Standardizing research methods for prognostics

Uckun, Serdar; Goebel, Kai; Lucas, Peter

doi:10.1109/phm.2008.4711437

Cited by 94 publications

(67 citation statements)

References 22 publications

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“…A good prognostics system not only provides accurate and precise estimates for the RUL predictions but also specifies the level of confidence associated with such predictions. Without such information any prognostic estimate is of limited use and cannot be incorporated in mission critical applications [5]. Uncertainties arise from various sources in a PHM system [6][7][8].…”

Section: Uncertainties In Prognosticsmentioning

confidence: 99%

Evaluating prognostics performance for algorithms incorporating uncertainty estimates

Saxena

Celaya

Saha

et al. 2010

2010 IEEE Aerospace Conference

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Uncertainty Representation and Management (URM) are an integral part of the prognostic system development. 1 As capabilities of prediction algorithms evolve, research in developing newer and more competent methods for URM is gaining momentum. 2 Beyond initial concepts, more sophisticated prediction distributions are obtained that are not limited to assumptions of Normality and unimodal characteristics. Most prediction algorithms yield non-parametric distributions that are then approximated as known ones for analytical simplicity, especially for performance assessment methods. Although applying the prognostic metrics introduced earlier with their simple definitions has proven useful, a lot of information about the distributions gets thrown away. In this paper, several techniques have been suggested for incorporating information available from Remaining Useful Life (RUL) distributions, while applying the prognostic performance metrics. These approaches offer a convenient and intuitive visualization of algorithm performance with respect to metrics like prediction horizon and α-λ performance, and also quantify the corresponding performance while incorporating the uncertainty information. A variety of options have been shortlisted that could be employed depending on whether the distributions can be approximated to some known form or cannot be parameterized. This paper presents a qualitative analysis on how and when these techniques should be used along with a quantitative comparison on a real application scenario. A particle filter based prognostic framework has been chosen as the candidate algorithm on which to evaluate the performance metrics due to its unique advantages in uncertainty management and flexibility in accommodating non-linear models and non-Gaussian noise. We investigate how performance estimates get affected by choosing different options of integrating the uncertainty estimates. This allows us to identify the advantages and limitations of these 1 978-1-4244-3888-4

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Section: Uncertainties In Prognosticsmentioning

confidence: 99%

Evaluating prognostics performance for algorithms incorporating uncertainty estimates

Saxena

Celaya

Saha

et al. 2010

2010 IEEE Aerospace Conference

Self Cite

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“…The benefit from prognostics can be flourished if its information is used as the main source to system health management. PHM is the emerging engineering discipline that links studies of failure mechanisms to system life cycle management [8].…”

Section: Prognostics and Health Management (Phm)mentioning

confidence: 99%

“…It is also accurate and precise, but accuracy and precision depend on model fidelity [8]. The advantages of this approach are that it is easy to validate, certificate, and verify.…”

Section: Physics-based Prognosticsmentioning

confidence: 99%

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Prognostics: a literature review

Elattar

Elminir

Riad

2016

Complex Intell. Syst.

142

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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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“…For a degradation process that is predicated on system aging and monotonic damage accumulation and manifests itself in the physical composition of the system, it should be possible to correlate sensor behavior with signs of aging to estimate the remaining useful life of systems [12]. However, the multi-dimensional data caused by multiple operational regimes could not provide useful information to measure the monotonic damage accumulation.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

Reducing Dimensionality of Multi-regime Data for Failure Prognostics

Bektaş

Alfudail

Jones

2017

J Fail. Anal. and Preven.

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Over the last decade, the prognostics and health management literature has introduced many conceptual frameworks for remaining useful life predictions. However, estimating the future behavior of critical machinery systems is a challenging task due to the uncertainties and complexity involved in the multi-dimensional condition monitoring data. Even though many studies have reported promising methods in data processing and dimensionality reduction, the prognostics applications require integration of these methods with remaining useful life estimations. This paper describes a multiple linear regression process that reduces the number of data regimes under consideration by obtaining a set of principal degradation variables. The process also extracts health indicators and useful features. Finally, a state-space model based on frequencydomain data is used to estimate remaining useful life. The presented approach is assessed with a case study on turbofan engine degradation simulation dataset, and the prediction performance is validated by error-based prognostic metrics.

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Standardizing research methods for prognostics

Cited by 94 publications

References 22 publications

Evaluating prognostics performance for algorithms incorporating uncertainty estimates

Evaluating prognostics performance for algorithms incorporating uncertainty estimates

Prognostics: a literature review

Reducing Dimensionality of Multi-regime Data for Failure Prognostics

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