Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings under Varying Operating Conditions

Document Type

Article

Publication Date

3-1-2021

Publication Title

IEEE Transactions on Industrial Informatics

Volume

17

Issue

3

First Page

1742

Keywords

Prognostics, remaining useful life (RUL), wind turbine (WT) bearings

Last Page

1752

Abstract

The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

DOI

10.1109/TII.2020.2993074

ISSN

15513203

E-ISSN

19410050

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