The modeling method on failure prognostics uncertainties in maintenance policy decision process

Liu, Rui; Ma, Lin; Kang, Rui; Wang, Naichao

doi:10.1109/icrms.2011.5979378

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The challenge of managing uncertainties associated with prognostics has been recently addressed in [1][2][4][5]. Uncertainty management in prognostics entails to identify, classify and analyze uncertainty sources with the aim of associating to the RUL predictions provided by a prognostic model an estimate of its uncertainty [4][5][6][7], i.e., a measure of the expected degree of mismatch between the real and predicted equipment failure time. This information, provided in the form of a probability distribution of the equipment RUL, can be used by the decision maker to confidently plan maintenance actions, according to the desired risk tolerance [2].…”

Section: Introductionmentioning

confidence: 99%

Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data

Baraldi

Mangili

Zio

2013

Reliability Engineering & System Safety

120

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International audienceWe look at different prognostic approaches and the way of quantifying confidence in equipment Remaining Useful Life (RUL) prediction. More specifically, we consider: (1) a particle filtering scheme, based on a physics-based model of the degradation process; (2) a bootstrapped ensemble of empirical models trained on a set of degradation observations measured on equipments similar to the one of interest; (3) a bootstrapped ensemble of empirical models trained on a sequence of past degradation observations from the equipment of interest only. The ability of these three approaches in providing measures of confidence for the RUL predictions is evaluated in the context of a simulated case study of interest in the nuclear power generation industry and concerning turbine blades affected by developing creeps. The main contribution of the work is the critical investigation of the capabilities of different prognostic approaches to deal with various sources of uncertainty in the RUL prediction

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Section: Introductionmentioning

confidence: 99%

Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data

Baraldi

Mangili

Zio

2013

Reliability Engineering & System Safety

120

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“…The prediction of the Remaining Useful Life (RUL) of a degrading equipment is affected by several sources of uncertainty such as the randomness in the future degradation of the equipment, the inaccuracy of the prognostic model used to perform the prediction and the noise in the sensor data used by the prognostic model to obtain the RUL prediction. Thus, any RUL prediction provided by a prognostic model should be accompanied by an estimate of its uncertainty (Tang et al 2009;Liu et al 2011) in order to confidently plan maintenance actions, taking into account the degree of mismatch between the RUL predicted by the prognostic model and the real RUL of the equipment (Coble 2010;Zio 2012). In this respect, a method able to estimate a probability density function of the degrading equipment RUL is PF, which is a model-based approach successfully used in prognostics applications (e.g., Vachtsevanos et al 2006, Orchard et al 2005, Orchard & Vachtsevanos 2009, Cadini et al 2009.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks

et al. 2012

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Particle Filtering (PF) is a model-driven approach widely used in prognostics, which requires models of both the degradation process and the measurement acquisition system. In many practical cases, analytical models are not available, but a dataset containing a number of pairs component state - corresponding measurement may be available.In this work, a data-driven approach based on a bagged ensemble of Artificial Neural Networks (ANNs) is adopted to build an empirical measurement model of a Particle Filter for the prediction of the Residual Useful Life (RUL) of a structure whose degradation process is described by a stochastic fatigue crack growth model of literature. The work focuses on the investigation of the capability of theproposed approach to cope with the uncertainty affecting the RUL prediction.

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“…In presence of uncertainties, e.g., due to the scatter in the microstructural and manufacturing characteristics, the loading and external conditions variability, etc., the damage state, at any time instant, is better represented by a random variable ) (  rather than by a deterministic quantity [25]. As a consequence, also the equipment RUL at the present time J  should be represented by a random variable ) ( J RUL  [23].…”

Section: B Prognostic Modelmentioning

confidence: 99%

“…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].…”

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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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The modeling method on failure prognostics uncertainties in maintenance policy decision process

Cited by 7 publications

References 8 publications

Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data

Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data

Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks

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

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