A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system

Zio, Enrico; Maio, Francesco Di

doi:10.1016/j.ress.2009.08.001

Cited by 280 publications

(177 citation statements)

References 19 publications

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“…The second is propagating uncertainty for RUL estimation uncertainty management. Zio and Di Maio [45] developed a similarity-based algorithms for online identification of failure modes and RUL estimation of nuclear systems. The computational performance analysis of the proposed methodology showed its applicability for online application.…”

Section: Data-driven Prognosticsmentioning

confidence: 99%

Prognostics: a literature review

Elattar

Elminir

Riad

2016

Complex Intell. Syst.

147

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Integrated systems health management (ISHM) is an enabling technology used to preserve safe and reliable operation of complex engineering systems. It also helps in reducing processing and operation time, manpower and cost, and increasing system availability and utility. ISHM includes various technologies ranging from design, analysis, build, and verify to operate and maintain. Prognostics is one of the most challenging and beneficial aspects of ISHM. Knowledge of the remaining useful life using prognostics can make a significant paradigm shift in ISHM. Researchers that have new interest in prognostics need to read hundreds of articles to have a complete picture about prognostics and its relation to other disciplines. Our contribution to solving this problem is by introducing the first comprehensive vision about prognostics as a part of ISHM in a single literature review paper. We focus on prognostics benefits, approaches, applications, and challenges. This paper can be considered as the starting point for studying prognostics and health management.

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Section: Data-driven Prognosticsmentioning

confidence: 99%

Prognostics: a literature review

Elattar

Elminir

Riad

2016

Complex Intell. Syst.

147

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“…Tian, et al [105], proposed a fuzzy-adaptive unscented Kalman filter to improve the prediction accuracy of nonlinear processes. Zio, et al [106], proposed a method based on fuzzy similarity analysis to estimate system RUL. This method is subjective to manually select membership function of fuzzy logic.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…In this work, this is done by using the similarity-based regression model presented in [3]. The idea underpinning this approach is to evaluate the similarity between the test trajectory and R available reference trajectories, and to use the RULs of these latter to estimate the RUL of the former, considering how similar they are [28,45,50].…”

Section: Direct Rul Prediction: Similarity-based Rul Predictionmentioning

confidence: 99%

“…At the present time, . In [50], the following bell-shaped function has turned out to give robust results in similarity-based regression due to its gradual smoothness:…”

Section: Direct Rul Prediction: Similarity-based Rul Predictionmentioning

confidence: 99%

“…With respect to the direct RUL prediction approaches (type (ii)), we have adopted an approach based on the combined use of Similarity-Based Regression (SBR) [50] and Belief Function Theory (BFT) (also called Dempster-Shafer or evidence theory [14,35]). Similarity-based regression methods are able to provide reliable RUL predictions even in a case, such as the one here addressed, in which very few training degradation trajectories are available [3].…”

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confidence: 99%

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A belief function theory based approach to combining different representation of uncertainty in prognostics

Baraldi

Mangili

Zio

2015

Information Sciences

Self Cite

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In this work, we consider two prognostic approaches for the prediction of the remaining useful life (RUL) of degrading equipment. The first approach is based on Gaussian Process Regression (GPR) and provides the probability distribution of the equipment RUL; the second approach adopts a Similarity-Based Regression (SBR) method for the RUL prediction and belief function theory for modeling the uncertainty on the prediction. The performance of the two approaches is comparable and we propose a method for combining their outcomes in an ensemble. The least commitment principle is adopted to transform the RUL probability density function supplied by the GPR method into a belief density function. Then, the Dempster's rule is used to aggregate the belief assignments provided by the GPR and the SBR approaches. The ensemble method is applied to the problem of predicting the RUL of filters used to clean the sea water entering the condenser of the boiling water reactor (BWR) in a Swedish nuclear power plant. The results by the ensemble method are shown to be more satisfactory than that provided by the individual GPR and SBR approaches from the point of view of the representation of the uncertainty in the RUL prediction.

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A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system

Cited by 280 publications

References 19 publications

Prognostics: a literature review

Prognostics: a literature review

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

A belief function theory based approach to combining different representation of uncertainty in prognostics

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