SSC degradation modes. In this context, in the present paper, we consider the development of prognostic methods for estimating the remaining useful life of nuclear components, structures and systems. Data-driven and model-based methods can be used for predicting the Remaining Useful Life (RUL) of degrading equipment [9-10], i.e., the remaining time during which the equipment can continue performing its function in a safe and efficient way. This allows the implementation of predictive maintenance strategies which have the potential of increasing safety and lowering costs [11]. Model-based methods assume that a mathematical model of the degradation process is available. In practice, the detailed knowledge necessary for b...

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Methodologies for uncertainty management in prognostics

Cited by 55 publications

References 17 publications

Review of Common Evaluation Methods for Life Prediction Models

Review of Common Evaluation Methods for Life Prediction Models

A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression

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