The latent state hazard model, with application to wind turbine reliability

Ramin Moghaddass, Cynthia Rudin

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

We present a new model for reliability analysis that is able to distinguish the latent internal vulnerability state of the equipment from the vulnerability caused by temporary external sources. Consider a wind farm where each turbine is running under the external effects of temperature, wind speed and direction, etc. The turbine might fail because of the external effects of a spike in temperature. If it does not fail during the temperature spike, it could still fail due to internal degradation, and the spike could cause (or be an indication of) this degradation. The ability to identify the underlying latent state can help better understand the effects of external sources and thus lead to more robust decision-making. We present an experimental study using SCADA sensor measurements from wind turbines in Italy.

Original languageEnglish (US)
Pages (from-to)1823-1863
Number of pages41
JournalAnnals of Applied Statistics
Volume9
Issue number4
DOIs
StatePublished - Dec 1 2015
Externally publishedYes

Keywords

  • Big data
  • Decision-making
  • Maintenance
  • Performance monitoring
  • Reliability

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

Fingerprint Dive into the research topics of 'The latent state hazard model, with application to wind turbine reliability'. Together they form a unique fingerprint.

  • Cite this