SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on Principal Component Analysis

Abstract Condition monitoring for wind turbines is essential for the further development of wind farms. Currently, many of the works are focused on the installation of new sensors to predict turbine failures, which raises the cost of wind projects. Wind turbines operate in a wide variety of environmental conditions, such as different temperatures and wind speeds that vary throughout the year season. Typically, most or all of the data available in a turbine is healthy data (operation without failure), so data-driven supervised classification methods have data imbalance problems (more data from one class). Also, when historical pre-failure data do not exist, those methods cannot be used. Taking into account the aforementioned difficulties, the stated strategy in this work is based on a principal component analysis anomaly detector for main bearing failure prognosis and its contributions are: i) this methodology is based only on healthy SCADA data, ii) it works under different seasons of the year providing its usefulness, iii) it is based only on external variables and one temperature related to the element under diagnosis, thus avoiding data containing information from other fault types, iv) it accomplishes the main bearing failure prognosis (several months beforehand), and v) the performance of the proposed strategy is validated on a real in production wind turbine.