Standard

Comparison of Data-driven Prognostics Models: A Process Perspective. / Li, Rui; Verhagen, Wim; Curran, Richard.

29th European Safety and Reliability Conference. ed. / Michael Beer; Enrico Zio. Singapore : Research Publishing, 2019. p. 1133-1141 503.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Li, R, Verhagen, W & Curran, R 2019, Comparison of Data-driven Prognostics Models: A Process Perspective. in M Beer & E Zio (eds), 29th European Safety and Reliability Conference., 503, Research Publishing, Singapore, pp. 1133-1141, 29th European Safety and Reliability Conference, Hannover, Germany, 22/09/19. https://doi.org/10.3850/978-981-11-2724-3_0503-cd

APA

Li, R., Verhagen, W., & Curran, R. (2019). Comparison of Data-driven Prognostics Models: A Process Perspective. In M. Beer, & E. Zio (Eds.), 29th European Safety and Reliability Conference (pp. 1133-1141). [503] Research Publishing. https://doi.org/10.3850/978-981-11-2724-3_0503-cd

Vancouver

Li R, Verhagen W, Curran R. Comparison of Data-driven Prognostics Models: A Process Perspective. In Beer M, Zio E, editors, 29th European Safety and Reliability Conference. Singapore: Research Publishing. 2019. p. 1133-1141. 503 https://doi.org/10.3850/978-981-11-2724-3_0503-cd

Author

Li, Rui ; Verhagen, Wim ; Curran, Richard. / Comparison of Data-driven Prognostics Models: A Process Perspective. 29th European Safety and Reliability Conference. editor / Michael Beer ; Enrico Zio. Singapore : Research Publishing, 2019. pp. 1133-1141

BibTeX

@inproceedings{b319c2494a5547758fe3d259a2c94887,
title = "Comparison of Data-driven Prognostics Models: A Process Perspective",
abstract = "Remaining useful life (RUL) prediction is crucial for the implementation of Prognostics and Health Management (PHM) systems, enabling application of predictive maintenance strategies for critical systems (e.g. in aviation, power, railway). Existing literature addresses aspects of data-driven prognostic approaches, with a predominant focus on introducing and testing various novel prediction techniques which are purposed towards improving prediction accuracy performance. However, a relative lack of research can be identified when considering a comparative evaluation of competing for data-driven approaches. In particular, the contributing process elements and characteristics of data-driven prognostics methods are typically not compared in detail. To overcome these drawbacks, this paper aims to evaluate the underlying technical processes for statistical and artificial neural networks (ANN) methods for prognostics. A case study is conducted to implement both approaches on the PHM08 Challenge Data Set for comparison. This research comprehensively compares the statistical and ANN prognostic methods in a systematic manner, covering and comparing their respective technical processes, and evaluates the results with respect to prediction accuracy ",
keywords = "Remaining useful life (RUL), Prognostics and Health Management (PHM), Data-Driven Prognostics, Statistical Prognostic, Artificial Neural Network (ANN)",
author = "Rui Li and Wim Verhagen and Richard Curran",
year = "2019",
month = sep,
day = "26",
doi = "10.3850/978-981-11-2724-3_0503-cd",
language = "English",
isbn = "978-981-11-2724-3",
pages = "1133--1141",
editor = "Michael Beer and Enrico Zio",
booktitle = "29th European Safety and Reliability Conference",
publisher = "Research Publishing",
note = "29th European Safety and Reliability Conference, ESREL 2019 ; Conference date: 22-09-2019 Through 26-09-2019",
url = "https://esrel2019.org/#/",

}

RIS

TY - GEN

T1 - Comparison of Data-driven Prognostics Models: A Process Perspective

AU - Li, Rui

AU - Verhagen, Wim

AU - Curran, Richard

PY - 2019/9/26

Y1 - 2019/9/26

N2 - Remaining useful life (RUL) prediction is crucial for the implementation of Prognostics and Health Management (PHM) systems, enabling application of predictive maintenance strategies for critical systems (e.g. in aviation, power, railway). Existing literature addresses aspects of data-driven prognostic approaches, with a predominant focus on introducing and testing various novel prediction techniques which are purposed towards improving prediction accuracy performance. However, a relative lack of research can be identified when considering a comparative evaluation of competing for data-driven approaches. In particular, the contributing process elements and characteristics of data-driven prognostics methods are typically not compared in detail. To overcome these drawbacks, this paper aims to evaluate the underlying technical processes for statistical and artificial neural networks (ANN) methods for prognostics. A case study is conducted to implement both approaches on the PHM08 Challenge Data Set for comparison. This research comprehensively compares the statistical and ANN prognostic methods in a systematic manner, covering and comparing their respective technical processes, and evaluates the results with respect to prediction accuracy

AB - Remaining useful life (RUL) prediction is crucial for the implementation of Prognostics and Health Management (PHM) systems, enabling application of predictive maintenance strategies for critical systems (e.g. in aviation, power, railway). Existing literature addresses aspects of data-driven prognostic approaches, with a predominant focus on introducing and testing various novel prediction techniques which are purposed towards improving prediction accuracy performance. However, a relative lack of research can be identified when considering a comparative evaluation of competing for data-driven approaches. In particular, the contributing process elements and characteristics of data-driven prognostics methods are typically not compared in detail. To overcome these drawbacks, this paper aims to evaluate the underlying technical processes for statistical and artificial neural networks (ANN) methods for prognostics. A case study is conducted to implement both approaches on the PHM08 Challenge Data Set for comparison. This research comprehensively compares the statistical and ANN prognostic methods in a systematic manner, covering and comparing their respective technical processes, and evaluates the results with respect to prediction accuracy

KW - Remaining useful life (RUL)

KW - Prognostics and Health Management (PHM)

KW - Data-Driven Prognostics

KW - Statistical Prognostic

KW - Artificial Neural Network (ANN)

U2 - 10.3850/978-981-11-2724-3_0503-cd

DO - 10.3850/978-981-11-2724-3_0503-cd

M3 - Conference contribution

SN - 978-981-11-2724-3

SP - 1133

EP - 1141

BT - 29th European Safety and Reliability Conference

A2 - Beer, Michael

A2 - Zio, Enrico

PB - Research Publishing

CY - Singapore

T2 - 29th European Safety and Reliability Conference

Y2 - 22 September 2019 through 26 September 2019

ER -

ID: 61631460