Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Abstract

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder

“…For this case, an AE is trained to rebuild the input at the output, using only healthy data. Here is where the normality model [23] works, since by contrast, if a faulty sample is entered, the AE will not rebuild the input well, thus, the residual error will help to detect a fault since it will be used as an indicator.…”

“…Thus, based on these errors a fault detection threshold, t F D , [23] is computed, which defines when the samples are considered as faulty or healthy. To calculate the t F D , the mean and standard deviation are used.…”

Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

“…The aim of the normality model is that it is capable to cope with the various operational and environmental conditions that the WT will face, see [18]. Thus, the train and test datasets include data from all working conditions: different wind speed regions and their associated regions of operation of the WT (start up, maximize power, and limit wind power to avoid exceeding the safe electrical and mechanical loads), different year seasons, curtailment, etc.…”

“…In this work, a normality model at WT level is selected, based in [18], where the target variable is selected to be the closest sensor to the component under study. In particular, as the main bearing is the component to be monitored, the main shaft temperature is used as target variable.…”

Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

“…Wind turbine faults emerge endlessly due to changeful working conditions and exposure to the sun, rain, sandstorms, and other severe weather factors throughout the year [5]. As a result, faults such as turbine gearbox faults [6], main bearing faults [7], and generator faults [8] lead to wind turbine maintenance downtime. Wind turbine maintenance is difficult and expensive due to high-altitude maintenance operations, which results in the need for considerable manpower and material resources, and can incur huge economic losses.…”