Prognostics and Health Management of Industrial Equipment

Zio, Enrico

doi:10.4018/978-1-4666-2095-7.ch017

Cited by 87 publications

(85 citation statements)

References 64 publications

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“…Since degradation evolution is intrinsically random, the system i RUL is a random variable and, thus, the objective of applying a prognostic method to a system whose current degradation state is i d is to estimate the probability distribution ) | ( Table I summarizes the main sources of information upon which prognostics can be based [4]:…”

Section: Information and Data For Prognosticsmentioning

confidence: 99%

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

Baraldi

Cadini

Mangili

et al. 2013

Probabilistic Engineering Mechanics

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In practical industrial applications, different prognostic approaches can be used depending on the information available for the model development. In this paper, we consider three different cases: 1) a physics-based model of the degradation process is available; 2) a set of degradation observations measured on components similar to the one of interest is available; 3) degradation observations are available only for the component of interest.The objective of the present work is to develop prognostic approaches properly tailored for these three cases and to evaluate them in terms of the assumptions they require, the accuracy of the Remaining Useful Life (RUL) predictions they provide and their ability of providing measures of confidence in the predictions. The first case is effectively handled within a particle filtering (PF) scheme, whereas the second and third cases are addressed by bootstrapped ensembles of empirical models.The main methodological contributions of this work are i) the proposal of a strategy for selecting the prognostic approach which best suits the information setting, even in presence of mixed information sources;ii) the development of a bootstrap method able to assess the confidence in the RUL prediction in the third case characterized by the unavailability of any degradation observations until failure.A case study is analyzed, concerning the prediction of the RUL of turbine blades affected by a developing creep.

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Section: Information and Data For Prognosticsmentioning

confidence: 99%

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

Baraldi

Cadini

Mangili

et al. 2013

Probabilistic Engineering Mechanics

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“…Various data-driven methods have been proposed for predicting the Remaining Useful Life ( ) of degrading equipment (Hines & Usynin, 2008;Vachtsevanos, 2006;Zio, 2012), i.e., the time left before the equipment will stop fulfilling its functions. Data-driven methods are of interest when an explicit model of the degradation process is not known; they are built based on observations of the degradation process of one or more similar equipment, and usually perform the regression of the future equipment degradation path until pre-defined criteria of failure are met (Niu et al, 2010;Baraldi et al, 2012a-b, Baraldi et al, 2013a.…”

Section: Introductionmentioning

confidence: 99%

Prediction of industrial equipment Remaining Useful Life by fuzzy similarity and belief function theory

Baraldi

Maio

Al‐Dahidi

et al. 2017

Expert Systems with Applications

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We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of industrial equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.

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“…As an essential part of prognostic and health management (PHM), lifetime or remaining useful life (RUL) estimation can provide an effective information for maintenance decision to avoid the accident caused by its failure and reduce the safety risk [5][6][7]. So far, the methods of the lifetime estimation have been widely researched and gained momentum [8,9]. Especially, as analyzed by jardine [10], stochastic data-driven method has been widely investigated and applied to many degradation systems since it only relies on the available observed data and statistical models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

Zhang

et al. 2017

Energies

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Abstract:A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the degradation model and estimate the lifetime. Toward this end, we first propose a multi-phase stochastic degradation model with random jumps based on the Wiener process, where the multi-phase model and random jumps at the changing point are used to describe the variation of degradation rate and state caused by regeneration phenomena accordingly. Owing to the complex structure and random variables, the traditional Maximum Likelihood Estimation (MLE) is not suitable for the proposed model. In this case, we treat these random variables as latent parameters, and then develop an approach for model identification based on expectation conditional maximum (ECM) algorithm. Moreover, depending on the proposed model, how to estimate the lifetime with fixed changing point is presented via the time-space transformation technique, and the approximate analytical solution is derived. Finally, a numerical simulation and a practical case are provided for illustration.

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Prognostics and Health Management of Industrial Equipment

Cited by 87 publications

References 64 publications

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

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

Prediction of industrial equipment Remaining Useful Life by fuzzy similarity and belief function theory

A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

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