Model-based and data-driven prognostics under different available information

Baraldi, Piero; Cadini, Francesco; Mangili, Francesca; Zio, Enrico

doi:10.1016/j.probengmech.2013.01.003

Cited by 93 publications

(49 citation statements)

References 26 publications

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“…First we use Archard's equation (1) and the wear model presented in Section 2 to predict the wheel degradation trend in the future. Next, we combine this prediction with a known failure threshold to calculate the RUL (see [27] and [28]). The RUL predicted at L(i) (i.e.…”

Section: Remaining Useful Life Predictionmentioning

confidence: 99%

Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging

Cremona

Liu

Bruni

et al. 2016

Reliability Engineering & System Safety

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Section: Remaining Useful Life Predictionmentioning

confidence: 99%

Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging

Cremona

Liu

Bruni

et al. 2016

Reliability Engineering & System Safety

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“…We interpreted this as an indication that some sources of uncertainty, e.g., the model uncertainty, have not been correctly accounted for. We suggest that resorting to the ensemble techniques [27,29], has the potential of improving these results.…”

Section: Discussionmentioning

confidence: 96%

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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“…Turbines, e.g. [44,168,[193][194][195] Data such as stress, fatigue strength, pressure, fuel flow rate, temperature, etc.…”

Section: Opportunities and Challengesmentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

321

153

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a b s t r a c tThe field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under nonlinear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under nonstationary and non-linear conditions are also discussed.

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Model-based and data-driven prognostics under different available information

Cited by 93 publications

References 26 publications

Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging

Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging

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

A review on prognostic techniques for non-stationary and non-linear rotating systems

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