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Survival analysis of gas turbine components
Linköping University, Department of Computer and Information Science, Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Survival analysis is applied on mechanical components installed in gas turbines. We use field experience data collected from repair inspection reports. These data are highly censored since the exact time-to-event is unknown. We only know that it lies before or after the repair inspection time. As event we consider irreparability level of the mechanical components. The aim is to estimate survival functions that depend on the different environmental attributes of the sites where the gas turbines operate. Then, the goal is to use this information to obtain optimal time points for preventive maintenance. Optimal times are calculated with respect to the minimization of a cost function which considers expected costs of preventive and corrective maintenance. Another aim is the investigation of the effect of five different failure modes on the component lifetime. The methods used are based on the Weibull distribution, in particular we apply the Bayesian Weibull AFT model and the Bayesian Generalized Weibull model. The latter is preferable for its greater flexibility and better performance. Results reveal that components from gas turbines located in a heavy industrial environment at a higher distance from sea tend to have shorter lifetime. Then, failure mode A seems to be the most harmful for the component lifetime. The model used is capable of predicting customer-specific optimal replacement times based on the effect of environmental attributes. Predictions can be also extended for new components installed at new customer sites.

Place, publisher, year, edition, pages
2016. , 52 p.
Keyword [en]
Bayesian Weibull regression, failure modes, optimal replacement time, reliability, survival analysis
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-129707ISRN: LIU-IDA/STAT-A-16/007-SEOAI: oai:DiVA.org:liu-129707DiVA: diva2:942437
External cooperation
Siemens Industrial Turbomachinery AB
Subject / course
Statistics
Available from: 2016-06-27 Created: 2016-06-23 Last updated: 2016-06-27Bibliographically approved

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2016AlessandroOlivi(1229 kB)284 downloads
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf