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 languageEnglish
Title of host publication29th European Safety and Reliability Conference
EditorsMichael Beer, Enrico Zio
Place of PublicationSingapore
PublisherResearch Publishing
Pages1133-1141
Number of pages8
ISBN (Print)978-981-11-2724-3
DOIs
Publication statusPublished - 26 Sep 2019
Event29th European Safety and Reliability Conference - Hannover, Germany
Duration: 22 Sep 201926 Sep 2019
https://esrel2019.org/#/

Conference

Conference29th European Safety and Reliability Conference
Abbreviated titleESREL 2019
CountryGermany
CityHannover
Period22/09/1926/09/19
Internet address

    Research areas

  • Remaining useful life (RUL), Prognostics and Health Management (PHM), Data-Driven Prognostics, Statistical Prognostic, Artificial Neural Network (ANN)

ID: 61631460