Current Computational Trends in Equipment Prognostics

Hines, J. Wesley; Usynin, A

doi:10.2991/ijcis.2008.1.1.7

Cited by 35 publications

(21 citation statements)

References 16 publications

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“…4). 4 and A x 5 of the patterns of X A a white Gaussian noise with probability 0.5. To this purpose, the intensity of the noise affecting the allocated parameters of the choke valve case study has been roughly guessed by considering the root of the mean square difference (RMSD) between the seven WT mass flow rate measurements and the corresponding SI values.…”

Section: An Artificial Dataset Xmentioning

confidence: 99%

“…Thus, related data Reducing the models can l the model out improve ma Providing a t prognostic da since the so specific app treatment m presented wit erosion of ch Norwegian C the actual val is retained a state and is affecting the valve flow opening, the calculated on drop through oil, gas and w on the meas wells and o temperatures) are actually tests carried the resulting noisy and lac process; the a rates are co inaccuracies actual valve f To verify this (FCM) cluste projections o subspace of th tion he evolution ent planning prognostic m tion directly egradation. 4 In by noise, sen eed to be ver are used for , the necessity arises in e uncertainty o lead to the red tput, i. sing Choke Valve he subspace ter and gas flo to investigate ed by the p thm based on partition of th as possible to ured paramete ameters in the rify the cohe the less relia ed by the two r es of oil, gas a on the relatio an ensembl s here devis gorithm 10,11 ; an avoid the nee ncrease the ro e estimate. 12 The main contributions of this work to the field of prognostic concern the pre-treatment of noisy and unreliable data.…”

Section: Introductmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble of Kernel Regression Models for Assessing the Health State of Choke Valves in Offshore Oil Platforms

Baraldi¹,

Zio²,

Mangili³

et al. 2014

IJCIS

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This paper considers the problem of erosion in choke valves used on offshore oil platforms. A parameter commonly used to assess the valve erosion state is the flow coefficient, which can be analytically calculated as a function of both measured and allocated parameters. Since the allocated parameter estimation is unreliable, the obtained evaluation of the valve erosion level becomes inaccurate and undermines the possibility of achieving good prognostic results. In this work, cluster analysis is used to verify the allocated parameter values and an ensemble of Kernel Regression models is used to correct the valve flow coefficient estimates.

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Section: An Artificial Dataset Xmentioning

confidence: 99%

Section: Introductmentioning

confidence: 99%

Ensemble of Kernel Regression Models for Assessing the Health State of Choke Valves in Offshore Oil Platforms

Baraldi¹,

Zio²,

Mangili³

et al. 2014

IJCIS

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“…This method for track maintenance is the same as the current trends in other equipment surveillance systems. 14 In 2008, Jinan and Kunming railway bureaus signed a R&D project of railway maintenance safety production management information system, in which one of the important research areas is to scientifically evaluate and judge track irregularity based on historical track inspection data, with the authors' laboratory.…”

Section: Introductionmentioning

confidence: 99%

A Novel Description Method for Track Irregularity Evolution

Xu¹,

Liu²,

Wang³

et al. 2011

IJCIS

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Track Irregularity has a significant influence on the safety of train operation. Due to the fact that the extremely large number of factors affect track irregularity， it is challenging to find a concise yet effective mathematical method to describe the evolution of track irregularity. In this paper, inspection data generated by GJ-4 track inspection cars from Jinan Railway Bureau in China were analyzed to identify the characteristics of track irregularity changes common to different mileage points. Based on these characteristics, a multi-stage linear fitting model to describe the pattern of track irregularity evolution over time was developed. The availability of new inspection data will make the model revise itself. In this sense, the model is a machine learning model. Finally, inspection data from the Beijing-Shanghai Railway Line (Jing-Hu Line) were used to verify the model.

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“…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].…”

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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Current Computational Trends in Equipment Prognostics

Cited by 35 publications

References 16 publications

Ensemble of Kernel Regression Models for Assessing the Health State of Choke Valves in Offshore Oil Platforms

Ensemble of Kernel Regression Models for Assessing the Health State of Choke Valves in Offshore Oil Platforms

A Novel Description Method for Track Irregularity Evolution

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

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