Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data

Effi Latiffianti, Shawn Sheng, Yu Ding

Research output: Contribution to journalArticlepeer-review

7 Scopus Citations

Abstract

The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.

Original languageAmerican English
Article number904622
Number of pages11
JournalFrontiers in Energy Research
Volume10
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Latiffianti, Sheng and Ding.

NREL Publication Number

  • NREL/JA-5000-82585

Keywords

  • anomaly detection
  • control chart
  • CUSUM
  • early warning
  • gearbox
  • minimum spanning tree (MST)
  • unsupervised learning

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