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
Original language | English |
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Title of host publication | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
Editors | Michael Beer, Enrico Zio |
Place of Publication | Singapore |
Publisher | Research Publishing |
Pages | 1133-1140 |
Number of pages | 8 |
ISBN (Electronic) | 9789811127243 |
ISBN (Print) | 978-981-11-2724-3 |
DOIs | |
Publication status | Published - 26 Sept 2019 |
Event | 29th European Safety and Reliability Conference - Hannover, Germany Duration: 22 Sept 2019 → 26 Sept 2019 https://esrel2019.org/#/ |
Publication series
Name | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
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Conference
Conference | 29th European Safety and Reliability Conference |
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Abbreviated title | ESREL 2019 |
Country/Territory | Germany |
City | Hannover |
Period | 22/09/19 → 26/09/19 |
Internet address |
Keywords
- Remaining useful life (RUL)
- Prognostics and Health Management (PHM)
- Data-Driven Prognostics
- Statistical Prognostic
- Artificial Neural Network (ANN)